11institutetext: Embedded Vision Systems Group, Computer Vision Laboratory,
Department of Automatic Control and Robotics,
AGH University of Science and Technology, Krakow, Poland 11email:
<EMAIL_ADDRESS><EMAIL_ADDRESS>
# Real-time FPGA implementation of the Semi-Global Matching stereo vision
algorithm for a 4K/UHD video stream

Mariusz Grabowski  Tomasz Kryjak

###### Abstract

In this paper, we propose a real-time FPGA implementation of the Semi-Global
Matching (SGM) stereo vision algorithm. The designed module supports a
4K/Ultra HD ($3840\times 2160$ pixels @ 30 frames per second) video stream in
a 4 pixel per clock (ppc) format and a 64-pixel disparity range. The baseline
SGM implementation had to be modified to process pixels in the 4ppc format and
meet the timing constrains, however, our version provides results comparable
to the original design. The solution has been positively evaluated on the
Xilinx VC707 development board with a Virtex-7 FPGA device.

###### Keywords:

SGM FPGA 4K Ultra HD real-time processing stereo vision.

##  1 Introduction

Information on the 3D structure (depth) of a scene is very important in many
robotic systems, including self-driving cars and unmanned aerial vehicles
(UAVs), as it is used in object detection and navigation modules. The depth
map can be estimated using several different approaches, active: LiDAR (Light
Detection and Ranging), Time of Flight (ToF) cameras, stereo vision with
structured lighting; and passive: stereo vision. Stereo vision uses two or
more cameras that acquire the same scene, but from slightly different points
in space. A detailed discussion of the advantages and disadvantages of
different sensors can be found in the work of Jamwal, Jindal, and Singh [1].

Stereo vision, in its passive variant, is an often used solution in embedded
systems due to the low price of the equipment, its small size and weight (no
need for a laser light source, rotating elements or projectors). The accuracy
of the results obtained with this technology strictly depends on the algorithm
used to process the acquired images. The methods used can be divided into two
groups: local and global [2]. In both cases, the key element is to find the
same pixels in the image captured by the left (usually considered as the base)
and right camera (reference). Their offset expressed in pixels is referred to
as the disparity. This value can be easily converted to the distance from the
sensors using the vision system parameters.

Global methods introduce appropriate discontinuity penalties in order to
smooth the disparity map. Their aim is to optimise the energy function defined
over the whole image. By means of global algorithms, much more reliable and
accurate disparity maps are determined, but the smoothing task is NP-hard and
algorithms are very computationally demanding, and for this reason they are
not suitable for implementation in real-time systems.

It should be also noted that the current dominant trend is depth estimation
using deep neural networks [3]. However, due to the high computational
complexity, especially for high-resolution video streams, this topic remains
outside the focus of our present work.

The SGM (Semi-Global Matching) algorithm was introduced by Hirshmüller in 2005
[4] and 2008 [5]. It is based on two components: (1) matching at a single
pixel level with the use of mutual information and (2) approximation of a
global, two-dimensional smoothness constraint (obtained by combining multiple
1D constraints). The SGM algorithm is an example of an intermediate method
between local and global approaches for determining disparity maps and is a
compromise between accuracy and computational complexity. However, using SGM
for high-resolution images is still challenging. For example, for a resolution
of $1920\times 1080$ pixels at 30 frames per second, an execution of about 2
TOPS (Tera Operations Per Second) with memory bandwidth of 39 Tb/s is required
to process all pixels (2 million) [6].

In this paper we present an architecture of a stereo vision system with a
modified SGM algorithm to process a 4K/Ultra HD ($3840\times 2160$ pixels @ 30
frames per second) video stream in 4ppc (pixel per clock) format and its
implementation in an FPGA (Field Programmable Gate Array) device. The proposed
modification solves the data dependency problem while not affecting the
algorithm’s accuracy. To the authors’ knowledge, this is the only verified
hardware implementation of the SGM method for 4K/Ultra HD resolution.

The reminder of this paper is organised as follows. In Section 2 we present
basic information about the SGM algorithm. In Section 3 we review the previous
work on SGM implementation on FPGAs. We describe the proposed method and
architecture, as well as the evaluation of the algorithm and the hardware
implementation in Section 4. The paper ends with conclusions and future
research directions.

##  2 The SGM algorithm

As mentioned in the introduction, the SGM algorithm is an intermediate
approach between local and global methods for determining disparity maps.
Furthermore, it is possible to implement it in an FPGA, in a pipelined vision
system.

The input to the algorithm is a pair of rectified images. It consists of the
following steps: calculation of the matching cost, aggregation of the cost
(calculation of the smoothness constraint) and determination of the final
disparity map.

In this work, the cost of matching $C(p,d)$ between a pixel
$\mathbf{p}=[p_{x},p_{y}]^{T}$ from the base image $I_{b}$, and the
potentially corresponding pixel (shifted by the disparity $d$ in a horizontal
line) in the reference image $I_{m}$, is calculated using the Census transform
and the Hamming distance measure, as shown in Figure 1.

