#  You can monitor your hydration level using your smartphone camera

Rose Alaslani1, Levina Perzhilla1, Muhammad Mahboob Ur Rahman1, Taous-Meriem
Laleg-Kirati1,2, Tareq Y. Al-Naffouri1
<EMAIL_ADDRESS>1Computer, Electrical and Mathematical Sciences
and Engineering Division (CEMSE),
King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
2The National Institute for Research in Digital Science and Technology, Paris-
Saclay, France
<EMAIL_ADDRESS>
###### Abstract

This work proposes for the first time to utilize the regular smartphone—a
popular assistive gadget—to design a novel, non-invasive method for self-
monitoring of one’s hydration level on a scale of 1 to 4. The proposed method
involves recording a small video of a fingertip using the smartphone camera.
Subsequently, a photoplethysmography signal is extracted from the video data,
capturing the fluctuations in peripheral blood volume as a reflection of a
person’s hydration level changes over time. To train and evaluate the
artificial intelligence models, a custom multi-session labeled dataset was
constructed by collecting video-photoplethysmogram data from 25 fasting
subjects during the month of Ramadan in 2023. With this, we solve two distinct
problems: 1) binary classification (whether a person is hydrated or not), 2)
four-class classification (whether a person is fully hydrated, mildly
dehydrated, moderately dehydrated, or extremely dehydrated). For both
classification problems, we feed the pre-processed and augmented PPG data to a
number of machine learning, deep learning and transformer models which models
provide a very high accuracy, i.e., in the range of 95% to 99%. We also
propose a highly-accurate alternate method where we feed high-dimensional
photoplethysmography time-series data to a DL model for feature extraction,
followed by t-SNE method for feature selection and dimensionality reduction,
followed by a number of ML classifiers that do dehydration level
classification. Finally, we interpret the decisions by the developed deep
learning model under the SHAP-based explainable artificial intelligence
framework. The proposed method allows rapid, do-it-yourself, at-home testing
of one’s hydration level, is cost-effective and thus inline with the
sustainable development goals 3 & 10 of the United Nations, and a step-forward
to patient-centric healthcare systems, smart homes, and smart cities of
future.

###### Index Terms:

Dehydration, non-invasive methods, deep learning, transformers, explainable
AI, smartphone, PPG.

##  I Introduction

Water is a vital component of the human body, making up approximately 60% of
its composition [1]. This significant proportion emphasizes the importance of
water in various physiological functions and in maintaining overall health.
The body’s daily requirement of roughly 1.5 liters of water is critical for
numerous essential roles that water fulfills [2]. Water’s role extends beyond
mere hydration; it forms the foundation of new cell membranes [3], a process
crucial for cell health and regeneration. Additionally, water enhances the
body’s metabolic rate, assisting in weight management [4], and is key in
transporting proteins and carbohydrates from consumed food [5], [6]. It is
also instrumental in the elimination of toxins, playing a major role in
urination and sweating [7]-[8]. Water is also vital for brain function [9],
and for cognitive health.

The significance of water for human body becomes more pronounced when
considering hydration states. A person with a total body water (TBW) content
of approximately 65%-70% is deemed well-hydrated and healthy. Even a minor
reduction in TBW can lead to dehydration, impacting bodily functions [10].
Dehydration is characterized by a negative water balance in the body, where
water intake is insufficient to replace the water lost [11], leading to an
imbalance that can affect many aspects of health and bodily performance
altering physiological parameters, and could result in a myriad of health
risks.

The symptoms of dehydration can range from mild, such as headaches, high heart
rate, and dizziness [12], to severe, including seizures, kidney failure,
myocardial infarction, and potentially fatal outcomes [11]. Notably, even a
minor reduction, as little as 1%-2% in body water, can have significant
effects on both mental and physical performance [13]. This condition is
particularly perilous in the elderly, attributed to their reduced body water
content, diminished sense of thirst, and prevalent kidney issues. Studies have
shown that dehydration significantly escalates the risk of mortality among
hospitalized elderly patients [14]. Furthermore, certain professions, like
soldiers and construction workers who engage in continuous, strenuous labor in
extreme weather conditions, are at an elevated risk for dehydration,
highlighting the need for efficient hydration monitoring. Dehydration’s impact
extends beyond mere physiological distress; it can also contribute to
conditions such as obesity in children, stemming from electrolyte imbalances
[15].

In the field of dehydration assessment, established methods such as body mass
changes and isotope dilution are considered the gold standard. These
techniques, while offering accuracy, require sophisticated and costly
equipment, as well as controlled procedures. For example, body mass changes
require specific conditions to accurately reflect water weight variations
apart from muscle or fat mass changes [16]. Isotope dilution, a method that
involves ingesting a known quantity of an isotope and then measuring its
concentration in bodily fluids, is accurate but impractical for routine use
[17]. Clinicians also commonly use blood and urine analysis for hydration
levels, assessing plasma osmolality or electrolyte balance [18], and examining
urine’s specific gravity or color [19], [20], [21]. Additionally, as saliva is
always available, salivary osmolality has been proven to be a reliable
biomarker for hydration status [21]. Further, microbiology based methods
employ leg skin microbiome data from subjects for precise prediction of skin
hydration levels along with various other significant biological markers [22].
However, these diagnostic methods necessitate advanced and expensive
instrumentation, in-depth laboratory analysis, and specialized medical staff.
Consequently, they are more apt for occasional use rather than frequent
monitoring, often leading to their employment in emergency scenarios, such as
when patients are admitted to hospitals with advanced stages of dehydration.

Keeping in mind the limitations of the traditional methods of dehydration
assessment, there’s a rising demand for solutions that are portable, non-
invasive, and user-friendly. Smartphones, widely available and equipped with
sufficient sensing and computational capabilities, have recently received
attention a potential tool for in-situ medical diagnostics [23]. Smartphones
not only offer quick and accessible diagnostics, bypassing the lengthy waiting
periods associated with hospital-based tests, but also provide a more cost-
effective alternative for regular hydration assessments compared to
traditional laboratory methods and equipment.

###  I-A Contributions

We propose to utilize the camera of a regular smartphone to record a small
video of the fingertip for non-invasive dehydration monitoring.

Rationale: Dehydration could reduce the overall blood volume and increase
blood viscosity, which may in turn increase the heart rate and reduce the
blood pressure. This motivates us to infer dehydration related information by
collecting video data from fingertip of the subject, which contains the
photoplethysmography (PPG) signal that captures the variations in the
peripheral blood volume due to change in hydration level of a person over
time.

The key contributions of this work are as follows:

  * •

We have acquired a novel labeled video-PPG dataset for dehydration assessment,
first of its kind. That is, we have constructed a custom multi-session labeled
dataset by collecting 125 minutes worth of video-PPG data from 25 fasting
subjects during the month of Ramadan in 2023. It is worth mentioning that each
subject fasted for a duration of 13 hours (from 6 am till 7 pm).

  * •

We propose a binary classification framework, i.e., dehydration detection,
using the pre-processed and augmented PPG time-series data. A large number of
our machine learning (ML), deep learning (DL) and transformer models reliably
determine whether a person is hydrated or dehydrated.

  * •

We propose a 4-class classification framework, i.e., our ML, DL and
transformer models reliably rate the dehydration level of a person on a scale
of 1 to 4, again using the pre-processed and augmented PPG time-series data.

  * •

We propose another low-complexity method whereby we feed high-dimensional PPG
time-series data to a DL model for feature extraction, followed by t-SNE
method for feature selection and dimensionality reduction, followed by a
number of ML classifiers that do dehydration level classification in a
reliable manner.

  * •

Finally, we interpret the decisions by our best-performing DL model by
identifying the most relevant features in the PPG time-series data under the
SHAP-based explainable AI framework.

To the best of our knowledge, this is the first work that: 1) utilizes a
smartphone camera to do non-invasive dehydration monitoring, 2) does
dehydration level classification on a scale of 1 to 4. Further, the proposed
method (with a maximum accuracy of 99.65%) outperforms all the previous works
on non-invasive dehydration monitoring in the literature where maximum
reported accuracy is 97.83%.

