Sensor-Integrated Chairs for Lower Body Strength and Endurance Assessment

Abstract

This paper describes an automated method and device to conduct the Chair Stand Tests of the Fullerton Functional Test Battery. The Fullerton Functional Test is a suite of physical tests designed to assess the physical fitness of older adults. The Chair Stand Tests, which include the Five Times Sit-to-Stand Test (5xSST) and the 30 Second Sit-to-Stand Test (30CST), are the standard for measuring lower-body strength in older adults. However, these tests are performed manually, which can be labor-intensive and prone to error. We developed a sensor-integrated chair that automatically captures the dynamic weight and distribution on the chair. The collected time series weight–sensor data is automatically uploaded for immediate determination of the sit-to-stand timing and counts, as well as providing a record for future comparison of lower-body strength progression. The automatic test administration can provide significant labor savings for medical personnel and deliver much more accurate data. Data from 10 patients showed good agreement between the manually collected and sensor-collected 30CST data (M = 0.5, SD = 1.58, 95% CI = 1.13). Additional data processing will be able to yield measurements of fatigue and balance and evaluate the mechanisms of failed standing attempts.

Full text

Citation: Lee, A.W.; Lee, M.S.; Yeh,
D.P.; Yeh, H.-J.J. Sensor-Integrated
Chairs for Lower Body Strength and
Endurance Assessment. Sensors 2024,
24, 788. https://doi.org/10.3390/
s24030788
Academic Editor: Stefan Wagner
Received: 29 November 2023
Revised: 18 January 2024
Accepted: 22 January 2024
Published: 25 January 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Article
Sensor-Integrated Chairs for Lower Body Strength and
Endurance Assessment
Alexander W. Lee 1, Melissa S. Lee 2, Daniel P. Yeh 2and Hsi-Jen J. Yeh 3,*
1Chino Premier Surgery Center, Chino, CA 91710, USA; alexanderlee8690@gmail.com
2Walnut Valley Research Institute, Walnut, CA 91789, USA; melissalee2390@gmail.com (M.S.L.)
3Department of Engineering and Computer Science, Azusa Pacific University, Azusa, CA 91702, USA
*Correspondence: hyeh@apu.edu
Abstract: This paper describes an automated method and device to conduct the Chair Stand Tests
of the Fullerton Functional Test Battery. The Fullerton Functional Test is a suite of physical tests
designed to assess the physical fitness of older adults. The Chair Stand Tests, which include the Five
Times Sit-to-Stand Test (5xSST) and the 30 Second Sit-to-Stand Test (30CST), are the standard for
measuring lower-body strength in older adults. However, these tests are performed manually, which
can be labor-intensive and prone to error. We developed a sensor-integrated chair that automatically
captures the dynamic weight and distribution on the chair. The collected time series weight–sensor
data is automatically uploaded for immediate determination of the sit-to-stand timing and counts, as
well as providing a record for future comparison of lower-body strength progression. The automatic
test administration can provide significant labor savings for medical personnel and deliver much
more accurate data. Data from 10 patients showed good agreement between the manually collected
and sensor-collected 30CST data (M = 0.5, SD = 1.58, 95% CI = 1.13). Additional data processing
will be able to yield measurements of fatigue and balance and evaluate the mechanisms of failed
standing attempts.
Keywords: aging; leg strength; chair stand test; sit-to-stand; strain gauge; weight sensor;
multi-channel
data; temperature compensation; weight distribution; internet of things
1. Introduction
Lower-body strength is an important factor for the overall health and quality of life of
elderly adults [
1
]. Falls can cause catastrophic injuries in older adults, which can be very
difficult to recover from [
2
]. Lower-body strength measurements can be used to assess
the fall risks associated with aging. The Fullerton Functional Test developed by Jones and
Rikli [
3
–
5
] contains two lower-body tests that use the ability of an individual to stand from
a sitting position on a chair without the assistance of the hands to provide a quantitative
measure of lower-body strength. The two Chair Stand Tests measure the time required to
execute a fixed repetition of sit-to-stand and the number of sit-and-stands in a specified
time. Specifically, the Five Times Sit-to-Stand Test (5xSST) measures the amount of time
in seconds required for the subject to stand from a sitting position five times, while the
30 Second Sit-to-Stand Test (30CST) counts the number of sit-to-stand repetitions that the
subject can execute in a 30-s period [6].
These tests have been correlated with fall risk [
7
], and standard performance metrics
have been developed for various age groups. For example, for the 5xSST, there is the risk of
recurrent falls for times greater than 15 s [
8
], and further assessment of fall risk is required
for times greater than 12 s. Norms or averages for various age groups are also given in the
references [8,9].
The chair stand tests are performed by the patient and guided by a clinician manually
with a stopwatch. The clinician records the number of sit-to-stand cycles or the time to
Sensors 2024,24, 788. https://doi.org/10.3390/s24030788 https://www.mdpi.com/journal/sensors

Sensors 2024,24, 788 2 of 12
perform the test. This research sought to create an automatic method to accurately acquire
the chair stand test data, reducing the workload of the clinician administering the test.
