# Identity and Posture Recognition in Smart Beds
with Deep Multitask Learning

Vandad Davoodnia1 and Ali Etemad2 1V. Davoodnia is with the Department of
Electrical and Computer Engineering, Queen’s University, Kingston, ON, Canada
<EMAIL_ADDRESS>Etemad is with Department of Electrical and
Computer Engineering, Queen’s University, Kingston, ON, Canada
<EMAIL_ADDRESS>
###### Abstract

Sleep posture analysis is widely used for clinical patient monitoring and
sleep studies. Earlier research has revealed that sleep posture highly
influences symptoms of diseases such as apnea and pressure ulcers. In this
study, we propose a robust deep learning model capable of accurately detecting
subjects and their sleeping postures using the publicly available data
acquired from a commercial pressure mapping system. A combination of loss
functions is used to discriminate subjects and their sleeping postures
simultaneously. The experimental results show that our proposed method can
identify the patients and their in-bed posture with almost no errors in a
10-fold cross-validation scheme. Furthermore, we show that our network
achieves an average accuracy of up to 99% when faced with new subjects in a
leave-one-subject-out validation procedure on the three most common sleeping
posture categories. We demonstrate the effects of the combined cost function
over its parameter and show that learning both tasks simultaneously improves
performance significantly. Finally, we evaluate our proposed pipeline by
testing it over augmented images of our dataset. The proposed algorithm can
ultimately be used in clinical and smart home environments as a complementary
tool with other available automated patient monitoring systems.

##  I Introduction

Sleep is a natural activity, which is essential for life and physical well-
being. It has been shown that sleeping affects symptoms of many diseases and
plays an important role in systemic physiology, including metabolism and the
functions of the immune, hormonal, and cardiovascular systems [1, 2]. As such,
recent studies have shown in-bed postures have a high influence on occurrence
of sleep diseases such as apnea [3], pressure ulcers [4], and even carpal
tunnel syndrome [5, 6]. For instance, sleep apnea patients are recommended to
only sleep in the side-positions [7]. Accordingly, several studies have
attempted to build in-bed pose estimation systems using data collected from
pressure maps as an indicator of sleep quality [8, 9].

Currently, aside from visual inspections and reports from patients, sleep
studies mainly rely on using either physiological signals and polysomnography
(PSG) [10] or camera-based systems, which are both performed exclusively in
hospital environments where patients are required to stay overnight. To
address such problem, automated in-bed integrated systems are needed for
clinical and smart home settings. Recently, commercial pressure sensing
mattresses have been used to continuously measure pressure distribution of the
body parts while the user lies on the bed [11, 12]. These pressure sensing
arrays can report valuable information whether the subject is lying, sitting,
or standing [13, 14, 15], which can be complementary to conventional systems.
For instance, pressure maps have been used for in-bed body pose estimation
[16] or measurement of physiological signals such as heart rate and
respiration[17]. There has also been increased attention on studies regarding
the effects of sleeping postures on sleep apnea [7, 18] and ulcer prevention
using advanced monitoring technologies [19, 20] for applications in smart
beds.

In addition to classification of postures, smart beds can enable automated
recognition and identification of users, which will in turn allow for security
and authentication applications, as well as personalization of smart-home
experiences. For example, textile-based pressure sensors were used to perform
user classification in [11].

Generally, pose detection techniques are divided into categories based on the
input modality such as video and depth cameras, electrode-based sensors, or
pressure mattresses. Camera-based approaches use a variety of cameras such as
motion capture [21], range [22], or RGB [23]. These methods suffer from
occlusions, lightening conditions, as well as viewpoint angle and calibration
effects. As a result, in-bed camera-based approaches often need to shift their
attention to addressing these problems [16, 24]. Furthermore, violation of
user privacy is another issue that has contributed to the lack of bed-based
image datasets available for public use. Where such datasets have been
available, deep learning methods have been explored for pose estimation. For
instance [22] trained a deep model to infer body pose from RGB-D data given
ground truths from an optical motion capture system.