The proposed method allows rapid, do-it-yourself, at-home testing of one’s
hydration level, is cost-effective and thus inline with the sustainable
development goals 3 & 10 of the United Nations, and a step-forward to patient-
centric healthcare systems, smart homes, and smart cities of the future.

Figure 1: Pictorial overview of our proposed smartphone-based method for non-
invasive dehydration monitoring.

###  I-B Outline

The rest of this paper is organized as follows. Section II discusses the
related work. Section III provides sufficient details about our data
collection process. Section IV summarizes the key data pre-processing steps.
Section V provides essential details about the various ML, DL, and transformer
models we have implemented in Matlab and Python. Section VI provides a
detailed performance analysis of our AI models. Section VII presents data
visualization, an alternate method for dehydration monitoring, and
interpretation of the decisions by our DL model under the SHAP-based
explainable AI framework. Section VIII concludes the paper.

##  II Related Work

Very recently, there has been an increasing interest to design novel non-
invasive methods for dehydration monitoring, in addition to the traditional
biochemistry based methods that rely upon blood, saliva, sweat or urine
samples. Since this work is positioned as a non-invasive method; therefore,
the related work closest to the scope of our paper include the papers whereby
authors employ wearable sensors, e.g., pulse oximeters, smartwatches, smart
wristbands, smartphones, smart tattoos/stickers, for non-invasive dehydration
monitoring. Alternatively, viewing from a different lens of sensing
modalities, the related work attempts to infer dehydration level of a person
using bio-signals, e.g., PPG, electrodermal activity (EDA), bioimpedance
sensors, etc., by passing them through various ML and DL algorithms. Below, we
discuss the related work by categorizing it according to the various sensing
modalities authors have used in their research.

EDA-based methods: In [24], researchers collected EDA data from five subjects
using the BITalino toolkit to assess hydration levels under two scenarios:
hydrated and dehydrated states. The data was further categorized based on
physical postures such as sitting, standing, and lying down. Participants were
classified as dehydrated after fasting for at least ten hours and as hydrated
if they had consumed water regularly. Employing feature extraction techniques
and the Random Forest algorithm, the study achieved an accuracy of 91.3% in
predicting the hydration status. In a similar work [25], the authors collected
data from 16 participants in three hydration states: hydrated, mildly
dehydrated, and extremely dehydrated. The data was gathered in different body
postures using the EDA sensor and BITalino toolkit. They focused on extracting
key statistical features from the data in order to feed it to their hybrid
BiLSTM that ultimately achieved an accuracy of approximately 97%. Further, in
[26], the authors collected EDA data from several subjects in different body
postures, such as sitting and standing, under varying hydration conditions.
The study utilized the BITalino EDA sensor for data collection and implemented
feature extraction, focusing on statistical features such as mean, variance,
and kurtosis. To classify the hydration status, six ML algorithms were
evaluated, with the K-Nearest Neighbour (K-NN) algorithm achieving the highest
accuracy of 87.78%. In [27], researchers developed an Android app titled
monitoring my dehydration which harnessed EDA data collected via a wristband
and fed it a convolutional neural network (CNN) for binary classification of
the hydration level of a person. Their app factored in four physical
activities–walking, sitting, standing, and lying down–along with two hand
gestures. When tested with data from five individuals, their app achieved a
classification accuracy of 84.5%. Finally, [28] proposed a hydration
monitoring method by utilizing EDA signals, which were analyzed using the
dynamic time warping (DTW) algorithm. Their proposed architecture includes the
integration of a microcontroller and a cloud-based database for the storage
and analysis of data. Additionally, they developed a mobile application for
real-time monitoring of hydration status.

PPG-based methods: [29] developed an automated, non-invasive approach to
detect dehydration using PPG signals. The researchers gathered data from key
body locations– the forehead, ear, and finger– of 42 patients suffering from
dehydration. By analyzing specific characteristics of the PPG signals,
including the goodness of fit and changes in amplitude within the heart rate
frequency range, they employed support vector machines (SVM) with a radial
basis function (RBF) kernel for the classification task. Their method
ultimately achieved a rather low classification accuracy of 67.91%.

PPG & EDA-based methods: In [30], researchers conducted a study to detect mild
dehydration by collecting both EDA and PPG data from 17 subjects. EDA was
measured using stainless-steel electrodes on the left hand connected to a
galvanic skin response module, and PPG signals were recorded with a wearable
device on the left wrist. The analysis was performed using various ML
algorithms, focusing on autonomic responses to cognitive stress induced by the
Stroop test. The authors were able to detect mild dehydration with an overall
accuracy of 91.2% using SVM technique. In the research presented in [31], data
including EDA, skin temperature, PPG readings, and body mass index were
gathered from 16 participants during circuit training sessions. This
information was subsequently analyzed using a specially derived empirical
formula to determine the level of fluid loss, a key indicator of dehydration,
that occurred due to the exercise. [32] proposed a regression-based approach
for dehydration monitoring, utilizing data from EDA and PPG sensors. This
method focuses on predicting the last drinking time by analyzing readings from
these sensors. Their approach also integrates additional data from wearable
sensors like accelerometers, magnetometers, gyroscopes, and sensors for
temperature and barometric pressure, to comprehensively assess hydration
status.

ECG-based method: The study in [33] utilized 12 lead-ECG signals from the
Cardiosoft device in a clinical setting to classify three hydration stages
during exercise using the SVM classifier. They took measurements before
exercise, post-exercise, and after hydration. They identified the RR-interval
as a key feature which helped their SVM classifier achieve a classification
accuracy of 60%.

Sweat-based & bioimpedance-based methods: In a series of studies, Ring et al.
have investigated various non-invasive methods to estimate total body water
(TBW) loss, exploring different approaches in each work. In [34], the focus
was on the viability of using sweat electrolyte concentrations, specifically
chloride and osmolality, as estimators for TBW loss. Utilizing sweat
collectors during exercise, the study found significant correlations between
TBW loss and sweat chloride concentration (r = 0.41) and osmolality (r =
0.43). Building on this work, the researchers proposed a method in [35] to
enhance the accuracy of TBW estimation by adjusting bioimpedance measurements
for temperature effects. Here, they employed ML techniques, including linear
and nonlinear support vector regression and Gaussian process regression, to
effectively mitigate the impact of temperature on bioimpedance readings.
Continuing their exploration in [36], the researchers then turned to salivary
markers as a tool for estimating TBW loss. They aimed to utilize various
substances found in saliva, such as electrolytes and hormones, applying ML
algorithms to refine the accuracy of hydration loss measurement in physical
activities. This approach, which utilized techniques like Gaussian process
regression and linear regression with k-exhaustive feature selection, achieved
an estimation error of about 0.34 liters.

Acoustic-based methods: In their study [37], the authors introduced the
AutoHydrate system, which employs acoustic signals captured by a throat
microphone. This system extracts time and frequency domain features, which are
then analyzed using an SVM classifier to determine drinking activities with a
classification accuracy of 91.5%. Additionally, the processed data is
transmitted via Bluetooth to a smartphone application, facilitating the
display of hydration monitoring results.

Smartphone-based methods: In [38], the authors introduce a smartphone-based
tool for non-invasive dehydration detection, utilizing a dataset of 2,340
facial images and a siamese neural network model to distinguish the two
hydration levels, demonstrating an accuracy of 76.1%. Also, in [39]
smartphones were employed to record skin turgor tests, utilizing two
techniques–skin marking and texture analysis. Subsequently, the authors
utilized image processing algorithms to analyze video frames to derive skin
mechanical characteristics and hydration status, simulating the traditional
medical approach of observing skin’s elasticity and recovery during
dehydration assessments.

Non-contact methods: Diverging from non-invasive methods, the authors in [40]
have developed a unique non-contact method for monitoring dehydration through
the use of radio frequency (RF) signals. Utilizing software-defined radios
(SDRs), their system directs RF signals towards the chest or hand of a subject
and then captures the reflected signals to determine hydration status. Their
study involved data collection from five fasting subjects during the month of
Ramadan. With this data, their neural network classifier yielded an accuracy
of 93.8% for their chest-based method and 96.15% for their hand-based method.