Several other methods have been proposed for the automated collection of chair stand
data. Millor et al. [
10
] proposed using a specialized accelerometer and gyroscope attached
to the subjects’ L3 lumbar spine to collect both movement and leaning data during the sit
and stand transition. The method used wired technology and is thus not very portable.
Cobo et al. [
11
] proposed an accelerometer attached to the thigh of the patient to measure
movement and transmit the data via Bluetooth to a tablet. This approach uses wireless
technology to maintain portability and takes into account the actual usage environment.
Hellmers et al. [
12
] proposed a similar system using an IMU secured with a specialized
inertial measurement unit (IMU, accelerometer plus gyroscope) belt and a separate ground
reaction force plate to collect data when the user is standing. These types of systems
require patients to wear sensors on their bodies, and clinicians must take additional steps
to attach the sensors securely. This can add obtrusiveness to the patient and complications
in real-world clinical settings.
Goncalves et al. [
13
] proposed using an infrared distance sensor mounted above the
chair to measure the distance as the patient stands and sits and a microswitch to measure
pressure on the seat. Takeshima et al. [
14
] proposed the use of infrared depth cameras to
capture the motion of patients as they execute sit-to-stand movements. While not obtrusive
to the patient, the overhead distance sensor mounted on a tripod column or infrared depth
camera mounted on a tripod 3 m away can cause difficulty in setup and portability. In
another paper, Cobo et al. [
15
] designed an ultrasonic sensor mounted on the back of the
seat to measure the distance between the subject and the seat back. The authors reported
noisy sensor signals that may degrade performance for older adults.
In our research, we designed and built sensor-integrated chairs that use multiple
sensors mounted on a commonly available chair. To maximize portability, we planned
for wireless networking technology and battery operation to avoid tethering the sensor-
integrated chairs, and we transmitted the collected sensor data to a cloud-based server. We
designed the entire system as a single unit, so no sensors are attached to the patients and
no sensors need to be set up external to the chair.
2. Materials and Methods
The main goals of the design of the sensor-integrated chair are (1) the availability
of components, (2) reduction in costs, and (3) the ease of exporting the design to other
chairs. All of these factors facilitate the integration of the design into existing chairs that
may function well in various facilities and be preferred by different clinicians. Therefore,
the design focuses on using commonly available components and avoiding specialized or
custom parts that add costs and are difficult to obtain.
The bill of materials is presented in Table 1.
Table 1. Bill of materials of the sensor-integrated chair.
No. Qty. Part Description
1 1 Chair Straight back, without armrest, seat 43 cm (17 in)
2 1 Board 50 cm ×50 cm ×2 cm (20 in ×20 in ×¾ in)
3 1 ESP32-WROOM-32 Module with USB Port
4 1 USB Battery Pack Supply 5V power to ESP32
5 4 Weight Scale Feet Removed from bathroom scale,
contains weight sensor
6 4 HX711 Module with analog end and digital end
7 4 50-kg weight sensor For temperature compensation
2.1. Sensor Integration
To meet the above goals, the sensor-integrated chair uses strain-gauge weight sensors
mounted to the bottom of the legs of a commonly available chair (Figure 1). This design is

Sensors 2024,24, 788 3 of 12
an improvement on previous designs [
16
], which placed weight sensors under the seating
area. Placing the sensors under the seating area requires more extensive modification in
that it involves the removal of the seating area and mounting the weight sensors. The
seating area needs to be supported entirely by the weight sensors, which causes it to be
free of mechanical constraints, and the entire chair lacks stability.
Sensors 2024, 24, x FOR PEER REVIEW 3 of 11
2.1. Sensor Integration
To meet the above goals, the sensor-integrated chair uses strain-gauge weight sensors
mounted to the boom of the legs of a commonly available chair (Figure 1). This design is
an improvement on previous designs [16], which placed weight sensors under the seating
area. Placing the sensors under the seating area requires more extensive modification in
that it involves the removal of the seating area and mounting the weight sensors. The
seating area needs to be supported entirely by the weight sensors, which causes it to be
free of mechanical constraints, and the entire chair lacks stability.
Figure 1. Drawing of the sensor-integrated chair. Four (4) weight scale feet are mounted directly
under the chair legs on the other side of the board, each containing a 50 kg weight sensor. The elec-
tronics consist of 4× analog-to-digital converters, 4× temperature-compensating weight sensors, the
WiFi-enabled microcontroller, and a USB baery pack for power. Each weight scale foot is connected
to its respective ADC in a Wheatstone bridge configuration with a temperature-compensating
weight sensor.
Placing the sensors on the boom of the legs has its challenges, as there needs to be a
mechanical assembly to hold the stationary part of the weight sensors in place while al-
lowing the movable part to have free motion to provide accurate deflection and strain
measurement. For the current implementation, we chose a readily available mechanical
structure, using the feet of commercial human body weight scales or bathroom scales. The
feet are optimized for human body weight ranges, and each scale typically comes with 4
feet. Mounting the scale feet on the chair legs requires matching the chair legs’ diameters
to the scale feet. Therefore, the chair was first fixed onto a rigid board, and then, the feet
were aached to the opposite side of the rigid board directly under each of the legs.