In contrast to the aforementioned approaches, pressure-based pose detection
systems avoid problems such as occlusion, lightening variations, and user
privacy, making them a good candidate for smart homes and clinical settings.
However, Pressure mapping systems suffer from noisy measurements and the need
for frequent calibration. Reference [11] performed subject recognition and
posture classification in three standard postures, namely, right, supine, and
left. They extracted eighteen statistical features from the pressure
distribution and fed them to a dense network, pre-trained by incorporating a
restricted Boltzmann machine. Similarly, others have taken multimodal
approaches [25] and Bayesian classification based on kurtosis and Skewness
estimation [26] for in-bed subject detection.

In recent years, there has been an increase in the number of studies on in-bed
posture recognition using high density pressure sensors to estimate pose and
limb locations. For instance, [27] used a $48\times 128$ array of sensors for
posture recognition based on weighted limbs, and [28] used $42\times 192$
array of pressure sensors for classifying $4$ major sleeping positions of
Supine, Prone, Left, and Right. Although these two approaches report a very
high performance of over $95\%$ accuracy, they have used a very large number
of sensors for their task. In contrast, [29] used only two piezoelectric and
pressure sensors for data collection. They reported near $90\%$ accuracy on
$120$ hours of data recorded for only one subject. Similarly, [30] reported
$80.7\%$ accuracy by using $48$ conductive sensors. However, unlike the
dataset used in our study [11], the number of posture classes in previous
studies were limited to the few main sleeping positions, i.e. supine, left,
and right side.

Multitask learning has been successfully used as a method of generalizing a
classifier by learning multiple tasks at the same time. It has been used
across many applications of machine learning, such as natural language
processing [31], speech recognition [32] and computer vision [33, 34, 35]. In
these settings, by forcing the model to learn the shared structures of the
appropriate tasks, the classifier benefits from each task and performs better
than learning each task individually. In this paper, we also leverage the
accuracy of our model by utilizing multitask deep learning model to predict
both subject identities and their postures at the same time.

In this paper, we propose an end-to-end framework for in-bed posture and
subject classification. Our method utilizes deep convolutional neural networks
(CNN) to classify subjects and their sleeping posture from a single frame of
data. We show that our method significantly outperforms other works including
the state of the art. In our model, we introduce an additional hyper-parameter
which acts as the coefficient of posture recognition and subject
identification loss. We illustrate that by learning both tasks simultaneously
and fine-tuning this parameter, our model is able to achieve significant
performance gain. We confirm the effectiveness of our approach by
incorporating a leave-one-subject-out (LOSO) evaluation scheme as well as
$k$-fold evaluation, which was presented in previous studies on the same
dataset [11]. To further evaluate our system, we have introduced additional
classification results by utilizing classical algorithms namely $k$-nearest
neighbour ($k$NN) [36], support vector machines (SVM) [37], and bagged trees
[38].

Figure 1: Several examples of pressure maps from the dataset are illustrated.
In (a), the samples are presented in raw format, while (b) illustrates the
samples after pre-processing.

##  II Material and Methods

###  II-A Data and Pre-processing

We used pressure map data provided in a public dataset, PmatData [11, 12], to
train and test our pressure-based posture and user recognition system. The
pressure data is collected using Vista Medical FSA SoftFlex $2048$. Each
mattress contained $2048$, $1$ square inch, sensors placed on a $32\times 64$
grid. The sensors report numbers in the range of $0-10000$. The data was
collected at a sampling rate of $1Hz$ from $13$ participants in $8$ standard
and $9$ additional uncommon postures. The age of participants was $19-34$
years, with a height and weight of $170-186$ $cm$ and $63-100$ $Kg$
respectively. A total of approximately $1800$ samples was recorded from each
subject.

Figure 2: Our proposed framework is presented. A network consisting of four
main blocks is designed to convert the pressure map data manifold into a
feature space, which is then fed to two dense layers leading to two
multinomial logistic regressors for simultaneous recognition of subjects and
their sleeping postures.

In the pre-processing step we attempted to reduce the noise caused by
occasional malfunctioning pressure sensors. Such artifacts often take place
when individual sensors are subjected to large pressure values outside of the
allowed voltage range. To cope with this problem, we applied a spatio-temporal
median filter of size $3\times 3\times 3$ pixels in the image space and
subsequently normalized the measurements. We also removed the first and last
$3$ frames of each sequence, as in some cases they did not contain a clear
image of the subject. Additionally, we removed $14$ data samples that
contained no images. Some of the raw and pre-processed pressure maps are
illustrated in Figure 1.