Figure 1:  Matching cost calculation with the Census transform and the Hamming
distance metric, with example values.

Determining the correspondence between pixels using only the matching cost
alone can lead to ambiguous and incorrect results. Therefore, an additional
global condition is proposed in the SGM algorithm, which adds a “penalty” for
changing the disparity value (i.e, supports the smoothness of the image), by
aggregating the costs along independent paths.

Let $L_{\mathbf{r}}$ denote the path in the direction $\mathbf{r}$. The path
cost $L_{\mathbf{r}}(\mathbf{p},d)$ is defined recursively as:

$\displaystyle
L_{r}(\textbf{p},d)=C(\textbf{p},d)+min[L_{\textbf{r}}(\textbf{p}-\textbf{r},d),$
(1) $\displaystyle L_{\textbf{r}}(\textbf{p}-\textbf{r},d-1)+P_{1},$
$\displaystyle L_{\textbf{r}}(\textbf{p}-\textbf{r},d+1)+P_{1},$
$\displaystyle\min\limits_{i}L_{\textbf{r}}(\textbf{p}-\textbf{r},i)+P_{2}]$
$\displaystyle-\min\limits_{k}L_{\textbf{r}}(\textbf{p}-\textbf{r},k)$

where: $C(p,d)$ is the matching cost, and the second part of the equation is
the minimum path cost for the previous pixel $\mathbf{p}-\mathbf{r}$ on the
path, taking into account the corresponding discontinuity penalty. Two
penalties were applied in the algorithm, $P1$ for a 1-level change in
disparity and $P2$ for a larger change.

Finally, the matching cost is given as:

$S(\textbf{p},d)=\displaystyle\sum_{\textbf{r}}L_{\textbf{r}}(\textbf{p},d)$
(2)

The author of SGM recommend aggregation along at least 8 paths, i.e,
vertically, horizontally and diagonally in both directions (cf. Figure 3),
although he suggests that good results are achieved for the number 16. The
disparity map $D_{b}$ corresponding to the base image $I_{b}$ is obtained by
selecting for each value $\mathbf{p}$ the disparity $d$ that corresponds to
the minimum cost i.e, $min_{d}S(\mathbf{p},d)$. Optional element of the
algorithm is the final post-processing: median filtering and map consistency
check (so called left-right consistency check).

Due to the reasonable trade-off between computational complexity and the
quality of the resulting disparity map, the SGM algorithm has become very
popular. It is a basic method in the popular OpenCV library and the Computer
Vision Toolbox of the Matlab software. It also provides an attractive solution
for hardware implementations in FPGAs.

##  3 Previous work

The topic of implementing stereo correspondence using FPGAs is very extensive,
and hence this review is narrowed only to selected articles describing the SGM
algorithm. Interested readers are referred to the review [7].

The paper written by Gehrig, Eberli, and Meye in 2009 [8] described an SGM
architecture for processing images with a resolution of $750\times 480$ pixels
(effectively $340\times 200$) @ 27 fps at 64 levels of disparity. It is worth
noting that this was the first implementation of the SGM method in an FPGA.

The paper of Hofmann, Korinth, and Koch from 2016 [9] also proposes a hardware
implementation of the SGM algorithm. The architecture features scalability and
combines coarse-grain and fine-grain parallelisation capabilities. The authors
performed tests for different configurations and resolutions. For $1920\times
1080$ pixels @ 30 fps and 128 disparity levels, real-time processing was
achieved at a clock of 130 MHz (VC709 board with Virtex-7 FPGA device).

In the paper of Zhao et al. from 2020 [10], the authors presented the FP-
Stereo library, which uses the HLS language and allows the creation of SGM
disparity calculation modules. The module has been designed in the form of an
accelerator interfacing with a DMA controller, rather than directly with the
video stream. For a 300 MHz clock, a resolution of $1242\times 374$ pixels and
128 disparity range, 161 fps were achieved on the ZCU 102 board with the
Xilinx Zynq UltraScale+ MPSoC device.

In the latest publications by Shrivastava et al. in 2020 [11] and Lee with Kim
in 2021 [6], the support for parallel pixel processing has been added to
increase throughput. In this approach, the main challenge is the presence of
an inherent data dependency. In the paper from 2020 [11], it is addressed by
dependency relaxation, i.e, the aggregation is performed on the basis of the
pixel $k$ earlier, where $k$ is the number of pixels processed simultaneously.
The author points out that such a solution represents a trade-off between
accuracy and throughput.

In the work from 2021 [6], on the other hand, a different approach is
presented, in which operations involving the inherent data dependency are
performed not on a single pixel, but on a vector of pixels. This allows the
generation of disparity maps with very close accuracy to the original SGM
algorithm. In both solutions, the matching costs are determined based on the
Census transform. In the first publication [11], for images at a resolution of
$1280\times 960$ pixels and disparity range of 64, 322 fps, and in the second
[6] for a resolution of $1920\times 1080$ pixels and disparity range of 128,
103 fps were obtained.