The chair must be mounted on the board so the board does not protrude excessively
in front of the chair. This design ensures that the subject does not place their feet on the
board while siing and that the subject’s legs remain clear of the board while performing
the sit-to-stand movements. Doing so would compromise the weight measurement as the
user’s weight would be borne (partially) by the board. We determined that a 1 inch (2.5
cm) margin is acceptable. In addition, mounting the board and the weight scale feet to the
legs of the chair adds height to the chair. The legs of the chair may need to be shortened
to maintain a 17 inch (43 cm) seat height as specified in the Chair Stand Tests.
Within each of the four scale feet is a weight sensor designed so that the part of the foot
in contact with the ground applies the weight force to the movable portion of the sensor. Each
weight sensor consists of two resistive elements (strain gauge) deposited on opposite sides of
the movable portion, nominally at 1 kΩ. When the movable portion is deformed or strained
from the applied force, one strain gauge is compressed, decreasing its resistance, while the
other strain gauge is stretched, increasing its resistance. For small strains, the strain is linearly
proportional to stress, and the resistance change is linearly proportional to the strain.
Figure 1. Drawing of the sensor-integrated chair. Four (4) weight scale feet are mounted directly under
the chair legs on the other side of the board, each containing a 50 kg weight sensor. The electronics
consist of 4
×
analog-to-digital converters, 4
×
temperature-compensating weight sensors, the WiFi-
enabled microcontroller, and a USB battery pack for power. Each weight scale foot is connected
to its respective ADC in a Wheatstone bridge configuration with a temperature-compensating
weight sensor.
Placing the sensors on the bottom of the legs has its challenges, as there needs to be
a mechanical assembly to hold the stationary part of the weight sensors in place while
allowing the movable part to have free motion to provide accurate deflection and strain
measurement. For the current implementation, we chose a readily available mechanical
structure, using the feet of commercial human body weight scales or bathroom scales. The
feet are optimized for human body weight ranges, and each scale typically comes with
4 feet. Mounting the scale feet on the chair legs requires matching the chair legs’ diameters
to the scale feet. Therefore, the chair was first fixed onto a rigid board, and then, the feet
were attached to the opposite side of the rigid board directly under each of the legs.
The chair must be mounted on the board so the board does not protrude excessively
in front of the chair. This design ensures that the subject does not place their feet on the
board while sitting and that the subject’s legs remain clear of the board while performing
the sit-to-stand movements. Doing so would compromise the weight measurement as the
user’s weight would be borne (partially) by the board. We determined that a 1 inch (2.5 cm)
margin is acceptable. In addition, mounting the board and the weight scale feet to the legs
of the chair adds height to the chair. The legs of the chair may need to be shortened to
maintain a 17 inch (43 cm) seat height as specified in the Chair Stand Tests.
Within each of the four scale feet is a weight sensor designed so that the part of the
foot in contact with the ground applies the weight force to the movable portion of the
sensor. Each weight sensor consists of two resistive elements (strain gauge) deposited on
opposite sides of the movable portion, nominally at 1 k
Ω
. When the movable portion is
deformed or strained from the applied force, one strain gauge is compressed, decreasing
its resistance, while the other strain gauge is stretched, increasing its resistance. For small
strains, the strain is linearly proportional to stress, and the resistance change is linearly
proportional to the strain.
For weight scale applications, only the total weight is required. Here, the four sensors
in the four feet are wired together in a single Wheatstone bridge configuration that sums
the changes in resistance when the voltage difference is measured across the bridge. In this

Sensors 2024,24, 788 4 of 12
case, one amplifier and one analog-to-digital converter (ADC) are required to obtain the
sum of the weights on the four feet.
In our application, we want to obtain four independent weight readings from each of
the four sensors. For four independent weight channels, each weight sensor must connect
independently to its own signal path (amplifier plus ADC). In this configuration, two 1 k
Ω
resisters are typically used to form the other arm of the Wheatstone bridge.
During testing, we found that the weight sensor plus two resistor configuration is
sensitive to temperature changes, causing a significant drift in the weight reading over time
as the temperature changes. We did not discover other environmental sensitivities, such
as sensitivity to light. Although relative humidity can cause drift to strain gauge sensors,
humidity changes are typically slow relative to the period of data capture; we compensate
for any long-term drift by calibrating the sensors before each measurement.
We experimented with different designs to compensate for the temperature drift.
We found that using an unweighted 1 k
Ω
weight sensor [
17
] as the other arm of the
Wheatstone bridge (Figure 2), instead of using two 1 k
Ω
resistors, effectively compensated
for the temperature drift.
Sensors 2024, 24, x FOR PEER REVIEW 4 of 11
For weight scale applications, only the total weight is required. Here, the four sensors
in the four feet are wired together in a single Wheatstone bridge configuration that sums
the changes in resistance when the voltage difference is measured across the bridge. In
this case, one amplifier and one analog-to-digital converter (ADC) are required to obtain
the sum of the weights on the four feet.