##  III Data Acquisition

This section describes the first major contribution of this work, i.e., a
novel labeled video-PPG dataset for dehydration assessment in a non-invasive
manner.

The need for the new dataset: The data acquisition campaign aimed to construct
a custom video-PPG dataset, driven by the absence of publicly available
labeled video-PPG datasets suitable for dehydration assessment (see Table I).
This lack of availability of public datasets on non-invasive dehydration
monitoring is in turn due to the fact that though the acquisition of the raw
video-PPG data is relatively easy and straightforward, yet it is difficult to
label this data except in the clinical setting where a trained medical
professional obtains blood, urine, or saliva samples of the subject and goes
on to do bio-chemistry analysis to obtain the relevant dehydration related
biomarkers, e.g., plasma/urine osmolality, urine specific gravity, etc.

Data labeling strategy: Since we conduct the data collection on the premises
of an academic campus (instead of a hospital), we adopt a rather innovative
approach to label the video-PPG data. Specifically, we chose to collect data
from a control group of fasting subjects during the month of Ramadan in 2023.
In our study, subjects consistently fasted for approximately 13 hours each day
(typically from 6 am to 7 pm), during a season characterized by high
temperatures, indicative of the hot weather conditions prevalent during that
specific time frame. This control group of fasting subjects allowed us to
label the video-PPG data collected from the fasting subjects in a systematic
manner111This research study was approved by the local ethical institutional
review board of King Abdullah University of Science and technology (KAUST).
All subjects provided their written informed consent before the data
collection.. That is, for the binary classification problem of dehydration
detection, the data collected an hour before (after) the sunset/breaking of
fast was labeled as belonging to dehydrated (hydrated) class.

Data labelling strategy for multi-class classification problem: For the multi-
class classification, where the objective is to rate the dehydration level of
a person on a scale of 1 to 4, we capitalize on the fact that the hydration
level of a fasting person monotonically decreases from morning till evening.
This allows us to collect five distinct measurements (at regular intervals)
from each fasting subject between sunrise and sunset. Furthermore, we label
the raw video-PPG data obtained during the five measurements as follows:

  * •

Level 1–Fully Hydrated (FH): The first measurement is taken at the beginning
of the day, when the fasting subjects are maximally hydrated.

  * •

Level 2–Mildly Dehydrated (MD1): The second measurement is taken 2-3 hours
later, at which point the hydration levels of the fasting subjects have
decreased slightly.

  * •

Level 3–Moderately Dehydrated (MD2): The third measurement is taken another
2-3 hours later, as the hydration levels of the subjects continue to decrease.

  * •

Level 4–Extremely Dehydrated (ED): ”The fourth measurement is taken after
another 2-3 hours, essentially an hour before the subjects break their fasts,
at which point the subjects are maximally dehydrated.

  * •

Rehydrated (RH): The fifth measurement is taken one hour after sunset, after
the subjects break their fasts, thus having the opportunity to rehydrate and
return to a fully hydrated state.

Key statistics of the custom dataset: In total, we collect raw video-PPG data
from 25 fasting subjects (16 males, 9 females) with an average age of 29.4
years, an average weight of 63 kg, and an average height of 164.23 cm. We use
an android Vivo phone with a camera frame rate of 30 Hz. During each
measurement session, each subject placed his/her fingertip on the rear camera
of the smartphone in order to let us record the video-PPG data for a duration
of 5 minutes. This implies that we collect $25\times 5=125$ minutes $=7,500$
seconds worth of video PPG data for each data class/session. This in turn
implies that we have $7,500\times 2=15,000$ seconds worth of labeled data for
binary classification, and $7,500\times 5=37,500$ seconds worth of labeled
data for multi-class classification. Last but not the least, we also collect
meta-data from each subject, e.g., age, weight, height, gender, etc.

Table I: A quick summary of publicly available datasets for non-invasive dehydration monitoring Work |  Device |  Modality | Subjects
---|---|---|---
[41] |  Shimmer3 GSR |  accelerometer, gyroscope, temperature, magnetometer, EDA, pressure, PPG | 11
[42] |  Bioimpedance device, laboratory equipment, thermometer, sweat collector |  bioimpedance, temperature, salivary samples, sweat samples | 10
[43] |  Smartwatch (SpectroPhon) |  total salt in sweat, sweat mass | 240
This work |  Smartphone |  video-PPG | 25

##  IV Data Pre-processing

Having acquired the custom dataset, we perform a number of pre-processing
steps in order to condition the raw video-PPG data and in order to prepare it
for the ML, DL and transformer models. Fig. 1 shows a pictorial summary of the
proposed methodology. Below, we describe the key data pre-processing steps,
one by one:

  * •

Extraction of PPG time-series: We begin by doing pixel-averaging for each
frame, for each of the three color channels (i.e., red, green and blue) in
each video-PPG snippet. This approach allows us to obtain three PPG signals at
a sampling rate of 30 samples/sec corresponding to the three color channels,
for each measurement session (data class).

  * •

Removal of artifacts, detrending and denoising: The three PPG signals
(corresponding to three color channels) contained occasional artifacts (due to
motion) and baseline (due to respiration), which were removed using discrete
wavelet transform-based decomposition and reconstruction method [44]. The
detrended PPG signals were then passed through a fourth-order Butterworth low-
pass filter with cut-off frequency 10 Hz, to remove any residual high-
frequency and out-of-band noise.

  * •

Data normalization: The conditioned PPG time-series data was normalized using
z-score normalization method.

  * •

Data segmentation: Subsequently, we segment the conditioned PPG data–with the
aim to increase the number of examples in the dataset. Specifically, we apply
the windowing method (with a rectangular window of size 3 seconds) to
partition each of the conditioned PPG time-series/examples into smaller, non-
overlapping segments/examples of duration 3 seconds each. Then, keeping in
mind that dehydration is a slowly-varying phenomenon which doesn’t change over
a small duration of a few cardiac cycles, we replicate the class label of each
original unsegmented PPG time-series to each of its segments. This way, the
data segmentation step increases the number of examples per class to 2,500,
which later helps the AI models learn better the dehydration-related features
in the dataset.

  * •

Data augmentation: To further enhance the ability of the AI models to
generalize, we apply Gaussian noise-based data augmentation. Specifically, we
utilize zero-mean Gaussian noise with a small standard deviation of 0.01 to
contaminate the dataset. This approach is used to increase the size of the
segmented dataset by a factor of two, three, and four,
respectively.222Additive Gaussian noise-based data augmentation is a well-
known method to introduce realistic perturbations in the data. The added noise
has zero mean so that the augmented data could retain the characteristics of
the original data, while a standard deviation of 0.01 introduces realistic
variability in the augmented data. Thus, data augmentation helps improve the
generalization capabilities of the developed AI models, without distorting the
intrinsic signal patterns.. The data augmentation not only increased the size
of the dataset, but also introduced necessary variability that mimics real-
world PPG signal fluctuations, thereby enhancing the robustness of the AI
models.

##  V Machine Learning, Deep Learning and Transformer Models Implemented

We train and evaluate a wide range of machine learning, deep learning, and
transformer-based methods for dehydration level classification. This includes
1) binary classification (hydrated vs. dehydrated), and 2) 4-class
classification (fully hydrated, mildly dehydrated, moderately dehydrated, and
extremely dehydrated).

###  V-A Machine learning models

We utilize the Matlab’s Classification Learner app to implement a plurality of
ML methods, e.g., K-nearest neighbours (KNN) with different distance metrics,
support vector machines (SVM) with various kernels, ensemble classifiers,
kernel logistic regression (KLR), and neural networks (NN). Each method is
chosen for its ability to capture different kinds of patterns within the data.
More pertinent details about the ML classifiers implemented are as follows:

  * •

KNN classifiers: We utilize the following three different variants of the KNN
classifier: KNN-Euclidean (with $k=1$), KNN-Cubic (with $k=1$), and KNN-Cosine
(with $k=10$). Note that each of these three KNN variants utilizes a different
distance metric. For example, KNN-Cosine classifier considers also the angle
(in addition to the distance) between data points in feature space.