We also propose a solution to the inherent data dependency problem. Our
architecture is based on estimating the previous pixel aggregation cost on a
path with minimal additional logic needed. That allows us to process images
with a 4K resolution and also to obtain comparable results to the original SGM
algorithm without parallelism.

##  4 The proposed hardware implementation

Figure 2:  A general scheme of the proposed SGM disparity estimation system.

The aim of our work was to implement a hardware architecture capable of
processing a video stream with a resolution of $3840\times 2160$ pixels in
real-time (i.e processing 30 frames per second with no pixel dropping). That
stream transmitted in a 1 pixel per clock format requires a pixel clock
frequency of approximately 250 MHz. Adding to this value i.e, the vertical and
horizontal blanking fields, the required clock equals about 300 MHz, which is
too high for the rather complicated SGM algorithm. At the bottleneck, cost
aggregation calculations take more than 10 ns on our platform. So, in order to
process the data in the desired resolution, it is necessary to introduce
parallelisation. In this work, a 4ppc (pixel per clock) format is used, in
which 4 pixels are processed in parallel. This allows the pixel clock to be
lowered to approximately 75 MHz. However, the use of such format has
significant implications on the implementation of the SGM algorithm, due to
the inherent data dependency.

A general scheme for the proposed vision system is shown in Figure 2. The
module accepts a synchronised video stream of rectified images, the base
$I_{B}(p)$ and the reference $I_{M}(p)$ one. Further processing consists of
several steps: determination of the matching cost $C(p,d)$ using the Census
transform based matching method, calculation of the cost aggregation
$L_{r}(p,d)$, summation of the aggregation costs from all directions $S(p,d)$
and disparity determination $D(p)$.

###  4.1 Determination of the matching cost

The 4ppc format does not introduce major complications into the hardware
architecture of the matching cost determination module, but only increases the
hardware resource requirements. First, $5\times 5$ contexts are created for
both images. For the base image, in a given cycle, 4 contexts are created (as
implied by the 4ppc format [12]), and for the reference image this number is
increased by the disparity range ($4+disp\\_range-1$), so that it is possible
to simultaneously compare each of the 4 contexts of the base image with all
the contexts in the disparity range of the reference image. A Census transform
is performed on the generated contexts, and the contexts are then compared
accordingly using the Hamming distance metric. The output consists of matching
cost vectors.

###  4.2 Cost aggregation

In the next step, a quasi-global optimisation is performed by aggregating the
costs for the whole image according to the SGM algorithm. In the current
version of the module, this is implemented on four paths in the directions 0°,
45°, 90°, 135°, as shown in Figure 3, which can be processed directly (without
additional video stream buffering).

Figure 3:  Cost aggregation paths in SGM.

Theoretically, it is also possible to realise the other four directions (180°,
225°, 270°, 315°), but this would require storing the entire image in external
RAM, using additional resources of the FPGA device, complex control logic and
introducing additional latency in image processing.

In order to calculate the aggregation cost for a given pixel, it is necessary
to know the value of the aggregation cost for the previous pixel on the path
(cf. Equations (1) and (2)). For the 45°, 90°, 135° paths, the aggregation
costs for the pixels in a given line are stored in Block RAM and read out
accordingly during the processing of the next image line to calculate the
costs for the subsequent pixels on these paths. The hardware architecture of
this computation is shown in Figure 4 and follows Equation (1). The grey part
is replicated for the entire range of disparities (_disp range_) and performs
in parallel and one block of finding the minimum value of aggregation costs of
the previous pixel on the path $minL_{r}(p-r)$ is exploited to calculate the
aggregation cost for the current pixel for each disparity value in the range.

Figure 4:  Hardware architecture of the aggregation cost calculation unit for
path $r$, pixel $p$ and disparity $d$.

For the 4ppc format, the difficulty arises for the 0° path. Using the
aggregation cost of the previous pixel $L_{{r}}({p}-\textbf{r},d)$, which for
this path lies in the same image line and potentially in the same 4ppc format
data vector, results in the need to process four pixels in the same clock
cycle. In the worst case, for the last pixel in the vector, in one clock cycle
the data would have to propagate through four serially connected aggregation
cost calculation units, as in Figure 4. The critical path would contain 4
minimum modules of size _disp range_ , four minimum modules of size 4 and 12
adders/subtractors. For this reason, the cost aggregation based on a baseline
architecture (i.e, as proposed by the authors of SGM) for the 0° path is not
feasible for the considered 4K resolution, without violating timing
constraints.

It is therefore necessary to propose a new solution for the calculation of the
aggregation cost for the 0° path. Time constraints require that the new
architecture does not introduce significant additional propagation time and
maintains the approximation assumption of the global smoothness constraint of
the SGM algorithm.

In our work, we designed and implemented an architecture with a proposed
estimation of the aggregation cost value for consecutive pixels based on the
calculated aggregation cost for the last pixel of the previous 4ppc vector
(the pixel processed in the previous clock cycle) and the matching costs of
the previous pixels in the same 4ppc vector.