In our application, we want to obtain four independent weight readings from each of
the four sensors. For four independent weight channels, each weight sensor must connect
independently to its own signal path (amplifier plus ADC). In this configuration, two 1
kΩ resisters are typically used to form the other arm of the Wheatstone bridge.
During testing, we found that the weight sensor plus two resistor configuration is
sensitive to temperature changes, causing a significant drift in the weight reading over
time as the temperature changes. We did not discover other environmental sensitivities,
such as sensitivity to light. Although relative humidity can cause drift to strain gauge sen-
sors, humidity changes are typically slow relative to the period of data capture; we com-
pensate for any long-term drift by calibrating the sensors before each measurement.
We experimented with different designs to compensate for the temperature drift. We
found that using an unweighted 1 kΩ weight sensor [17] as the other arm of the Wheat-
stone bridge (Figure 2), instead of using two 1 kΩ resistors, effectively compensated for
the temperature drift.
Figure 2. Connection diagram of a weight sensing channel with temperature compensation. The
analog end of the HX711 module contains the excitation or input terminals (E+ and E−) and the
signal or output terminals (A+ and A-) of the Wheatstone bridge. One arm of the bridge consists of
the upper and lower strain gauge resistors of the weight-measuring sensor, and the other arm con-
sists of the lower and upper strain gauge resistors of the temperature-compensating sensor. The
reversal of the connections between the sensors provides compensation for the temperature drift.
(Diagram modified from the ESP Easy Document site [18]).
We chose an ASIC (application-specific integrated circuit) optimized for human
weight range measurements for the amplifier and ADC. Weight-measuring ADCs have
different requirements compared to audio or other types of ADCs. Weight sensors are
rated by their full measurement range, the maximum force that can be applied before sig-
nificant nonlinear response or damage to the sensor. Using stiffer (higher modulus) ma-
terial for the movable part results in smaller strain from a given stress and, therefore, a
larger measurement range. Human body weight sensors are typically designed for a 50
kg range, for a total weight measure of 4 × 50 kg = 200 kg with 0.1 kg resolution. Since the
change in strain is very small at the full rated measurement range, the ADC must have
very good resolution and low noise. However, since they measure static weight, the
Figure 2. Connection diagram of a weight sensing channel with temperature compensation. The
analog end of the HX711 module contains the excitation or input terminals (E+ and E
−
) and the
signal or output terminals (A+ and A
−
) of the Wheatstone bridge. One arm of the bridge consists
of the upper and lower strain gauge resistors of the weight-measuring sensor, and the other arm
consists of the lower and upper strain gauge resistors of the temperature-compensating sensor. The
reversal of the connections between the sensors provides compensation for the temperature drift.
(Diagram modified from the ESP Easy Document site [18]).
We chose an ASIC (application-specific integrated circuit) optimized for human weight
range measurements for the amplifier and ADC. Weight-measuring ADCs have different
requirements compared to audio or other types of ADCs. Weight sensors are rated by their
full measurement range, the maximum force that can be applied before significant nonlinear
response or damage to the sensor. Using stiffer (higher modulus) material for the movable
part results in smaller strain from a given stress and, therefore, a larger measurement range.
Human body weight sensors are typically designed for a 50 kg range, for a total weight
measure of 4
×
50 kg = 200 kg with 0.1 kg resolution. Since the change in strain is very
small at the full rated measurement range, the ADC must have very good resolution and
low noise. However, since they measure static weight, the measurement rate of weight
scales can be low, on the order of ~1 Hz. With 10 sample averaging, the sample rate of the
ADC is in the order of ~10 sps (samples per second).

Sensors 2024,24, 788 5 of 12
We chose the commercially available HX711 ASIC from Avia Semiconductors [
19
]
for the initial system due to its high commercial availability. The HX711 is an integrated
amplifier and ADC chip with a selectable sample rate of 10 sps or 80 sps and a resolution
of 24 bits with an effective number of bits (ENOBs) of 20 or lower. For ease of connection,
we used HX711 ICs packaged in module form. The module consists of an analog end
that powers the Wheatstone bridge and reads the differential signal and a digital end that
provides a 2-wire protocol to the host microcontroller or CPU.
Other signal chain components can be used as well, including the NAU7802 ASIC
from Nuvoton [
20
] and the ADS1231 ASIC from Texas Instruments [
21
], both of which
operate at similar specifications (24-bit ADC at 10 sps or 80 sps). The TI ADS1222 [
22
]
provides a 24-bit ADC with 20-bit ENOBs at 240 sps, which can be used if a higher sample
rate is desired. However, we found 80 sps to be sufficient for the sit-to-stand application.
The digital end of each HX711 module is connected to two pins on the microcontroller.
The digital interface is not I2C compliant, and a custom driver is used. However, due to
the use of the custom driver whose pseudocode and timing are detailed in the datasheet, it
became simpler for us to create an asynchronous multi-channel (multi-chip) driver used in
this 4-chip application.
