Measuring Fall Risk Using the Internet-of-Things Chair

Alexander W. Lee

, Melissa S. Lee

, Chelsea Yeh

and Kyle Yeh

Chino Premier Surgery Center, Chino, CA 91710, U.S.A.

Yale University, New Haven, CT, 06520, U.S.A.

Brown University, Providence, RI, 02912, U.S.A.

Keywords: Lower Extremity Strength, Leg Strength, Falls, 30 Second Chair Stand Test, 5 Times Sit-to-Stand Test,

Internet of Things, Automatic Chair, Wireless Chair, Clinical Study.

Abstract: Falls are one of the leading causes of injuries and deaths for U.S. adults ages 65 and older. People can fall

because of imbalance and leg weakness. Fall risks are evaluated by standardized tests, including the 30-

Second Chair Stand Test (30CST) and 5x Sit-to-Stand Test (5xSST). These tests are conducted by visual

observation of the participant and manual counting, which can be inaccurate and tedious. This study clinically

tested an Internet of Things Chair (IoT) on how well it performed on the 30CST and 5xSST. A clinical study

was performed on 224 participants. The results of the IoT Chair were found to be similar to the traditional,

visually observed method. The IoT Chair required less manual work and provided information that was not

obtainable with the observer method. The IoT Chair was able to calculate the weight exerted on the individual

chair legs, rate of weight change, lag time between each sit-stand cycle, the amount of time spent standing

during each cycle, and the amount of time each sit-stand cycle required. This additional information can allow

for a better understanding of a person's leg strength and improves the prediction for falls, which can save lives

and lower healthcare costs.

1 INTRODUCTION

Falls in adults over 65 years old are the leading cause

of injury-related deaths in the United States (CDC,

2020). The rate of age-adjusted deaths due to falls has

increased by 41% from 2012 to 2022 (CDC, 2024). In

a 2016 National Study of Long-Term Care Providers

conducted by the National Center for Health

Statistics, they found that 22% of adults living in an

assisted-living facility or residential care

communities had fallen in the prior 90 days. Of the

individuals who fell, 19% had to go to the hospital,

and 15% had sustained injuries (Harris-Kojetin &

Sengupta, 2018).

Impairments in vision (Jin et al., 2024), hearing

(Riska et al., 2021), muscle strength (Rodrigues et al.,

2023), reflexes (Marigold et al., 2005), cognition

(Chantanachai et al., 2021), balance (Papalia et al.,

2020), side effects of medications (Hartikainen et al.,

https://orcid.org/0000-0001-7809-8181

https://orcid.org/0000-0002-3975-821X

https://orcid.org/0000-0002-0502-8235

https://orcid.org/0000-0002-2979-7143

2007), and environmental hazards can all cause falls

(Campani et al., 2020), (National Institute on Aging,

2022). Preventing these falls is critical in keeping

older adults healthy and active. The 30-Second Chair

Stand Test (30CST) (Jones et al., 1999) (Chan-Mei

Ho-Henriksson et al., 2024), and 5-Time Sit-to-Stand

Test (5xSST) (Muñoz-Bermejo et al., 2021), (Albalwi

& Alharbi, 2023) are well-established tests that

objectively evaluate lower extremity strength,

balance, and fall risks. In the 30CST, patients are

evaluated on how many times they can change from

sitting to standing in 30 seconds, with the observer

visually counting. Their arms are crossed across the

chest and cannot be used during the test. If the person

performs less than what is established for their age

group and gender, then they are at higher risk for falls

(CDC, 2017). The 5xSST is performed in the same

manner as the 30CST with the participant's arms

crossed at the chest and cannot be used during the test.

For the 5xSST, the longer the person takes to

Lee, A. W., Lee, M. S., Yeh, C. and Yeh, K.

Measuring Fall Risk Using the Inter net-of-Things Chair.

DOI: 10.5220/0013404500003944

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security (IoTBDS 2025), pages 353-360

ISBN: 978-989-758-750-4; ISSN: 2184-4976

353

complete the test, the higher their fall risk (5 Times

Sit to Stand Test (FTSST), n.d.). There are no

standardized cut-off ranges published by the Center

for Disease Control (CDC) for the 5xSST. Buatois et

al. studied 1,618 community-dwelling people over

the age of 65 and found that if they took longer than

15 seconds to complete their 5xSST, their risk of falls

would double. This study is cited in many tests as the

cut-off number (Buatois et al., 2010).

Experimental studies have evaluated the use of

electronics applied to the sit-to-stand test. For

instance, a study by Collado-Mateo et al. used an

automatic chronometer developed by Chronopic to

evaluate the patient during the 30CST trial (Collado-

Mateo et al., 2019). The participant had to wear a vest

with metallic tape. That metallic tape would have to

come into contact with the Chronopic device attached

to the chair seat. The Chronopic device could detect

the time the tape was in contact, thereby establishing

the amount of time the patient was sitting or standing.

If the person was not sitting correctly and the metallic

tape on the vest did not come into contact with the

Chronopic device, the change to the sitting position

would not be recorded.