###  II-B Classification

We used a deep learning approach [39] for subject and posture identification.
As illustrated in Figure 2, The classification is carried out by a feed-
forward network which consists of two blocks of Conv-BatchNorm-MaxPool-
LeakyRelu followed by another $2$ blocks of Conv-BatchNorm-LeakyRelu,
capturing important properties of each single frame. The classification is
then achieved by feeding the outcome to two dense layers followed by two
SoftMax used in parallel to classify subjects and postures separately. For
regularization, dropout is also used in the dense layers during training.

Let $I\in\mathbb{R}^{\textit{W}\times\textit{H}}$ be a dataset consisting of
pressure maps from a set of users $\Gamma_{I}$ in a set of postures
$\Delta_{I}$. The objective is to learn a function
$\Phi(I;\theta)\mathrel{\mathop{\mathchar
58\relax}}\mathbb{R}^{\textit{W}\times\textit{H}\times\mathinner{\\!\left\lvert\theta\right\rvert}}\rightarrow\mathbb{R}^{\textit{2}}$,
where $\theta$ is the set of trainable parameters and $\Phi$ projects pressure
maps $I$ onto a representation space where subjects and their sleeping
postures are better separated. To address this problem, we consider training a
CNN to discriminate between users and postures in $I$.

Formally, we consider a training set composed of tuples $(I,\gamma,\delta)$
where $I$ is the input pressure map and $\gamma$ and $\delta$ are the user and
the corresponding posture respectively, that is, $\delta\in\Delta_{I}$ and
$\gamma\in\Gamma_{I}$. Accordingly, we created a neural network with multiple
layers, with the objective of discrimination between users and their sleeping
postures. The last layer of the neural network consists of two multinomial
logistic regressors, placed in parallel, with $M$ and $N$ units, where $M$ and
$N$ are the number of users and sleeping postures in the dataset with
$M=\mathinner{\\!\left\lvert\Gamma_{I}\right\rvert}$ and
$N=\mathinner{\\!\left\lvert\Delta_{I}\right\rvert}$, and estimates
$P(\gamma|I)$ and $P(\delta|I)$. To learn both functions, we consider using
the multi-class cross-entropy loss of the users and their postures:

$\displaystyle\mathcal{L}_{user}$
$\displaystyle=-\sum_{j=1}^{M}{\gamma_{ij}\log P(\gamma_{j}|I_{i})},$ (1)
$\displaystyle\mathcal{L}_{posture}$
$\displaystyle=-\sum_{j=1}^{N}{\delta_{ij}\log P(\delta_{j}|I_{i})}.$ (2)

For training the model, we consider a loss function that combines the two
losses and minimizes both at the same time. Our approach considers a weighted
sum of both individual losses:

$\displaystyle\mathcal{L}$
$\displaystyle=\lambda\mathcal{L}_{user}+(1-\lambda)\mathcal{L}_{posture},$
(3)

where $\lambda$ is a hyper-parameter that enforces the trade-off between the
two mentioned objectives (separating users in $I$ and detecting postures). For
this study, we trained our model with different values of $\lambda$ ranging
from $0$ to $1$. Finally, for regularization purposes, we added the L2-loss of
our convolution and FC layers’ weights to the final loss.

In order to obtain a baseline for classification accuracy, we also
incorporated several classical classifiers, namely SVM with a quadratic
kernel, $k$NN with $k=10$, and bagged trees. Additionally, a multi-layer
perceptron (MLP) neural network was utilized which consisted of $5$ layers of
fully connected units, with a LeakyRelu activation functions, and
$128,256,256,128,$ and $64$ nodes in each layer. These algorithms are used
frequently in IoT devices and smart home settings [40]. Therefore, this study
provides a good example of how well they perform for practical smart beds. The
parameters of these classifiers, such as $k$ in the $k$NN, the kernel in the
SVM, and the number of hidden layers/nodes in the MLP, were selected and tuned
empirically. The original features proposed for posture classification in [11]
were used in this phase.