  * •

SVM and KLR classifiers: We train a Kernel-SVM classifier with two different
kernels, with the aim to project the data into a higher-dimensional space for
linear separation. We also utilize the SVM-Fine Gaussian classifier that uses
a fine-tuned Gaussian kernel to capture more subtle variations in the data.
Further, we also implement Kernel logistic regression method that leverages
the power of kernel methods in the probabilistic framework of logistic
regression.

  * •

Ensemble methods: We utilize two ensemble methods: Ensemble-Subspace KNN and
Ensemble-Bagged Trees, to take advantage of the collective decision-making of
multiple models, thereby improving the generalization capabilities of the
developed classification system.

  * •

Shallow neural network: We explore three different variants of a standard
shallow neural network: 1) ’2x10 NN’, consisting of two hidden layers with ten
neurons each, 2) ’1x100 NN’, consisting of a single hidden layer with one
hundred neurons, and 3) ’3x10 NN’, consisting of three hidden layers with ten
neurons each. This approach allows us to evaluate a shallow neural network for
its ability to learn non-linear relationships across different levels of
complexity.

###  V-B Deep learning models

Since a PPG signal represents the temporal dynamics of various physiological
processes of the body, it is natural to think of using recurrent neural
networks (RNNs) which could effectively process such sequential data (by
utilizing their internal states to retain contextual information).

####  V-B1 Long Short-Term Memory-Fully Convolutional Network (LSTM-FCN)

One notably successful variant of RNNs is long short-term memory (LSTM).
Instead of a simple LSTM model, we choose to implement an LSTM-FCN model for
our problem, as it combines the strengths of both LSTM and FCN architectures
for time-series analysis tasks. That is, the LSTM-FCN model is designed to
capture both the temporal dependencies in sequential data through the LSTM
layers and the local patterns through the convolutional layers. Thus, we train
and fine-tune a custom LSTM-FCN model with four convolutional layers and one
LSTM layer, followed by a dropout layer, to perform binary and multi-class
classification for non-invasive dehydration monitoring using PPG time-series
data. Fig. 2 presents the architecture of the purpose-built LSTM-FCN model in
detail.

Figure 2: Architectures of the purpose-built deep learning models (LSTM-FCN
and BiLSTM-FCN) for dehydration level classification.

####  V-B2 Bidirectional Long Short-Term Memory-Fully Convolutional Network
(BiLSTM-FCN)

We also create a variant of the LSTM-FCN model, termed the BiLSTM-FCN model,
by replacing the LSTM layers in the original model with BiLSTM layers. Note
that the BiLSTM model itself is a variant of the LSTM model whereby each
BiLSTM layer consists of two LSTM layers, one that analyzes the input sequence
forward in time and another that analyzes it backward. This dual-pathway
architecture allows BiLSTMs to capture context from both past and future
states at any point in the sequence, which is particularly beneficial in time-
series data such as the PPG data that we consider in this work. Fig. 2
presents the architecture of the purpose-built BiLSTM-FCN model in detail.

###  V-C Transformer models

Transformer is a well-known deep learning model in natural language processing
(NLP) domain that relies on attention mechanism in order to do sequence-to-
sequence conversion, e.g., language translation, text summarization [45]. As a
typical sequence-to-sequence model, transformer is built on an encoder-decoder
architecture. Unlike its predecessors, i.e., RNNs and LSTMs, which do
sequential computation on the data, transformer is uniquely designed for more
effective parallel processing which accelerates the training process of
transformer. More recently, transformer models have successively been used for
analysis of time-series biomedical signals [23, 46, 47, 48, 49]. Motivated by
this, we implement two distinct transformer models in this work for
dehydration level classification using PPG time-series data: an NLP
transformer known as DistilBERT, and a vision transformer (ViT).

####  V-C1 DistilBERT

BERT (Bidirectional Encoder Representations from Transformers) is an advanced
NLP transformer that is pre-trained on a large corpus of text. DistilBERT is a
more compact variant of BERT that offers similar performance as BERT but with
much fewer parameters which makes it computationally light. Fig. 3 illustrates
the architecture of the pre-trained DistilBERT model that we implement and
fine-tuned for the downstream task of dehydration level classification using
PPG time-series data analysis. We begin by reshaping the three-channel PPG
data to a single-channel data (from 100x3 format, to 300x1 format) followed by
conversion of data type from numerical to string format, making it suitable
for DistilBERT that was originally pre-trained on textual data. Further, in
order to do binary and 4-class classification, we utilize only the encoder
component of DistilBERT and appended a softmax classification layer at the
end, thereby tailoring the model for the classification task while getting rid
of the complexity of a decoder. This streamlined approach leverages
DistilBERT’s efficient feature extraction capabilities, minimizes its
computational demands, while making sure that the model determines the
dehydration status from PPG signals with high accuracy.

Figure 3: Architecture of the purpose-built DistilBERT model for the
downstream task of dehydration level classification.

####  V-C2 Vision Transformer (ViT)

The block diagram in Fig. 4 outlines the architecture of a pre-trained 1D
vision transformer (ViT) model that we tailor (by customizing the first few
layers of the ViT) for the downstream task of dehydration level classification
using PPG time-series data analysis. The PPG signal undergoes a linear
projection into flattened patches after initial processing. Positional
embeddings are then added to these patches to maintain the sequential
information, which is pivotal due to the time-series nature of the PPG data.
These embedded patches serve as input to the ViT encoder, which consists of
multiple layers, each featuring a deformable attention module and a multi-
layer perceptron (MLP) block, with normalization layers interspersed between
them. Eventually, the MLP head functions as the classification layer, taking
the encoded representations from the ViT encoder and outputting the
probabilities of the PPG signal corresponding to different states of
dehydration.

Figure 4: Architecture of the purpose-built 1D-ViT model for the downstream
task of dehydration level classification.

##  VI Results

We first list the performance metrics used in this work to evaluate and
explain the results, followed by a brief discussion on the hyper-parameters
for the developed deep learning and transformer models. We then discuss the
performance results of the proposed ML, DL, and transformer models in detail.

###  VI-A Performance metrics

We use three kinds of performance metrics for performance evaluation of the
proposed ML, DL and transformer models:

  * •

We use confusion matrices, true positive rates (TPR), false positive rates
(FPR), true negative rates (TNR), false negative rates (FNR), and accuracy as
performance metrics.

  * •

Additionally, we also discuss the flip side of each model, i.e., number of
trainable parameters, training time (in seconds), memory requirements (in kB),
prediction speed (in observations/sec), number of floating point operations
(FLOPs) needed by each model.

  * •

Finally, we use t-SNE (t-distributed stochastic neighbor embedding) method as
a tool for dimensionality reduction, and for data visualization.

###  VI-B Hyper-parameters of the ML, DL and transformer models

ML models: We use a train-validation-test split of 75-15-10 for training and
performance evaluation of the proposed ML models and shallow neural network,
using Matlab’s classification learner app.

LSTM-FCN and BiLSTM models: We use a train-validation-test split of 75-15-10
for training and performance evaluation of the LSTM-FCN and BiLSTM-FCN models
in Python+Keras+Tensorflow framework.

DistilBERT and ViT models: We use a train-validation-test split of 75-15-10
for training and performance evaluation of the two transformer models in
Python+Keras+Tensorflow framework. An important hyper-parameter of the 1D-ViT
model is the patch size. In our implementation of 1D-ViT, we use a patch size
of 33 samples. This selection of patch size is based on the fact that the
heart rate lies in the range of 60 to 100 beats per minute, and that the PPG
time-series in the dataset is sampled at a sampling rate of 30 Hz. This in
turn implies that a cardiac cycle consists of 18 to 30 samples, in the
dataset. Thus, setting patch size=33 samples ensures each patch covers at
least one full cardiac cycle, which helps us retain the complete morphological
details of the PPG signal, which in turn help for the efficient dehydration
level classification. Thus, in the implementation, we split a PPG segment into
three patches.

Table II summarizes the other important hyper-parameters for the two deep
learning models and the two transformer models, e.g., batch size, loss
function and more.

Table II: Hyper-Parameters for the LSTM-FCN and BiLSTM-FCN.