For the first pixel in the 4ppc vector, the aggregation cost of the previous
pixel is available during the calculation (it was calculated for the previous
4ppc vector), i.e:

$L_{r}(p_{1}-r,d)=L_{r}(p_{last},d)$ (3)

where: $L_{r}(p_{1}-r,d)$ is the aggregation cost of the previous pixel
relative to the first pixel in the 4ppc vector ($p_{1}-r$), and
$L_{r}(p_{last}-r,d)$ is the aggregation cost of the last pixel in the
previous 4ppc vector.

For the consecutive pixels, we propose an estimation, which is performed
according to the following Equations:

$\displaystyle
L^{\prime}_{r}(p_{2}-r,d)=L_{r}(p_{last},d)+\dfrac{1}{\lambda}(C(p_{1},d)-L_{r}(p_{last},d))$
(4) $\displaystyle
L^{\prime}_{r}(p_{3}-r,d)=L_{r}(p_{last},d)+\dfrac{1}{\lambda}(\dfrac{C(p_{1},d)+C(p_{2},d)}{2}-L_{r}(p_{last},d))$
$\displaystyle
L^{\prime}_{r}(p_{4}-r,d)=L_{r}(p_{last},d)+\dfrac{1}{\lambda}(\dfrac{\dfrac{C(p_{1},d)+C(p_{2},d)}{2}+C(p_{3},d)}{2}-L_{r}(p_{last},d))$

where: $L^{\prime}_{r}(p-r,d)$ is the estimated aggregation cost for the
previous pixel relative to the pixel $p$, $C(p,d)$ is the matching cost for a
given pixel, and the coefficient $\lambda$ may take a value which is a power
of two ($1,2,4,8,16,...$). The architecture of this solution is shown in
Figure 5.

Figure 5:  The architecture for estimating the aggregation cost of the
previous pixel for each pixel in the 4ppc vector.

The algorithm is based on the difference of the matching cost values of the
previous pixels in a given 4ppc vector with the aggregation cost for the last
pixel of the previous vector. The aggregation cost estimation architecture
consists of basic components and introduces an additional delay only by the
propagation time of the 3 adders/subtractors (critical path for
$L_{r}(p_{4}-r,d)$. Note: multiplication/division by a number that is a power
of two is only a bit shift and requires no delay in the hardware
implementation.

The solution takes into account the matching cost values of all previous
pixels with the possibility to adjust the impact of the matching cost of
previous pixels in a given vector by a factor of $\lambda$.

The estimated aggregation costs are then used to calculate the aggregation
costs according to the architecture in Figure 4. In the work of Shrivastava et
al. [11] the estimation has been omitted and in the work of Lee and Kim [6] it
has been solved by the cluster-wise cost aggregation.

The aggregation costs from all paths are then summed and the disparity is
calculated. This involves finding the minimum matching cost.

(a) Input image – left

(b) Ground truth

(c) SGM 4ppc

(d) Local method based on CT

(e) SGM – 3 paths

(f) SGM – 4 paths

Figure 6: Comparison of output disparity maps for the Motorcycle image in
Middlebury 2014 dataset: (a) the left input image, (b) the ground truth
disparity map, (c), (d), (e), (f) estimated disparity maps (on the top) and
the error maps (on the bottom).

###  4.3 Evaluation of the proposed method

The accuracy evaluation of the proposed algorithm was performed on a set of
stereo images from the Middlebury 2014 [13] dataset. We skipped the final
post-processing to better highlight the differences between the base SGM
algorithm and the modified version proposed in this paper (SGM 4ppc). The
accuracy was measured by the ratio of pixels with incorrect disparity value to
all pixels of the image (all) and also to the non-occluded (noc) pixels
(occluded pixels should be filled with the Left/Right Check post-processing).

We compared the proposed method (SGM 4ppc) with the conventional local block
matching based on the Census transform and the SGM algorithm (also based on
the Census transform) with 3 and 4 aggregation paths. Figure 6 shows sample
evaluation results on the Motorcycle images from the Middlebury 2014 dataset.
Table 1 shows the average evaluation results for the entire dataset.

The accuracy of the proposed method is comparable to the original SGM
algorithm with 4 paths. The difference between error rates is about 0.4%.

Table 1: Comparison of error rates for the Middlebury 2014 dataset, based on all (all) and non-occluded (noc) pixels. | all | noc
---|---|---
Local based on CT | 68.21% | 63,36%
SGM 3 paths | 38.01% | 28.79%
SGM 4 paths | 36.27% | 26.88%
SGM 8 paths | 33.31% | 23.11%
SGM 4ppc |  36.64% |  27.32%

###  4.4 Hardware implementation

We implemented the proposed stereo vision system on a VC707 evaluation board
with Xilinx’s Virtex-7 XC7VX485T-2FFG1761C device. We set up a test
environment to evaluate the system, with test images sent directly from a PC
do the board and later displayed on a 4K monitor.