2.2. Microcontroller Platform
A data interface needs to be provided to transfer data collected from the sensors
integrated into the chair in real time. For convenience of operation, we chose a wireless
interface, specifically WiFi. An additional advantage of a microcontroller that supports
WiFi is that the device can upload data directly to the cloud using a ubiquitous and existing
WiFi infrastructure without the need for any bridge devices, as would be required if, for
example, Bluetooth or Zigbee were used. With appropriate software development, the
microcontroller can collect data, connect to the WiFi router, and upload the data using a
variety of communication protocols without any additional hardware.
We chose the WiFi-integrated microcontroller, the ESP32 system-on-chip (SOC) from
Espressif Systems [
23
]. Although it has relatively low memory size and computing power
compared to modern CPUs, it offers excellent performance for an embedded microcontroller.
It is one of the workhorses for Internet of Things (IoT) applications [
24
]. The ESP32 in module
form provides dual 240 MHz 600 DMIPS CPUs, one for WiFi and the other for user firmware.
It implements the full TCP/IP stack, allowing it to work as a fully stand-alone WiFi node.
We used the Arduino integrated development environment (IDE) due to its ease of use and
extensive library of useful open-source libraries. The programming language is C++.
Because each HX711 ADC module has its own oscillator, the data acquisition rate
among the four sensors is not synchronized. We configure the HX711 modules to run 80 sps.
In initial testing, the actual sample period ranged between 11 ms and 12 ms, exceeding the
80 sps specification. To handle the distribution in sample rates, we developed a custom
driver firmware to asynchronously gather data from each sensor as soon as they were ready.
The firmware then reports the data from all four sensors synchronously at a fixed sampling
period not exceeding 12 ms.
We calculated and stored the scaling constant between the ADC reading and weight to
calibrate the weight sensor ADC signal chain. The scaling constant is obtained by dividing
the difference between the ADC readings with (gross reading) and without (tare reading) a
known weight placed on the chair by the known weight. After one-time calibration, the
object’s (net) weight equals the ADC reading with that object on the chair minus the ADC
reading with no object divided by the scaling constant. To confirm that the scaling constant
is consistent for each of the four independent weight sensors—the ADC signal chains—we
placed a known weight at different locations on the seat of the chair for different weight
distributions on the legs. Although the four sensors registered different readings as the
weight was shifted, the combined total weight remained the same within the resolution
limits, indicating consistent scaling constants.

Sensors 2024,24, 788 6 of 12
2.3. Software and System
Both a legacy menu-driven serial interface and a browser interface (Figure 3) were
developed as the ESP32 firmware to facilitate the data collection process. The browser
interface utilizes MQTT Subscription [
25
] to receive and execute commands sent from a
browser. Parameters can be set, including the sampling period (and therefore the sample
rate) and the trial period (number of seconds to collect the data). We recommend the trial
period to be ~40 s for 30CTS to allow ample time before and after the 30-s test, and ~20 s
for 5xSST since values above 14 sec are considered a positive indication of high fall risk.
Additional parameters such as subject number (or user ID) and trial classification (such as
subject physical state to be specified by the clinician) can be specified.
Sensors 2024, 24, x FOR PEER REVIEW 6 of 11
different readings as the weight was shifted, the combined total weight remained the same
within the resolution limits, indicating consistent scaling constants.
2.3. Software and System
Both a legacy menu-driven serial interface and a browser interface (Figure 3) were
developed as the ESP32 firmware to facilitate the data collection process. The browser
interface utilizes MQTT Subscription [25] to receive and execute commands sent from a
browser. Parameters can be set, including the sampling period (and therefore the sample
rate) and the trial period (number of seconds to collect the data). We recommend the trial
period to be ~40 s for 30CTS to allow ample time before and after the 30-s test, and ~20 s
for 5xSST since values above 14 sec are considered a positive indication of high fall risk.
Additional parameters such as subject number (or user ID) and trial classification (such as
subject physical state to be specified by the clinician) can be specified.
Before each trial run, consisting of one repetition of the 30CTS or 5xSST, the system
will determine the offset when no weight is placed on the chair and tare the system. After
tare offset, which takes less than one second, the user interface will instruct the clinician
to commence the trial.
Figure 3. Data collection HTML interface. The top bar (in red) shows the connection status to the
MQTT broker. The second bar (yellow) displays the MQTT messages from the microcontroller of
the sensor-connected chair. The rest of the page provides the user interface for changing the data
collection parameters and collecting data. Data collection is performed in a three-step process: (1)
specifying the user (subject), whether to classify the trial, and the data upload method; (2) waiting
Figure 3. Data collection HTML interface. The top bar (in red) shows the connection status to the
MQTT broker. The second bar (yellow) displays the MQTT messages from the microcontroller
of the sensor-connected chair. The rest of the page provides the user interface for changing the
data collection parameters and collecting data. Data collection is performed in a three-step process:
(1) specifying the user (subject), whether to classify the trial, and the data upload method; (2) waiting
for offset calibration (tare) to complete with no weight on the chair, and then start the trial; and
(3) classification of the trial, to be used for machine learning at a post-data collection stage, if classify
is turned on.