##  III Experiments and Results

Our proposed pipeline was implemented using TensorFlow on an NVIDIA Titan Xp
GPU. The convolution kernels were $3\times 3$ with a stride of $1$. Since the
input pressure map dimension was small, bigger kernel sizes were not feasible
with the required depth in the network. Up-sampling and padding did not
contribute to the performance and therefore were not used. The convolutional
layers were followed by MaxPooling with kernel size of $3\times 3$, where
applicable, and then followed by a batch normalization and LeakyRelu with
negative activation coefficient of $0.2$. The pipeline then feeds into two
dense layers with $256$ nodes each, with a dropout rate of $0.5$. Furthermore,
each convolutional block was followed by an increasing dropout rate of $10\%$,
$20\%$, $30\%$, $40\%$. The dropout layer acts as a regularizer to prevent the
network from over-fitting by distributing the essential information between
different nodes. We also employed an L2 regularization loss with a coefficient
of $\sigma=0.002$. Finally, two SoftMax regressors were employed, one for
classifying the subjects, and the other for recognizing their postures. We
used an Adam optimizer at the training stage with a learning rate of $2\times
10^{-5}$, which was decayed with a rate of $0.95$ every $10$ epochs. We
trained the network for a total of $40$ epochs and a batch size of $64$. For
our final model, we chose the best $\lambda$ for each validation scheme
($\lambda=0.5$ for $k$-Fold cross validation and $\lambda=0.2$ for LOSO).

For validating our proposed method, we trained our model based on two
validation schemes, namely, $k$-fold (with $k=10$) and LOSO. In the $k$-fold
cross-validation, we split the data into $10$ subsets, where $10\%$ was used
for testing and $90\%$ for training. The process was repeated $10$ times.
Finally, we averaged all the results, which resulted in a total posture and
subject recognition accuracy of near $100\%$. In the LOSO scheme, one subject
was kept aside at each iteration for testing and the rest of the subjects were
used in training. Consequently, with LOSO, subject classification was not
possible as evaluation needs to be done on the test subject. However, the loss
function provided in Equation 3 was used in both validation schemes
regardless. Figure 3 presents the training and test curves of both validation
schemes for posture recognition and subject identification, as well as
$10$-fold cross-validation for subject identification. It can be seen that our
model quickly learns and converges to a steady state, most likely due to the
low variance (consecutive frames) in the data. Moreover, as expected, the test
curve for the LOSO scheme exhibits higher error, which is due to the fact that
the test frames are from a subject whose data has not been included in the
training - a scenario which is not guaranteed for $10$-fold cross-validation.

Figure 3: Task specific accuracy losses vs. the number of epochs for presented model are illustrated for our experiments. TABLE I: $10$-Fold Subject Identification Accuracy over $3$ Postures (in $\%$). |  Supine |  Right |  Left
---|---|---|---
Ref. [11] |  88.8 |  76.5 |  83.3
Proposed method ($\lambda$=$0.5$) |  99.9 |  100 |  100
|  |  |

We compared the performance of our model for subject identification across the
$3$ general postures (supine, left side, and right side), with $10$-fold
cross-validation, to [11] which also used $k$-fold validation across the same
$3$ main postures. The results are presented in Table I, where our model
significantly outperforms [11]. It is important to note that in [11], a
separate model was trained for each of the three postures, and therefore, in
Table I, we are showing the accuracy averaged over the three classifiers.

Table II illustrates the performance of our model in classification of the $3$
main postures, evaluated using both $10$-fold and LOSO cross-validation. In
order to further evaluate the performance of our model, we implemented $3$
baseline classification methods, namely SVM, $k$NN, and bagged trees. These
classifiers were fine-tuned to provide the highest accuracy. It can be
observed that, similar to our model, the baseline classifiers perform with
high accuracy with $10$-fold validation. However, when faced with samples from
subjects not involved in training (LOSO), the baseline methods experience a
significant drop in accuracy, while our model performs robustly and almost
performs the same as $10$-fold.