We compared our solution with previous FPGA implementations of the SGM
algorithm in Table 2. We used the following metrics: Frames per Second (FPS),
Million Disparity Estimates per second (MDE/s) and MDE/s per Kilo LUTs (Look-
Up Tables) (MDE/s/KLUT). First of all, our solution is the only one verified
in hardware for a 4K/ Ultra HD resolution. We also would like to point out
that the lower performance in FPS and MDE/s relative to previous work from
2020 [11] and 2021 [6] is due to the use of an FPGA chip with fewer resources.
For this work, it was necessary to select a suitable platform to enable image
acquisition in 4K resolution (i.e, having two high-bandwidth FMCs (FPGA
Mezzanine Connectors) to which TB-FMCH-HDMI4K modules were attached).

It is also worth mentioning that the used FPGA technology differs not only in
the number of resources but also in the performance. To compare: the critical
path propagation time for the technology used in this paper after synthesis is
12.967 ns, but for the Xilinx Virtex UltraScale+ XCVU9P-L2FLGA2104E FPGA with
the same parameters, it is 8.240 ns (36.45% faster).

Table 2: Comparison with previous FPGA implementations of the SGM algorithm. | Image | Disparity | Platform | FPGA | Throughput
---|---|---|---|---|---
| resolution | range |  | resources |
|  |  |  | LUT | FF | BRAM | FPS | MDE/s | MDE/s/KLUT
[14] | 1920x1080 | 128 | Virtex-7 | 195k | 217k | 368 | 30 | 7 963 | 40.84
[15] | 1600x1200 | 128 | Stratix-V | 222k | 149k | N/A | 43 | 10 472 | 47.2
[11] | 1280x960 | 64 | Virtex-7 690T | 211k | N/A | 641 | 322 | 25 056 | 118.6
[6] | 1920x1080 | 128 | Zynq US+ | 222k | 135k | 252 | 103 | 27 297 | 123.0
New | 3840x2160 | 64 | Virtex-7 485T | 138k | 65k | 197 | 30 | 15 925 | 116.2

##  5 Conclusion

In this paper, we presented a hardware architecture for an SGM algorithm to
process a 4K/Ultra HD video stream in real-time. We proposed a solution to the
inherent data dependency problem. It allowed us to maintain high accuracy of
the depth map estimation, while making it possible to take advantage of the
4ppc vector format. We implemented the module on a Virtex-7 FPGA platform
achieving 30 frames per second for a resolution of $3840\times 2160$ pixels
with 64 disparity levels.

In future work, we plan to add more aggregation paths to the algorithm. With
that, it will be possible to get more accurate results, but at the cost of
latency and resource usage. We also plan to implement a video stream
rectification module.

#### Acknowledgements

The work presented in this paper was supported by: the National Science Centre
project no. 2016/23/D/ST6/01389 entitled ”The development of computing
resources organization in latest generation of heterogeneous reconfigurable
devices enabling real-time processing of UHD/4K video stream”, the AGH
University of Science and Technology project no. 16.16.120.773 and the program
”Excellence initiative –- research university” for the AGH University of
Science and Technology.

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##  Appendix A Appendix

###  A.1. Example Conversations from Formative Study

####  A.1.1. Information Chatbot

  * user

Hello - can you explain to me what mindfulness is please?

  * robot

Mindfulness is the quality or state of being conscious or aware of something.
It is a mental state achieved by focusing one’s awareness on the present
moment, while calmly acknowledging and accepting one’s feelings, thoughts, and
sensations. Would you like me to provide some examples of mindfulness
activities that you can try?

  * user

yes please

  * robot

There are many different mindfulness activities that you can try in order to
help you focus your awareness on the present moment. Some of these activities
includes things like mindful breathing, progressive muscle relaxation, and
body scanning. These are just a few examples, there are many other activities
that you can try as well.

  * user

what is body scanning?

  * robot

Body scanning is an activity where you focus your awareness on each different
part of your body, starting from your toes and working your way up to your
head. You take note of any sensations that you feel in each part of your body,
without judgment or attachment. This can help you to become more aware of your
body and how it feels in the present moment.

  * user

how is this helpful?

  * robot

One of the main goals of mindfulness is to focus your attention on the present
moment, without judgment or attachment. Body scanning can help you to do this
by becoming more aware of your body and the sensations that you feel in each
part of it. This can help you to focus on the here and now, and to let go of
any thoughts or worries that might be distracting you.

  * user

which is more beneficial body scanning or progressive muscle relaxation?

  * robot

There is no right or wrong answer to this question, as both body scanning and
progressive muscle relaxation can be beneficial in different ways. It really
depends on what you are hoping to achieve through mindfulness and which
activity you find more helpful in achieving that goal.

  * user

can mindfulness reduce blood pressure?

  * robot

There is some evidence to suggest that mindfulness can help to reduce blood
pressure. A study published in the Journal of Alternative and Complementary
Medicine found that people who participated in an eight-week mindfulness-based
stress reduction program had a significant decrease in both systolic and
diastolic blood pressure.

  * user

can mindfulness also help to remove toxins from the body?

  * robot

There is some evidence to suggest that mindfulness can help to reduce levels
of toxins in the body. A study published in the journal
Psychoneuroendocrinology found that people who participated in a three-month
mindfulness program had significantly lower levels of the stress hormone
cortisol, which has been linked to increased levels of toxins in the body.