Before each trial run, consisting of one repetition of the 30CTS or 5xSST, the system
will determine the offset when no weight is placed on the chair and tare the system. After

Sensors 2024,24, 788 7 of 12
tare offset, which takes less than one second, the user interface will instruct the clinician to
commence the trial.
Data from the four sensor channels are continuously collected during the trial at the
specified sampling period. At the end of the trial, additional data processing is performed
to provide the time series total weight on the chair and calculate the sit-to-stand and
stand-to-sit transition times. Various peak detection and digital filtering algorithms were
attempted for transition detection. However, graphing typical sit-to-stand transition data
indicates a square wave or a truncated sine wave pattern; after a successful sit-to-stand
transition, the measured total weight moves quickly to and remains cleanly at the tare
value (zero reading) as the subject removes all weight from the chair. After a stand-to-sit
transition, the signal is not as clean and exhibits mechanical oscillation or bounce. Thus,
we developed a fast sit-to-stand transition algorithm using the mean of the total weight
data series as the crossing threshold. This fast and simple algorithm has been shown to
be very accurate. The number of sit-to-stand transitions can be reported immediately
to the clinician for redundant recording. Since the data are collected and stored, more
sophisticated data analysis can be performed offline.
The data from the sensor channels, in addition to the parameters or metadata, are
uploaded to a cloud-based server. We chose the JSON (JavaScript Object Notation) [
26
]
data format, using either MQTT Publish or HTTP POST protocols (Table 2). Due to the
limited memory of the ESP32 module and to ensure reliability, the metadata and data from
each channel are packetized and transmitted in separate publish or POST requests.
Table 2. Data format in JSON. The data are sent in separate MQTT Publish or HTTP POST requests.
The initial request contains parameters (metadata) regarding the trial, including the user (subject)
ID uid, classification cls if any, trial period Ttrl in ms, sample period Tsmp in ms, and the number of
channels, which is 4. This request is followed by data from the four channels, with the data channel
label (for example, right-back or RB) and the time series data_c_i (channel c, sample i). Each channel
has data length n=Ttrl/Tsmp. The separate uploads are merged into a single JSON statement.
Data Parameters
{“metadata”: {“user”: uid, “class”: cls, “T_trial”: Ttrl, “T_sample”: Tsmp, “N_dev”: 4}}
Data Set
{“data”: {“label”: “RB”, “data”: [data_0_1, . . ., data_0_n]}}
{“data”: {“label”: “RF”, “data”: [data_1_1, . . ., data_1_n]}}
{“data”: {“label”: “LF”, “data”: [data_2_1, . . ., data_2_n]}}
{“data”: {“label”: “LB”, “data”: [data_3_1, . . ., data_3_n]}}
Regarding data storage and retrieval, the system stores data from each trial on a
cloud-based server, and the stored data can be retrieved with HTML pages. The HTTP
interface also provides graphs of the separate weight channels, total weight, sit-to-stand
transitions, and rate of weight change.
The data collection server consists of the following modules: (1) HTML pages that
serve as the browser interface from the clinician to the ESP32 firmware, (2) the MQTT
Subscription and HTTP POST endpoint to receive the data from the ESP32 and store them
on the server, and (3) HTML pages to perform data access, processing, and graphing. The
browser interface converts clinician commands to MQTT Publish requests matching the
MQTT Subscription topics on the ESP32 firmware and displays messages from the ESP32
firmware by MQTT Subscriptions. The collected data are stored in text files whose names
consist of the user ID and the sequence number of that user ID in JSON format. The initial
data collection server software was developed in Python and ported to Node.js and PHP.
The various software ports give great flexibility in the different cloud hosting platforms
where the server software can be deployed.
The stored data can be retried or processed via additional HTML pages. We currently
implement the sit-to-stand transition times using the same thresholding algorithm for

Sensors 2024,24, 788 8 of 12
immediate feedback to the clinician and the rate of weight change (or weight velocity) on
the seat by calculating the numerical derivative of the time series weight.
3. Results
3.1. Functional Testing
We successfully obtained four independent data channels in a 12 ms sampling period
(~83 sps) for the right-back, right-front, left-front, and left-back legs of the chair. The data were
collected from a 53-year-old male with no lower-body physical impairment. We summed and
obtained the total weight from the four independent channels and determined the sit-to-stand
event times. We also calculated the rate of weight change, or weight velocity, which can
indicate the strength with which the subject leaves the chair.
In measuring independent strain-gauge weight sensors, we successfully mitigated the
weight sensors’ temperature drift by using unloaded temperature-compensating weight
sensors. We saw no noticeable temperature drift during the collection period. Also, we
determined that the calibration constants of the four independent signal chains were con-
sistent within the resolution limits. The firmware we developed was capable of collecting
data from each sensor at full speed asynchronously and providing synchronous readings
at the desired sample rate.