Hyper-Parameter | LSTM-FCN | BiLSTM-FCN | DistilBERT | ViT
---|---|---|---|---
Epoch | 200 | 250 | 12 | 100
Batch size | 32 | 64 | 32 | 32
Test Ratio | 10$\%$
Validation Ratio | 15$\%$
Optimizer | Adam
Loss Function | Cross-entropy
Learning rate | 1e-3 - 1e-4 | 1e-3 - 1e-4 | 1e-5 | 1e-5

###  VI-C Performance Evaluation of Machine Learning Models

We analyze the performance of the various machine learning classifiers for
hydration level classification using PPG time-series data acquired via a
smartphone camera.

We begin with the results for the binary classification problem of dehydration
detection (i.e., hydrated vs. dehydrated). Fig. 5 (a) provides a comprehensive
accuracy performance comparison of the various ML classifiers that we
implement in this work. We make the following observations from Fig. 5 (a):

  * •

When there is no data augmentation, the accuracy reported by all the
classifiers is still high and remains in a modest range of 84.5% to 93.8%,
with KNN-Euclidean (KNN-Cosine) classifier performing the best (worst). This
points to the pleasant fact that video-PPG (i.e., smartphone)-based
dehydration detection is indeed a feasible problem (and not an ill-posed
problem).

  * •

An increase in the data augmentation factor (from 2 to 4) leads to (mostly) an
increase in the accuracy of all the ML classifiers. However, there is a caveat
which we explain next. Consider KNN-Euclidean, Ensemble-subspace KNN, and
Ensemble bagged tree classifiers which report a maximum accuracy of 100%
(which is probably overfitting due to data memorization), for a data
augmentation factor of 4. Thus, we conclude that doing excessive data
augmentation beyond a certain factor results in overfitting. Furthermore, as
can be seen in Fig. 5 (a), the optimal data augmentation factor is different
for different classifiers. For example, the optimal data augmentation factor
for KNN-Euclidean, Ensemble-subspace KNN, Ensemble bagged tree classifiers is
2, while for some other classifiers, e.g., KNN-Cosine, KNN-Cubic, it is 4. The
takeaway message is that the data augmentation (if not excessive) helps us
design robust ML classifiers that could efficiently differentiate between
meaningful patterns (related to dehydration) and the noise.

Next, Fig. 5 (b) provides a detailed accuracy performance comparison of the
various ML classifiers, for the 4-class classification problem (i.e., fully
hydrated, mildly dehydrated, moderately dehydrated, fully dehydrated). We
learn the following from Fig. 5 (b):

  * •

First of all, without any data augmentation, the accuracy reported by all the
ML classifiers (except the shallow neural network) is modest, and lies in the
range of 72% to 83.7%, with KNN-Euclidean (KNN-Cosine) classifier performing
the best (worst). This again corroborates our hypothesis that video-PPG (i.e.,
smartphone)-based dehydration classification on a scale of 1 to 4 is indeed
feasible (as the four classes are separable, and not entangled).

  * •

We again observe that the optimal data augmentation factor is different for
different classifiers. For example, the KNN-Euclidean, Ensemble-Subspace KNN,
and Ensemble-Bagged Trees models again exhibit potential overfitting as their
accuracy reach 100% for a data augmentation factor of 4. On the contrary, KNN-
Cosine, KNN-Cubic, Kernal-SVM, Kernal-LR, SVM-Fine Gaussian show a monotonic
increase in accuracy with increase in data augmentation factor, and achieve
maximum accuracy when data augmentation factor is 4.

  * •

It’s noteworthy that all three variants of the shallow neural network models
recorded very low accuracies in this task, with the best accuracy being 78.5%
that was achieved by the 1x100 NN model, for a data augmentation factor of 2.
This indicates the potential need for further optimization of neural network
architectures to fully exploit the benefits of data augmentation in complex
classification scenarios.

(a) Accuracy comparison of the various ML classifiers for binary
classification (hydrated vs. dehydrated).

(b) Accuracy comparison of the various ML classifiers for 4-class
classification (fully hydrated, mildly dehydrated, moderately dehydrated,
extremely dehydrated).

Figure 5: Accuracy comparison of the various ML models, i.e., KNN-Euclidean,
KNN-Cosine, KNN-Cubic, Ensemble-Subspace KNN, Ensemble-Bagged Trees, Kernel-
SVM, Kernel-LR, SVM-Fine Gaussian, 2x10 NN, 1x100 NN, and 3x10 NN classifiers
for hydration level classification. Sub-fig. (a) compares the accuracy of the
ML models for binary classification, while sub-fig. (b) compares the accuracy
of the ML models for 4-class classification. For both sub-figures, we also
evaluate the impact of data augmentation (by a factor of 2, 3, and 4) on the
accuracy of the ML models.

Next, Fig. 6 (a) (Fig. 6 (b)) utilizes heatmaps as compact representations of
confusion matrices of various ML models that we have implement to do binary
(4-class) hydration level classification using the PPG time-series data. Note
that this result has been generated using a data augmentation factor of 4.
Fig. 6 is basically an alternate visual illustration of the quantitative
results presented in Fig. 5, thus corroborating what we infer from Fig. 5. For
example, in Fig. 5 (b), the three variants of the shallow neural network are
visibly distinguished by their lighter hues, indicating a higher rate of
misclassification by the shallow neural networks for both binary and 4-class
classification. On the other hand, the darker shades observed for the ML
models such as KNN-Euclidean, Ensemble-Bagged Trees, Ensemble- Subspace KNN
imply a higher level of accuracy achieved by these models.

(a) Binary classification

(b) 4-class classification

Figure 6: Heatmaps (confusion matrices) of various ML Classifiers for
hydration level classification. Sub-fig. (a) shows the confusion matrices of
various ML models for binary classification problem, showing explicitly their
true and false predictions (about dehydration and hydration). Sub-fig. (b)
shows the confusion matrices of all the ML models for the four-class
classification, showing explicitly the four levels of hydration, from fully
hydrated to extremely dehydrated. Each heatmap provides a visual
representation of the true positive rate (TPR) and false positive rate (FPR)
for a given classifier.

Moving forward, Table III provides a performance comparison of the developed
ML models in terms of other alternate performance metrics, e.g., training
time, prediction speed, memory needs, and computational complexity (in big-O
notation). We learn from Table III that KNN-Euclidean, KNN-Cosine, and SVM-
Fine Gaussian models have the smallest training time, while the remaining ML
models and the shallow neural network have relatively large training time.
Also, we note that all the ML models (except the KNN-cubic and Ensemble
subpace-KNN models) are computationally-efficient, as they could make several
thousands of predictions every second. Finally, we note that all the ML models
(except the Ensemble subspace-KNN model) are light-weight as they require a
memory of less than 10 kB. This makes the ML models well-suited for edge
computing, i.e., the proposed ML models could easily be implemented as an app
that runs on android phones and iPhones (or, even on a low-end modules, e.g.,
Raspberry PI, Arduino UNO etc.). Note that we train and evaluat the ML models
using Matlab Online that utilizes the Matlab cloud server. Thus, the machine-
specific parameters such as the training time and prediction speed as listed
in Table III are based on the Matlab cloud server machine. Nevertheless, it is
worth mentioning that since we do performance comparison of the ML models
relative to each other, the conclusions drawn remain invariant even if the ML
models are implemented on another machine with potentially different
specifications. Last but not the least, Table III also summarizes the training
and test complexity of all the ML classifiers in terms of big-O notation,
which is agnostic of the underlying machine executing the ML models.