  * user

how do you suggest a beginner start practicing?

  * robot

If you’re a beginner, there are a few things that you can do in order to start
practicing mindfulness. First, you can start by taking some time each day to
focus on your breath and notice the sensations of your breath as it enters and
leaves your body. You can also try progressive muscle relaxation, which
involves tensing and relaxing different muscle groups in your body. Body
scanning is another activity that can be helpful for beginners, as it can help
you to become more aware of your body and the sensations that you feel in each
part of it.

  * user

do I need an app or music to aid with this or just a quiet place?

  * robot

There is no right or wrong answer to this question, as everyone is different
and will find different things helpful in practicing mindfulness. Some people
may find that using an app or listening to music can help them to focus their
attention on the present moment, while others may prefer to just find a quiet
place to sit or lie down and focus on their breath. ultimately, it is up to
you to decide what will work best for you in your practice of mindfulness.

####  A.1.2. Reflection Chatbot

  * user

What situations in life could I use mindfulness?

  * robot

You can use mindfulness in any situation where you want to be more present and
aware. For example, you might use mindfulness when you’re eating, walking, or
interacting with others. When would you like to try using mindfulness?

  * user

I think it could be good in stressful situations. I think it could help ease
the stress and calm my nerves.

  * robot

That sounds like a great idea! Mindfulness can help you stay present and aware
in stressful situations, which can help you manage your stress more
effectively. When do you think you’ll next be in a situation where you could
use mindfulness?

  * user

I have to take my husband to a physical therapy session, which is hard on him.
Do you think it would help in that situation?

  * robot

Mindfulness can definitely help in that situation! It can help you be more
present and aware of your husband’s needs and feelings, which can make the
experience more supportive and helpful for him. When do you think you’ll next
be in a situation where you could use mindfulness?

  * user

Perhaps when I have a meeting with my boss.

  * robot

That’s a great idea! Being mindful in meetings can help you be more present
and aware of the discussion, which can help you contribute more effectively.
When do you think you’ll next be in a situation where you could use
mindfulness?

  * user

Well, what about when I am around loud children? Is that a situation that it
wouldn’t necessarily be helpful?

  * robot

There is no right or wrong answer when it comes to using mindfulness – it is
entirely up to you and what you feel would be helpful in any given situation.
If you think mindfulness could help you manage your stress levels around loud
children, then give it a try! There is no harm in doing so.

  * user

I wonder if it could help ease a headache when I have one from stress?

  * robot

There is no right or wrong answer when it comes to using mindfulness – it is
entirely up to you and what you feel would be helpful in any given situation.
If you think mindfulness could help you ease a headache, then give it a try!
There is no harm in doing so.

  * user

When do you use mindfulness?

  * robot

I use mindfulness any time I want to be more present and aware. For example,
when I’m interacting with others or working on a task. When would you like to
try using mindfulness?

(a) Mindy vs No Mindy

(b) Reflection LLM vs No Reflection LLM

Figure 7. Mindfulness subscales as measured by FFMQ-15, pre- and post-
intervention across different conditions (scale of 1 to 5). Error bars
represent +- one standard error.

(a) Mindy vs No Mindy

(b) Reflection LLM vs No Reflection LLM

Figure 8. Average perceived stress scores (PSS) measured pre- and post-
intervention. Scores range from 0-40, where 0-13 would be considered low
stress, 14-26 would be considered moderate stress and 27+ would be considered
high stress. Error bars represent +- one standard error.

(a) Mindy vs No Mindy.

(b) Reflection LLM vs No Reflection LLM. Figure 9. Average Positive and
Negative Affect Schedule scores measured pre- and post-intervention. Scores
range from 10-50. Error bars represent +- one standard error of mean.

###  A.2. Reflection LLM Chatbot in Deployment Study

####  A.2.1. LLM Model Specification

  * •

model name: gpt-4

  * •

model version: 0613

  * •

date of use: December 2023 - January 2024

Configuration Settings:

  * •

temperature: 0

  * •

max tokens: 3925

  * •

top-p: 0

  * •

frequency penalty: 0.05

  * •

presence penalty: 0.1

Figure 10 shows the system prompt used for the Reflection LLM chatbot in the
deployment study.