From the graph of various sit-to-stand transitions (Figure 4), we measured mechanical
oscillations between 4 Hz and 5 Hz. We also found that sit-to-stand transition occurred
over seven sample points or more (from 90% to 10% sitting weight). Therefore, an ~80 sps
ADC sample rate is adequate for this application. However, the recommended minimum
sampling rate for other body motion systems, such as ground reaction force plates, is
200 sps to capture jumping motion [
27
]. We will be able to use faster ADCs at a higher cost
if the need arises during analysis of the clinical data.
Sensors 2024, 24, x FOR PEER REVIEW 8 of 11
immediate feedback to the clinician and the rate of weight change (or weight velocity) on
the seat by calculating the numerical derivative of the time series weight.
3. Results
3.1. Functional Testing
We successfully obtained four independent data channels in a 12 ms sampling period
(~83 sps) for the right-back, right-front, left-front, and left-back legs of the chair. The data
were collected from a 53-year-old male with no lower-body physical impairment. We
summed and obtained the total weight from the four independent channels and deter-
mined the sit-to-stand event times. We also calculated the rate of weight change, or weight
velocity, which can indicate the strength with which the subject leaves the chair.
In measuring independent strain-gauge weight sensors, we successfully mitigated
the weight sensors’ temperature drift by using unloaded temperature-compensating
weight sensors. We saw no noticeable temperature drift during the collection period. Also,
we determined that the calibration constants of the four independent signal chains were
consistent within the resolution limits. The firmware we developed was capable of collect-
ing data from each sensor at full speed asynchronously and providing synchronous read-
ings at the desired sample rate.
From the graph of various sit-to-stand transitions (Figure 4), we measured mechani-
cal oscillations between 4 Hz and 5 Hz. We also found that sit-to-stand transition occurred
over seven sample points or more (from 90% to 10% siing weight). Therefore, an ~80 sps
ADC sample rate is adequate for this application. However, the recommended minimum
sampling rate for other body motion systems, such as ground reaction force plates, is 200
sps to capture jumping motion [27]. We will be able to use faster ADCs at a higher cost if
the need arises during analysis of the clinical data.
Figure 4. Graph of collected data. This graph shows the time series weights of the four independent
(right-back, right-front, left-front, and left-back) channels, the total weight, and the calculated times
of the sit-to-stand transitions (stands). The graph shows seven (7) stands in a 10 sec interval. There
are 4.5 Hz mechanical oscillations when the subject sits on the chair and no oscillations when the
subject stands.
Figure 4. Graph of collected data. This graph shows the time series weights of the four independent
(right-back, right-front, left-front, and left-back) channels, the total weight, and the calculated times
of the sit-to-stand transitions (stands). The graph shows seven (7) stands in a 10 sec interval. There
are 4.5 Hz mechanical oscillations when the subject sits on the chair and no oscillations when the
subject stands.

Sensors 2024,24, 788 9 of 12
3.2. Validation
We completed the clinical data collection process with the sensor-integrated chair
at the Chino Hills Premiere Surgery Center, a pain management clinic. We present the
validation results of the chair stand tests for 20 patients. The age of the patients ranged
from 26 to 84 years old (M = 61.5, SD = 13.7), and the weight of the patients ranged from
97.0 to 299.4 lb (M = 193.3, SD = 45.3). The manually measured 30CST ranged from 3 to 18
(M = 8.78, SD = 4.0), and the 5xSST ranged from 5 to 42 s (M = 20.6, SD = 9.9). In contrast,
the sensor recorded a 30CST range from 4 to 15 (M = 7.9, SD = 3.2). The sensor measuring
the 5xSST ranged from 12 s to the maximum of 30 s, at which time data collection was
terminated because anything above 14 s was already considered a failure; in total, 4 out of
the 20 patients’ data reached the cutoff (M = 19.9 and SD = 5.8).
To assess the performance of the sensor-integrated chair, we calculated the difference
between the manual and sensor measurements. The performance of the sensor-integrated
chair for the 30CST was good (M = 0.9, SD = 1.29, 95% CI = 0.65). For the 5xSST, after remov-
ing data that exceeded the 30-s cutoff, we obtained reasonable performance (M =
−
2.66 s,
SD = 2.27, 95% CI = 1.21).
4. Discussion
The sensor-integrated chair passed functional testing and captured four indepen-
dent sensor data channels, one under each leg of the chair. We fixed a board on the
bottom of the chair to provide structural rigidity, which did not interfere with data
collection. We plan for further design optimization by eliminating the board. The data
are captured at a sufficient rate for sit-and-stand application at 80 sps, although we can
move to a higher sampling rate if the conditions require. The temperature compensation
method to mitigate the effect of temperature drift using an additional sensor per channel
was successful.
The captured data are transmitted either as an HTTP POST or an MQTT Publish
request to the server, in JSON data format. The web-based server contains an HTML
interface to control the data collection process and retrieve or graph the collected data with
a user-friendly interface. Functional testing was completed, and the captured data were
verified to be accurate.
We analyzed preliminary clinical data from 20 patients at a pain-management clinic.