Table III: Performance benchmarks for ML classifiers with a data augmentation factor of 4, detailing their accuracy, training time, prediction speed, and memory usage. Training and classification complexity of each ML classifier are expressed in Big-O notation, where $n$ is the number of samples, $d$ is the number of features, $t$ is the number of trees in ensemble methods, and $L$ is the number of layers in the shallow neural network. Model | Accuracy | Training Time | Prediction Speed | Memory | Training Complexity | Classification Complexity
---|---|---|---|---|---|---
| (%) | (sec) | (obs/sec) | (MB) |  |
KNN-Euclidean | 99.9 | 48.034 | 347.28 | 75.44 | $O(1)$ | $O(n*d)$
KNN-cosine | 84.9 | 21.259 | 798.25 | 75.44 | $O(1)$ | $O(n*d)$
KNN-Cubic | 89.4 | 484.47 | 32.958 | 75.44 | $O(1)$ | $O(n*d)$
Ensemble-SubspaceKNN | 99.9 | 103.22 | 159.88 | 1089.859 | $O(t*n*\log{n}*d)$ | $O(t*n*log(n)*d)$
Ensemble-BaggedTree | 99.9 | 92.363 | 34960 | 11.467 | $O(t*n*\log{n}*d)$ | $O(t*log(n)*d)$
Kernel-SVM | 90.2 | 89.68 | 1225.4 | 1.119 |  $O(n^{2}*d)$ to $O(n^{3}*d)$ | $O(n*d)$
Kernel-LR | 86.5 | 287.15 | 1273 | 1.119 | $O(n*d)$ | $O(d)$
SVM-Fine Gaussian | 88 | 2.2048 | 3765.6 | 57.29 |  $O(n^{2}*d$ to $O(n^{3}*d)$ | $O(n*d)$
1x100 NN | 77.3 | 280.65 | 55877 | 0.276 | $O(L*n*d^{2})$ | $O(L*d^{2})$
2x10 NN | 60.8 | 37.002 | 15,526 | 0.068 | $O(L*n*d^{2})$ | $O(L*d^{2})$
3x10 NN | 70.4 | 177.99 | 36857 | 0.070 | $O(L*n*d^{2})$ | $O(L*d^{2})$

###  VI-D Performance Evaluation of Deep Learning and Transformer Models

We begin with Fig. 8 that presents a detailed examination of the training
performance of the two DL and two transformer models (for a data augmentation
factor of 4). Specifically, for all the four models, we observe a steady
increase (decrease) in the accuracy (loss) as we increase the number of epochs
in the top (bottom) row of Fig. 8. This is indicative of a robust learning
process without any underfitting or overfitting. Especially, the loss and
accuracy functions for DistilBERT model reach a plateau in about 10 epochs
during the fine-tuning process, which demonstrates the effectiveness of the
pre-training of DistilBERT. Similarly, for LSTM-FCN, BiLSTM-FCN, and 1D-ViT
models, the accuracy and loss functions saturate in about 100 epochs. Fig. 8
also demonstrates the effectiveness of our approach whereby we use a
relatively large learning rate in the beginning with the aim of faster
convergence of the proposed models, and later, decrease the learning rate in
order to make sure that the models reach the plateau in a stable manner
without big fluctuations in accuracy.

Next, Fig. 9 provides a comprehensive accuracy comparison of LSTM-FCN, BiLSTM-
FCN, DistilBERT, and 1D-ViT models for hydration level classification (both
binary and 4-class classification) using PPG time-series data. We make the
following observations:

  * •

Without any data augmentation, the accuracy of the DL and transformer models
is still good as it lies in the range of 89.5% to 95.14% (75.67% to 87.38%)
for binary (4-class) classification. This again points to the feasibility of
the problem at hand, i.e., the smartphone-based video-PPG data is ineed a
legitimate biomarker of dehydration.

  * •

Data augmentation (by factors of 2, 3, 4) leads to a further and monotonic
increase in accuracy for all the four models, for both classification
problems. For example, for a data augmentation factor of 4, the accuracy of
all the four models is very high and lies in a narrow range of 95.25% to
99.65% (93.08% to 99.57%) for binary (4-class) classification problem.

  * •

For both classification tasks, the data augmentation has more impact on the
performance of data-hungry transformer models, compared to DL models. That is,
DistilBERT shows dramatic performance improvements with increase in data
augmentation factor, thereby indicating the potential need for larger datasets
to train more complex models effectively. On the other hand, the LSTM-FCN (and
BiLSTM-FCN) model maintains relatively high accuracy in a narrow range, for
all data augmentation levels. This reflects the fact that the proposed DL
models are well-matched with the PPG time-series data for the given problem of
dehydration level classification.

  * •

The performance offset between the accuracy of the lowest-performing model
(i.e., 1D-ViT) for binary classification and for the 4-class classification is
quite small, i.e., about 2% (for a data augmentation factor of 4). This is
quite impressive keeping in mind that 4-class classification is much more
challenging/complex problem.

Fig. 10 provides the confusion matrices of the DL and transformer models that
we implement to do binary (4-class) hydration level classification using the
PPG time-series data. Fig. 10 is again an alternate visual illustration of the
accuracy results of the DL and transformer models, and thus, strengthens our
findings from Fig. 9. Additionally, Fig. 10 provides more fine-grained details
about the performance of each model in terms of sensitivity and specificity
across classes, TPRs and FPRs. For example, the TPR of LSTM-FCN model is
maximum for the classes 0 and 3, while the TPR of BiLSTM-FCN model is maximum
for the classes 0 and 1. Note that this result has been generated using a data
augmentation factor of 4.

Table IV provides a quantitative performance comparison of the DL and
transformer models in terms of other alternate performance metrics, e.g.,
number of trainable parameters, memory needs, and FLOPs. We observe that the
two DL models (LSTM-FCN and BiLSTM models) consists of a few hundred thousands
of trainable parameters, which is two orders of magnitude less than the number
of trainable parameters of the transformer models (tens of millions of
parameters). This difference then translates to FLOPs as well, whereby the
FLOPs required by the transformer models are two orders of magnitude greater
than the FLOPs required by the DL models. Finally, all the four models require
a modest memory storage of a few MBs. All in all, Table IV demonstrates that
the developed DL models are well-suited for edge computing, i.e., by
implementing them as an app on android phones and iPhones.

In fact, we implement the DL models by means of a custom android app on a
smartphone whereby a user places his/her fingertip on the rear camera of
his/her smartphone and records a small video of duration 10 seconds. The
developed app then pre-processes the raw video data, inputs it to the LSTM-FCN
and BiLSTM-FCN models, and subsequently outputs the dehydration level of a
subject on a scale of 1 to 4, in about 1 minute. The app is tested to run
successfully on an android Vivo phone with the following specs: Vivo model
V2024, android version 10, 2 Giga Hertz Snapdragon 665 octa-core processor, 4
GB RAM, and 128 GB storage. For the sake of illustration, Fig. 7 displays the
results page of the custom android app. It is worth mentioning that we are
using our custom android app to validate and test the DL models on the new,
unseen data nowadays. We also plan to add a feature in the android app whereby
it will raise an alert and provide the phone user with the suggestions for
rehydration if sufficient decrease in hydration level is detected by the app.

Figure 7: Results page of the custom android app that outputs the dehydration level of a subject on a scale of 1 to 4, based on his/her video-PPG data. Table IV: Performance comparison of DL and transformer models: other benchmarks (for a data augmentation factor of 4) Model |  Accuracy (%) |  Number of Parameters |  Memory (MB) |  FLOPs
---|---|---|---|---
Binary classification
LSTM-FCN |  99.65 |  397,154 |  4.650 |  371,817
BiLSTM-FCN |  99.23 |  414,690 |  5.704 |  531,215
1D-ViT |  95.25 |  22,136,580 |  2.895 |  17,899,821
DistilBERT |  99.20 |  66,363,649 |  8.523 |  236,615,078
4-class classification
LSTM-FCN |  99.13 |  397,348 |  4.65 |  475,405
BiLSTM-FCN |  99.57 |  415,012 |  5.705 |  513,530
1D-ViT |  93.08 |  18,004,356 |  2.811 |  22,025,517
DistilBERT |  97.42 |  66,365,956 |  8.466 |  236,515,198

(a) LSTM-FCN Accuracy

(b) BiLSTM-FCN Accuracy

(c) DistilBERT Accuracy

(d) 1D-ViT Accuracy

(e) LSTM-FCN Loss

(f) BiLSTM-FCN Loss

(g) DistilBERT Loss

(h) 1D-ViT Loss

Figure 8: Training performance (i.e., Accuracy/Loss vs. epochs) curves for
LSTM-FCN, BiLSTM-FCN, DistilBERT, and 1D-ViT models. X-axis represents the
number of epochs, while Y-axis represents Accuracy/Loss of a given model. Data
augmentation factor is set to 4.