As discussed above, our model performs very accurately in classification of
the $3$ main sleeping postures. However, these postures themselves are
composed of a number of sub-postures with very subtle differences. We trained
our model on all these sub-classes (a total of $17$) and evaluated the
performance using LOSO. Figure 4 presents the confusion matrix of this
experiment. The average accuracy of our model for classification of these $17$
classes is $87\%$, which, as expected, is lower than the $3$ main postures.
However, a closer look at the confusion matrix reveals that most of the
misclassified sub-posture fall within the correct main posture (i.e. supine,
left side, and right side). For instance, classes $1$ to $9$ are all minor
variations of the supine posture, where in $7$, $8$, and $9$ only the bed is
inclined by $30^{\circ}$, $45^{\circ}$, and $60^{\circ}$ respectively.

TABLE II: Posture Recognition Precision (in %). Validation Scheme | 10-Fold | LOSO
---|---|---
Posture |  Supine |  Right |  Left |  Supine |  Right |  Left
Quadratic SVM |  99.3 |  98.6 |  99.7 |  81.2 |  64.3 |  64.3
$k$NN ($k=10$) |  99.9 |  99.6 |  99.9 |  75.6 |  46.1 |  54.3
Bagged Trees |  99.8 |  99.9 |  99.9 |  90.6 |  54.0 |  65.0
Proposed method |  100 |  100 |  99.9 |  99.0 |  100 |  99.7
|  |  |  |  |  |
Figure 4: Posture recognition confusion matrix with leave-one-subject-out
validation scheme. The posture categories are represented in 3 regions.

During our experiments, we evaluated our model over different values of
$\lambda$ to study it’s effects on the outcome of our proposed pipeline. As we
had already achieved near $100\%$ performance for both posture and subject
identification in $k$-fold cross-validation, evaluation of $\lambda$ was only
carried out in the LOSO scheme. Even though subject classification was not
possible with the LOSO scheme, our experiments showed that by learning both
posture and identity (only used in training), our model gains a performance
improvement of $3.4\%$. We performed a t-test on accuracy for $\lambda=0$ and
$\lambda\neq 0$, to evaluate the significance of this difference, and achieved
$p=0.01$, indicating significant effect for $\lambda$.

TABLE III: Series of data augmentation steps. Augmentation Probability |  Process
---|---
$50\%$ |  Rotation by $180^{\circ}$
$20\%$ |  Translation by up to $\pm 10\%$ along $x$
$20\%$ |  Translation by up to $\pm 10\%$ along $y$
$20\%$ |  Rotation by up to $\pm 25^{\circ}$
|

Lastly, to further investigate the capacity of our model and its ability to
generalize to more diverse datasets, we tested and trained our solution on
augmented data. Data augmentation was done in a sequential process in such a
way to resemble different body postures in the bed. Each step of data
augmentation was assigned a probability according to the Table III, which
shows the probability of each augmentation being applied. We trained using the
augmented dataset with $\lambda=0.5$ with $50$ number of epochs. Our model
achieved a subject identification accuracy of $89.7\%$ and posture recognition
of $93.2\%$ for $17$ postures and $99.2\%$ for $3$ posture categories with the
$k$-fold cross-validation scheme. Additionally, a posture recognition accuracy
of $75.6\%$ over all $17$ classes and $98.2\%$ over the $3$ general sleeping
pose categories with LOSO validation was achieved. These results show the
robustness of our method even when used with more diverse sleeping postures.

##  IV Conclusions

In this work, a deep end-to-end CNN framework is proposed for ubiquitous
classification of patient postures and identities with in-bed pressure-
sensors. Our work is an efficient and highly accurate model that performs both
tasks simultaneously, where conventional classification algorithms fail to
achieve a good accuracy for unseen data. Our method can provide valuable
information, which can be used in smart home and clinical settings. The
proposed approach can predict subject’s in-bed postures with an average
accuracy of over $99\%$ based on LOSO evaluation scheme. Results from data
augmentation experiments suggest that our method can be generalized for an
accurate estimation of even larger datasets. We also demonstrated the effects
of the combined loss function, and by means of statistical tests, concluded
that learning both tasks simultaneously can help improve the posture
recognition performance. The future work can focus on pressure datasets with
higher temporal resolution in order to measure physiological signals such as
heart-rate and respiration-rate to further study the effects of sleeping
postures on patient’s state of health.

## Acknowledgment

This work was funded by the Natural Sciences and Engineering Research Council
of Canada (NSERC), Discovery Grant RGPIN-2018-04186. The Titan XP GPU used for
this research was donated by the NVIDIA Corporation.

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