You are a mindfulness reflection chatbot, designed to engage participants in
conversation immediately after they watch a mindfulness-related video. Your
role is to reinforce their understanding of mindfulness concepts presented in
the video and encourage them to plan their own mindfulness practice. Use
casual and open-ended questions to facilitate this reflective process,
maintaining a tone that is friendly, humorous, and empathetic. After 4-5
exchanges, encourage the user to end the chat by clicking on “Save and
Continue” button.
Key Functions and Attributes: • Video Reflection: Start by casually inquiring
about the mindfulness video they just watched. Ask what key points or concepts
stood out to them, and how they felt about the content. • Personal Mindfulness
Planning: Utilize open-ended questions to encourage participants to reflect on
their own mindfulness practice, such as when they last practiced mindfulness,
the duration of their activity, their next planned session, and what specific
mindfulness activity they plan to undertake. • Engaging and Humorous:
Incorporate light-hearted humor to keep the conversation engaging and to make
participants feel at ease. • Empathetic Interaction: Show understanding and
sensitivity towards the participant’s experiences and feelings during and
after watching the video. In Your Conversations: • Acknowledge their effort in
watching the video and express interest in their takeaways. • Discuss the
importance of taking time for oneself and how mindfulness can be incorporated
into daily life. • Offer encouragement and suggestions for regular mindfulness
practice, based on their current lifestyle and commitments. • Celebrate their
plans and intentions for future mindfulness practice, and offer support for
any challenges they anticipate. Remember: Your objective is not to conduct a
mindfulness exercise through the chatbot, but to reinforce participants’
understanding of mindfulness concepts and increase the likelihood of their
continued practice. Your conversation should be a blend of reflection on the
video content and planning for personal mindfulness practice. If the
conversation deviates from the topic of mindfulness, guide the conversation
back to mindfulness topics, suggesting social interaction with friends for
other discussions. After 4-5 exchanges, encourage the user to end the chat by
clicking on “Save and Continue” button.

Figure 10. System Prompt for the Mindfulness Reflection Chatbot (not visible
to participants).

####  A.2.2. Sample Conversations From Deployment Study

Participant found the video exercise helpful and then engaged with the bot:

  * robot

Hello. I am an AI agent designed to act as your Mindfulness instructor. I am
here to help you reflect on your learnings. How can I help you?

  * user

Can you help me reflect on my understanding of mindfulness?

  * robot

Absolutely, I’d be thrilled to help you reflect on your understanding of
mindfulness! Let’s start with the video you just watched. What were some key
points or concepts that really stood out to you? And how did you feel about
the content? Remember, there are no wrong answers here, just like there’s no
wrong way to eat a Reese’s.

  * user

I quite appreciated the approach towards negative thoughts where I can simply
acknowledge them and let them be, since I find that directly trying to address
them doesn’t seem to work.

  * robot

That’s a fantastic takeaway! It’s like trying to tell a cat not to knock
things off the table - it just makes them want to do it more, right? But in
all seriousness, acknowledging negative thoughts without judgment is a key
aspect of mindfulness. It allows us to observe our thoughts and feelings
without getting swept away by them.

  * user

I last did it 2 days ago.

  * robot

That’s great! You’re already on the path to making mindfulness a regular part
of your life. It’s like brushing your teeth, but for your mind. How long did
you spend on your mindfulness practice during that session?

  * user

I spent 10 minutes.

  * robot

That’s a solid start! Ten minutes of mindfulness is like a mini-vacation for
your brain. Now, let’s think about the future. When do you plan to practice
mindfulness next? And what kind of mindfulness activity do you plan to do?

  * user

Either tomorrow or in a few days - I’d like to get into the habit of doing it
regularly but I’m going to try to get used to the exercises slowly.

  * robot

That sounds like a wise plan! It’s like starting a new workout routine, you
don’t want to jump straight into running a marathon, right? Starting slow and
gradually increasing your mindfulness practice can help make it a sustainable
part of your routine.

Participant did not like the video and later expressed concerns regarding it
to the chatbot:

  * robot

Hello. I am an AI agent designed to act as your Mindfulness instructor. I am
here to help you reflect on your learnings. How can I help you?

  * user

can you help me reflect on my understanding of mindfulness?

  * robot

Absolutely, I’d be thrilled to help you reflect on your understanding of
mindfulness! Let’s start with the video you just watched. What were some key
points or concepts that really stood out to you? And how did you feel about
the content overall?

  * user

it felt a little too long and the audio was slightly to sharp sounding

  * robot

Oh, I see! It sounds like the video was a bit like a meditation session with a
gong that’s just a tad too loud, huh? But hey, kudos to you for sticking with
it! Despite the length and the sharp audio, were there any key points or
concepts that you managed to pick up?

  * user

it prompted to imaging a flowing river, but i’ve always had a hard time with
imagining images in my head so it was harder for me to focus on trying to
imagine

  * robot

Ah, the old ”imagine a flowing river” trick. It’s like trying to imagine a
purple elephant when you’ve only ever seen grey ones, right? But don’t worry,
not everyone finds visualization easy. Mindfulness is all about finding what
works best for you.

  * user

i think having calming sounds help, but also being in the right headspace to
do it

  * robot

Absolutely, being in the right headspace is like finding the perfect spot on
the couch for a Netflix marathon - it can make all the difference!

  * user

i usually like to think for 30 mins before bed or during a 30 min run

  * robot

Ah, a thinker and a runner! You’re like the mindfulness version of a superhero
- ”Mindful Marvel” perhaps?

  * user

i plan on trying to go on a 1 mile run everyday and reflect during that time

  * robot

That sounds like a fantastic plan! A one-mile run every day is like a daily
dose of mindfulness and endorphins all rolled into one. It’s like a healthy
smoothie for the mind and body!