(a) Accuracy comparison of the deep learning and transformer models for binary
classification (hydrated vs. dehydrated)

(b) Accuracy comparison of the deep learning and transformer models for
4-class classification (fully hydrated, mildly dehydrated, moderately
dehydrated, extremely dehydrated)

Figure 9: Accuracy comparison of LSTM-FCN, BiLSTM-FCN, DistilBERT, and 1D-ViT
models for hydration level classification. Sub-fig. (a) compares the accuracy
of the four models for binary classification, while sub-fig. (b) compares the
accuracy of the four models for 4-class classification. For both sub-figures,
we also evaluate the impact of data augmentation (by a factor of 2, 3, and 4)
on the accuracy of the four models.  Figure 10: Confusion matrices for LSTM-
FCN, BiLSTM-FCN, 1D-ViT, and DistilBERT for dehydration level classification,
i.e., binary classification and 4-class classification.

###  VI-E Performance comparison with related work

Table V provides a compact but comprehensive performance comparison of our
work with the state-of-the-art on non-invasive dehydration monitoring. Our
work stands out in terms of the following:

  * •

The previous works have utilized various sensing modalities and various
sensors, e.g., PPG-sensor-based oximeters, electrodermal activity
(EDA)-sensor-based smart watches, bio-chemical analysis of sweat, computer
vision-based analysis of the RGB camera-based videos of the face, etc., for
non-invasive dehydration monitoring. In contrast, our work utilizes video-PPG
data acquired from a smartphone camera for non-invasive dehydration
monitoring, for the first time.

  * •

All previous works do dehydration monitoring by means of binary or tertiary
(3-class) classification only. In contrast, our work not only does the binary
classification, but it could also efficiently provide the dehydration level of
a person on a scale of 1 to 4.

  * •

The maximum reported accuracy in the literature for binary classification
problem is 97.38% (by Liaqat et al. [25]). On the other hand, a number of the
developed ML, DL and transformer models provide an accuracy of more than 99%.
Thus, our ML, DL and transformer models outperform all the methods previously
reported in the literature. Additionally, the proposed models report a
remarkable maximum accuracy of 99.57% for the 4-class classification problem,
for the first time.

Finally, thanks to the mass proliferation of the smartphones in the society,
the proposed method is anticipated to find its application in many diverse
usecase scenarios, e.g., self-testing by athletes, marathon runners, labor
people working outdoor in the sun, chronic stay-at-home patients, etc.

Table V: Accuracy comparison of the proposed smartphone-based method for non-invasive dehydration monitoring with the state-of-the-art.  Work | Method | Modality | Accuracy
---|---|---|---
Liaqat et al. [25] | Non-invasive | EDA | 97.83$\%$
Kulkarni et al. [27] | Non-invasive | EDA | 75.96$\%$
Liaqat et al. [24] | Non-invasive | EDA | 91.53$\%$
Rizwan et al [26] | Non-invasive | EDA | 87.78
Saha et al. [38] | Non-invasive | Facial images | 76.1$\%$
Mengistu et. al. [37] | Non-invasive | acoustic | 91.5$\%$
Reljin et. al. [29] | Non-invasive | PPG | 67.91$\%$
Hasan et. al. (CBDM) [40] | Non-contact | RF | 93.8$\%$
Hasan et. al. (HBDM) [40] | Non-contact | RF | 96.15$\%$
This work (2 classes) | Non-invasive | video-PPG | 99.65$\%$
This work (4 classes) | Non-invasive | video-PPG | 99.65$\%$

##  VII Data Visualization, An Alternate Low-Complexity Method for Dehydration
Monitoring and Interpretation of Model Predictions

Having presented the results that showcase the very high accuracy obtained by
the proposed ML, DL and transformer models for dehydration level
classification, we now turn our attention to the following:

  * •

Data Visualization: We do PPG data visualization in a lower-dimensional
(2D/3D) feature space by means of a well-known dimensionality reduction method
known as t-distributed stochastic neighbor embedding (t-SNE). We observe that
the raw PPG data when visualized through t-SNE leads to the two or four
classes being entangled. We resolve this issue by passing the PPG data through
an LSTM-FCN model to the t-SNE. This in turn prompts us to design an alternate
low-complexity method for dehydration monitoring as follows.

  * •

An Alternate Low-Complexity Method for Dehydration Monitoring: We first feed
the raw PPG data to an LSTM-FCN model (basically, a variant of the model
described in Fig. 2) that does feature extraction. The extracted features are
then passed through t-SNE algorithm that does dimensionality reduction (or,
feature selection). Finally, the low-dimensional (2D/3D) data is fed to a
number of ML methods which efficiently do dehydration level classification.

  * •

Interpretation of Model Predictions: We provide interpretation of predictions
by our best-performing LSTM-FCN model, under the explainable AI (XAI)
framework. With this, we aim to not only validate the discriminative power of
the features extracted by the developed DL model, but also to enhance the
transparency and trustworthiness of the predictions by the DL model.

###  VII-A PPG Data Visualization by Dimensionality Reduction via t-SNE Method

We utilize the well-known t-SNE algorithm that projects the high-dimensional
PPG data into a low-dimensional (2D/3D) feature space. Specifically, we feed
the raw PPG segments, each spanning 3.3 seconds (or, having 100 samples), to
the t-SNE method in order to construct a visual map of data distribution in
2D/3D space. Fig. 11 (a), (c) show the resulting t-SNE visualizations of the
data for the binary classification problem in 2D, 3D space, respectively.
Similarly, Fig. 11 (e), (g) show the resulting t-SNE visualizations of the
data for the 4-class classification problem in 2D, 3D space, respectively. We
observe from the Fig. 11 (a),(c),(e),(g) that the data points corresponding to
different classes in the dataset are entangled. This points to the inherent
challenge in distinguishing between classes, with data points appearing
intermixed within the feature space, emphasizing the intricate nature of PPG
data. This prompts us to feed the PPG segments through an LSTM-FCN model
(basically, the same model as described in Fig. 2, except the softmax layer),
whose output is then fed to the t-SNE algorithm. Fig. 11 (b), (d) show the
resulting t-SNE visualizations of the feature vector for the binary
classification problem in 2D, 3D space, respectively. Similarly, Fig. 11 (f),
(h) show the resulting t-SNE visualizations of the feature vector for the
4-class classification problem in 2D, 3D space, respectively. We note that the
data points corresponding to different classes are more discernibly separated
now, which points to the enhanced feature extraction capability of the
developed LSTM-FCN model. Thus, LSTM-FCN and t-SNE together facilitate the
distinction between hydrated and dehydrated states, as well as the gradation
of hydration levels on a four-tier scale. Additionally, the t-SNE method
reduces the size of the feature vector from $100\times 1$ to $g\times 1$, with
$g$ being 2 or 3. This in turn motivates us to design an alternate low-
complexity method for dehydration monitoring which we discuss in the next sub-
section.

(a) 2D visualization without using LSTM-FCN (Binary classification)

(b) 2D visualization using LSTM-FCN (Binary classification)

(c) 3D visualization without using LSTM-FCN (Binary classification)

(d) 3D visualization using LSTM-FCN (Binary classification)

(e) 2D visualization without using LSTM-FCN (4-class classification)

(f) 2D visualization using LSTM-FCN (4-class classification)

(g) 3D visualization without using LSTM-FCN (4-class classification)

(h) 3D visualization using LSTM-FCN (4-class classification)

Figure 11: 2D and 3D t-SNE visualization of PPG time-series data, with and
without using LSTM-FCN Features.