We found a good correlation between the manually collected and sensor-collected data for
the 30SST and a reasonable correlation for the 5xSST. We are continuing data collection,
and we will further analyze the clinically collected sit-to-stand timing and counts with
human-collected data and make improvements to the data processing algorithm.
With the time series weight data, we plan for future work with more sophisticated data
analysis than just the raw times or raw counts. For example, change in or lengthening of
the period between successive sit-to-stand transitions can indicate the level of fatigue and
estimate endurance as the subject performs the tests. The rate of change in weight or weight
velocity can indicate the force of the stands and assess the strength of the musculature
(Figure 5).
Although we use the time series total weight as the subject performs the chair stand
tests, we designed the system to capture four independent channels. For example, the
relative balance between the right and left leg can provide insight into left versus right
favoring in injury recovery. The shift in weight from the front to the back immediately
before standing can give insight into lower limb weakness and the subject rocking to build
momentum to assist in standing.

Sensors 2024,24, 788 10 of 12
Sensors 2024, 24, x FOR PEER REVIEW 10 of 11
Figure 5. Graph of weight velocity. This graph shows the total weight, the weight rate of change
(deriv.), and the sit-to-stand transitions (stands) times. The magnitude of the weight velocity indicated
by the y-axis location of the dots (1700~2100 lb/s) can provide an estimate of the strength of the stand.
5. Conclusions
This paper presents a sensor-integrated chair for the automatic data capture of the
5xSST and the 30CST which aims to reduce the administering clinicians’ workload. This
system is designed so that sensors are integrated into the chair, rather than worn by the
patient, to minimize obtrusiveness in clinical data collection. Because no sensing element
needs to be set up external to the chair, setup is greatly simplified. Additionally, the sys-
tem transmits data via wireless communications and is powered by a baery pack to avoid
tethering and maximize portability. A set of web pages was developed to provide a con-
venient user interface to run tests and capture data.
The sensor-integrated chair utilized commercially available components to ensure
wide availability, low cost, and ease of porting into other chairs. We believe these choices
facilitate the integration of the design into existing chairs that function well in various
facilities and are preferred by different clinicians. After successful functional testing and
clinical validation, we believe that this research could be beneficial in assessing lower-
body strength and fall risk in older adults.
Author Contributions: Conceptualization, A.W.L., M.S.L., and H.-J.J.Y.; methodology, M.S.L. and
A.W.L.; software, D.P.Y. and H.-J.J.Y.; validation, A.W.L. and M.S.L.; writing—original draft prepa-
ration, A.W.L. and D.P.Y.; writing—review and editing, H.-J.J.Y.; supervision, H.-J.J.Y.; project ad-
ministration, A.W.L. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted in accordance with the Declara-
tion of Helsinki and approved by the Institutional Review Board (or Ethics Commiee) of Azusa
Pacific University (protocol code 23-130-MED approved 12 October 2023).
Informed Consent Statement: Not applicable—the study at this point does not involve human trials.
Data Availability Statement: The data presented in this study are available from the corresponding
author upon request.
Conflicts of Interest: The authors declare no conflicts of interest.
Figure 5. Graph of weight velocity. This graph shows the total weight, the weight rate of change
(deriv.), and the sit-to-stand transitions (stands) times. The magnitude of the weight velocity indicated
by the y-axis location of the dots (1700~2100 lb/s) can provide an estimate of the strength of the stand.
5. Conclusions
This paper presents a sensor-integrated chair for the automatic data capture of the
5xSST and the 30CST which aims to reduce the administering clinicians’ workload. This
system is designed so that sensors are integrated into the chair, rather than worn by the
patient, to minimize obtrusiveness in clinical data collection. Because no sensing element
needs to be set up external to the chair, setup is greatly simplified. Additionally, the
system transmits data via wireless communications and is powered by a battery pack to
avoid tethering and maximize portability. A set of web pages was developed to provide a
convenient user interface to run tests and capture data.
The sensor-integrated chair utilized commercially available components to ensure
wide availability, low cost, and ease of porting into other chairs. We believe these choices
facilitate the integration of the design into existing chairs that function well in various
facilities and are preferred by different clinicians. After successful functional testing and
clinical validation, we believe that this research could be beneficial in assessing lower-body
strength and fall risk in older adults.
Author Contributions: Conceptualization, A.W.L., M.S.L. and H.-J.J.Y.; methodology, M.S.L. and
A.W.L.; software, D.P.Y. and H.-J.J.Y.; validation, A.W.L. and M.S.L.; writing—original draft prepara-
tion, A.W.L. and D.P.Y.; writing—review and editing, H.-J.J.Y.; supervision, H.-J.J.Y.; project adminis-
tration, A.W.L. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Azusa Pacific
University (protocol code 23-130-MED approved 12 October 2023).
Informed Consent Statement: Not applicable—the study at this point does not involve human trials.
Data Availability Statement: The data presented in this study are available from the corresponding
author upon request.

Sensors 2024,24, 788 11 of 12
Conflicts of Interest: The authors declare no conflicts of interest.
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