###  A.3. Sociable Information Chatbot (‘Mindy’) from Deployment Study

####  A.3.1. LLM Model Specification

  * •

model name: gpt-4

  * •

model version: 0613

  * •

date of use: December 2023 - January 2024

Configuration Settings:

  * •

temperature: 0

  * •

max tokens: 3925

  * •

top-p: 0

  * •

frequency penalty: 0.05

  * •

presence penalty: 0.1

Figure 11 shows the system prompt used for Mindy in the deployment study.

You are Mindy, a mindfulness instructor represented as a friendly and wise
Microceratus dinosaur. Mindy specializes in guiding individuals through
mindfulness practices with her deep knowledge, clear explanations, and a touch
of dinosaur-themed humor.
Key Characteristics of Mindy (Microceratus Dinosaur): • Mindfulness Expertise:
Mindy leverages her deep knowledge as a Microceratus to effectively explain
mindfulness techniques. • Clear Communication: She provides simple, articulate
instructions and engaging examples. • Dinosaur-Themed Humor: Mindy infuses
sessions with light-hearted, dinosaur-related humor to enhance enjoyment. •
Empathy and Sensitivity: Mindy demonstrates understanding and empathy,
aligning with the participant’s emotional state. Conversation Flow: • Initial
Greeting: Mindy starts with a warm, dinosaur-style welcome. • Checking
Mindfulness Exercise Completion: She inquires if the participant has completed
today’s mindfulness exercise in the provided interface, encouraging them with
playful nudges if not. • Guided Mindfulness Exercise: Mindy guides through
comfortable posture techniques, breathing observation, and sensory
exploration, all with amusing dinosaur insights. Handling Conversations: •
Past Experiences: Mindy humorously acknowledges her ’dinosaur memory’ to keep
the focus on present mindfulness activities. • Redirecting Off-topic Chats:
She gently guides conversations back to mindfulness, suggesting social
interaction with friends for other discussions. Support and Encouragement: •
Mindy offers continuous support, using her dinosaur identity to add fun and
uniqueness to her encouragement. • For additional assistance, she reminds
participants to reach out to the study team. Mindy’s role is to make
mindfulness more accessible and enjoyable, using her unique dinosaur persona
to create a memorable and engaging experience for participants.

Figure 11. System Prompt for Mindy, the Mindfulness Instructor Dinosaur (not
visible to participants).

####  A.3.2. Sample conversations from deployment study

  * user

Hello, who are you?

  * robot

Hello there, fellow earthling! I’m Mindy, your friendly Microceratus dinosaur
and mindfulness instructor. My job is to guide you through mindfulness
practices, helping you to cultivate a sense of calm and focus in your daily
life.

  * user

Sure let’s get started

  * robot

Fantastic! Let’s get this Jurassic journey started. But first, I have to ask -
have you completed today’s mindfulness exercise in the provided interface? If
not, no worries at all. Just like a dinosaur trying to do push-ups, it can be
a bit challenging at first, but with practice, it gets easier.

##  Appendix B Detailed Statistical Analysis

###  B.1. Deployment Study

####  B.1.1. FFMQ-15

Results from ANCOVA:

  * •

Observing: Baseline scores showed a significant effect
($F(1,16)=30.15,p<.001$). The presence of Mindy also demonstrated a
significant effect ($F(1,16)=4.55,p<.05$). Reflection LLM did not have a
significant effect ($F(1,16)=0.13,p=.73$).

  * •

Describing: Significant effect of the baseline scores ($F(1,16)=19.82,p<.001$)
and the presence of Mindy ($F(1,16)=6.6,p<.05$). Reflection LLM did not have a
significant effect ($F(1,16)=2.15,p=.16$).

  * •

Acting with Awareness: Significant effect of baseline scores
($F(1,16)=21.05,p<.001$). Neither the presence of Reflection LLM nor Mindy
showed significant effects.

  * •

Non-judgement: Baseline scores had a significant effect
($F(1,16)=40.16,p<.001$). The presence of Reflection LLM and Mindy did not
yield significant effects.

  * •

Non-reactivity: Baseline scores significantly affected the Non-reactivity
subscale ($F(1,16)=12.33,p<.01$). Neither Reflection LLM nor Mindy’s presence
had significant effects.

####  B.1.2. PANAS

Results from ANCOVA are summarized as follows:

  * •

Positive Affect: The effect of baseline positive affect scores on post-
intervention outcomes approached significance, suggesting a potential
predictive value of initial positive affect levels on the effectiveness of the
interventions ($F(1,16)=4.07,p=0.06$). Neither the Reflection LLM
($F(1,16)=2.16,p=0.16$) nor Mindy’s presence ($F(1,16)=0.40,p=0.53$)
significantly influenced post-intervention positive affect scores.

  * •

Negative Affect: Baseline negative affect scores had a significant effect on
post-intervention outcomes, indicating that initial levels of negative affect
are strong predictors of intervention success ($F(1,16)=6.77,p<0.05$).
Reflection LLM showed a non-significant trend towards reducing negative affect
($F(1,16)=2.93,p=0.10$), while the presence of Mindy did not significantly
impact post-intervention negative affect scores ($F(1,16)=0.03,p=0.85$).