###  VII-B A LSTM-FCN-plus-t-SNE based low-complexity method for dehydration
level classification

We now discuss the particulars of an alternate low-complexity method, that
utilizes the LSTM-FCN model for feature extraction from the PPG time-series
data, utilizes the t-SNE method for feature selection, and implements a number
of ML classifiers for dehydration level classification (see Fig. 12). Recall
from Fig. 11 that the visibly segregated clusters formed due to feeding of the
PPG data to LSTM-FCN followed by t-SNE serve as a foundation for the hydration
level classification. Subsequently, our alternate method employs a number of
ML classifiers in order to interpret and differentiate these clusters
corresponding to different classes in order to make predictions about the
hydration status of a person. Note that the performance of the ML classifiers
is greatly dependent on the quality of the feature extraction process (by
LSTM-FCN) as well as the feature selection process (by t-SNE). Our workflow in
Fig. 12, therefore, emphasizes the synergy between feature extraction,
dimensionality reduction, and classification, leading to an effective
methodology for predicting dehydration status from PPG time-series data.

Figure 12:  Block diagram of an alternate low-complexity method, that utilizes
LSTM-FCN model for feature extraction from the PPG time-series data, utilizes
the t-SNE method for feature selection, and implements a number of ML
classifiers for dehydration level classification.

In Table VI, we systematically compile the accuracy results of our alternate
method, which in turn relies upon the proficiency of various ML classifiers in
differentiating between various hydration states. For the binary
classification problem, the accuracy of all the ML models (except Kernal-LR
and Kernal-SVM) is very high and lies in a very narrow range of 89.8% to
96.8%, 94.7% to 98.6% for 2D, 3D case, respectively. Similarly, for the
4-class classification problem, the accuracy of all the ML models (except
Ensemble-subspace KNN) is again very high and lies in a very narrow range of
92.2% to 93.9%, 90.2% to 94.1% for 2D, 3D case, respectively. Essentially,
Table VI reflects the effectiveness of the purpose-built alternate method that
feeds high-dimensional PPG time-series data to LSTM-FCN model for feature
extraction, followed by t-SNE for feature selection and dimensionality
reduction, followed by a number of ML classifiers that utilize a feature
vector of very small size (2 or 3) in order to do dehydration level
classification.

Table VI: Accuracy of the ML Models under the proposed alternate LSTM-FCN-plus-t-SNE based low-complexity method for dehydration level classification. Model | Accuracy (%)
---|---
| Binary | 4-Class
| 2D | 3D | 2D | 3D
KNN-Euclidean | 95.8 | 96.5 | 92.2 | 93.6
KNN-Cosine | 92.1 | 94.9 | 92.6 | 90.2
KNN-Cubic | 96.8 | 98.6 | 93.5 | 93.9
Ensemble - Subspace KNN | 89.8 | 94.7 | 61.7 | 91.1
Ensemble-Bagged Trees | 96.3 | 96.8 | 93.4 | 93.5
Kernal-SVM | 92.1 | 83.8 | 92.6 | 93.6
Kernal-LR | 92.1 | 76.6 | 92.6 | 92.9
SVM-Fine Gaussian | 95.6 | 97.9 | 93.9 | 93.5
2x10 NN | 95.1 | 96.3 | 93.4 | 93.3
1x100 NN | 95.8 | 98.1 | 93.5 | 94.1
3x10 NN | 95.4 | 95.8 | 92.4 | 93.2

###  VII-C XAI: SHAP Analysis of the Designed LSTM-FCN Model for Non-Invasive
Dehydration Monitoring

Recall that we implement more than a dozen AI models for dehydration level
classification, and a majority of the developed models reported a very high
accuracy in a narrow range of 95% to 99% (for both classification problems),
which is quite satisfactory. Nevertheless, it is imperative to look for
insights into the internal decision-making process of these (block-box)
classifiers that do dehydration level classification using PPG time-series
data analysis. Thus, to demystify these models and elucidate the key features
driving their predictions, we employ XAI techniques that aim to shed light on
the opaque nature of decision-making process of the AI models. The XAI
approach not only enhances the transparency of the designed AI models, but
also empowers us with a deeper understanding of the physiological indicators
of the hydration level of a person.

Having said that, we utilize SHAP (SHapley Additive exPlanations) method [50]
with the aim to highlight the most influential features within the PPG
segments that contribute to the decision-making by the proposed AI models.
More precisely, we employ SHAP Gradient Explainer, a feature attribution
technique that is based on integrated gradients method [51] to elucidate the
predictions made by our best-performing LSTM-FCN model using the RGB PPG time-
series data. By leveraging this approach, we compute a SHAP value for every
input feature within the PPG segment across all three color channels. From
this set of SHAP values, we identify and highlight the top 10 SHAP values per
PPG color channel, which represent the most influential features that
contribute to the diagnostic interpretation of a specific PPG segment.

Fig. 13 provides a visual depiction of SHAP-based XAI analysis of the LSTM-FCN
model, for eight representative PPG segments (each consisting of three color
channels). Specifically, SHAP method takes as input an RGB PPG segment of
length 3.3 seconds (or, 100 samples), and eventually highlights the top 10
significant features (i.e., samples) contributing to the LSTM-FCN model’s
decision-making, by marking them with black dots. These features, identified
through SHAP values, help us identify the pivotal regions within the PPG
segment that are critical for classification. We learn from Fig. 13 (a)-(d)
that the most significant features identified by the SHAP method represent
those samples of PPG segments which correspond to: 1) the decay of the PPG
waveform (that may occur during the diastole period), 2) rise in the PPG
waveform (that corresponds to the systole period), and 3) the moments of
noticeable change in the amplitude of the PPG waveform (e.g., systolic peak,
etc.). This implies that the morphology of the PPG waveform (i.e., the rate of
change in amplitude and more), and thus, the underlying physiology is captured
by the LSTM-FCN and SHAP models, which then plays a key role in informing the
model’s decision-making process. Further, Fig. 13 (e) captures a rather
different situation where the significant SHAP features for the three PPG
color channels span across multiple cardiac cycles, highlighting the
complexity of the problem, and providing guidance for PPG segment size
selection (i.e., a PPG segment should consist of at least a couple of cardiac
cycles). Finally, Fig. 13 (f)-(h) depict some low-quality PPG segments, that
contain some irregularities, e.g., motion artifacts, noise etc. Nevertheless,
the SHAP method still identifies crucial features in these abnormal-looking
PPG segments successfully. This illustrates the robustness of the LSTM-FCN
model, and the efficacy of the SHAP method in identifying the right set of
features that influence the model’s decisions.

Figure 13: SHAP method-based interpretation of the decision-making by LSTM-FCN
model by identification of 10 most significant SHAP features (see black dots)
in RGB PPG segments of length 100 samples each. Sub-fig. (a)-(d) highlight
SHAP-determined features pinpointing critical areas influencing model
predictions, for clean PPG segments. Sub-fig. (e) shows a situation where
significant SHAP features for the three PPG color channels span across
multiple cardiac cycles. Sub-fig. (f)-(h) do SHAP-analysis for low-quality PPG
segments that contain distortions or artifacts.

##  VIII Conclusion

We utilized the camera of a regular smartphone to record a small video of the
fingertip for non-invasive dehydration monitoring, exploiting the fact that
the PPG signal extracted from the video data contains the variations in the
peripheral blood volume due to change in hydration level of a person over
time. To train and test our AI models, we constructed a custom multi-session
labeled dataset by collecting video-PPG data from 25 subjects who were fasting
during Ramadan in 2023. With this, we solved two distinct problems: 1)
dehydration detection, 2) dehydration level classification on a scale of 1 to
4. For both classification problems, our ML, DL and transformer models
provided a very high accuracy, i.e., mostly in the range of 95% to 99%. We
also proposed a highly-accurate alternate method where we fed the high-
dimensional PPG time-series data to a DL model for feature extraction,
followed by t-SNE method for feature selection and dimensionality reduction,
followed by a number of ML classifiers that did dehydration level
classification. Finally, we interpreted the decisions by our DL model under
the SHAP-based explainable AI framework.

The proposed smartphone-based non-invasive method for dehydration monitoring
is highly accurate, allows frequent do-it-yourself monitoring, is easy to use,
and provides rapid results. Further, the proposed method is cost-effective and
thus inline with the sustainable development goals 3 & 10 of the United
Nations, and a step-forward to patient-centric healthcare systems, smart
homes, and smart cities of future. The anticipated beneficiaries of the
proposed method include: sportsmen, athletes, elderly, diabetic and diarrhea
patients, and labor people working outdoors.

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