Yeh et. al. (Yeh, C. et al., 2022) developed an

Internet of Things (IoT) Chair designed to evaluate

patient movement from the chair. A pressure pad

placed on the chair could detect the movement of

patients changing from a sitting position on the chair

to a standing position by detecting pressure and

motion changes. This data would then be transmitted

to a cellular phone app. A significant limitation of that

study was that the chair was not tested for accuracy.

Another problem was that the pressure pad on the

chair could shift with use, decreasing the accuracy of

the measurements. The shifting pad could also cause

patients to slip out of the chair and injure themselves.

The chair only detected whether or not the person was

sitting on the chair. No sensors were detecting the

amount of pressure placed on the chair.

Lee et al. (Lee et al., 2024) improved upon Yeh et

al.'s chair and developed a novel Internet-of-Things

(IoT) Chair utilizing built-in sensors to evaluate fall

risks in adults. This type of sensor technology has

been previously applied to other medical devices (Lee

et al., 2023), (Lee & Yeh, 2022), (Yeh, C. et al.,

2022), (Yeh, C. et al., 2022), (Yeh. K. et al., 2021)

including the measurement of human body movement

(Yeh H.J. et al., 2020), and to computer networking

(Yeh. H.-J. et al., 2019).

Lee et al. designed the entire system as a single

unit so that no sensors were attached to the patients

and no sensors needed to be set up external to the

chair. The participant also did not need to sit in a

particular position in order for the chair to measure

the amount of force exerted on the chair. There were

built-in sensors in the chair which transmitted the data

to a cloud-based server. The chair was designed to

perform the Fullerton Functional Tests, which

included the 30CST and the 5xSST. The technical

aspects of the devices used and their integration into

the chair are detailed in the paper by Lee et al.

This paper examined how well Lee et al.'s IoT

Chair (Lee et al., 2024) performed in test participants

with both the 30CST and the 5xSST. We chose to test

the chair on the 30CST and 5xSST because studies

have shown that they are highly reliable across

different adult populations (Figueiredo et al., 2021),

(Gill et al., 2012), (Goldberg et al., 2012). Prior to

conducting the clinical trials, we received IRB

approval #23-130 from Azusa Pacific University. A

total of 224 people participated in the study. Testing

was conducted over a period of 12 months, from

November 2023 through October 2024.

2 MATERIALS AND METHOD

The use of strain-gauge force sensors for the

measurement of dynamic human weight distribution

is novel and presents significant advantages over

other sensing technologies. Strain gauges are

commonly used to measure static human weight

distribution and are the sensing element in many

commercial and most electronic consumer scales.

Because of their widespread use, economies of scale

in their design and manufacturing have been

achieved, leading to broad availability and low cost.

Designing with these components leads to decreased

end-user costs that offset high equipment costs that

beset the healthcare industry.

The use of weight sensors that are mounted on the

chair provides significant improvements over

previous automatic chair-stand measurement

apparatus. Many of the previous devices, such as

accelerometers or contact sensors, require the

attachment of sensors on the body of the subject

(Cobo et al., 2020), (Hellmers et al., 2019), (Millor et

al., 2013). This can lead to significant complications

during the trial process, increasing preparation time

for each patient and reducing participation.

Additionally, other previous devices using distance

sensors (Takeshima et al., 2019), (Cobo et al., 2020),

(José Gonçalves et al., 2015) require a more

complicated setup, which limits their portability.

Two designs were created for the placement of the

strain gauge weight sensors. Four weight sensors

were integrated into the four corners of common

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

354

chairs. For human body scale applications, the four

sensors are typically wired into a single Whetstone

bridge that provides a single reading, which is the

sum of the four weights. In this application, the four

sensors were wired into four separate bridges to

provide four separate channels – right front, right

back, left front, and left back.

For the initial design, the four strain gauges were

mounted on the frame of the chair directly under the

four corners of the seat of the chair. This design had

issues in securing the seat on the chair while

providing accurate measurements. A second design

solved the issue by mounting the sensors under the

four legs of the chair. Since the diameters of typical

chair legs are smaller than the sensors, the chair is

first mounted on a rigid board, and the sensors are

secured to the bottom of the board directly under the

legs (Figure 1).

Figure 1: This picture shows the chair that was designed for

this clinical study.

The completed apparatus directly measures

dynamic weight distribution on the four separate chair

legs as the subject performs each trial. This is not

available with other sensing methodologies. Distance

sensors and accelerometers will be able to provide

velocity data but not measure the weight distribution.

Another improvement over previous work is the

use of a novel network infrastructure that uses cloud

computing. This system architecture provides user-

friendly control and data flow, storage, and retrieval

during data collection and processing. The benefit of

this architecture is that the data collection is highly

scalable and portable; because existing and popular

network protocols are used, migrating or duplicating

the system to different or multiple servers is

extremely simple – often with the simple copying of

the relevant scripts with little or no setup or

provisioning. This design greatly simplifies the

deployment of new systems.

Commands to the system (inputting the patient

number or id, tuning the data collection parameters

such as the collection period or sample rate, and

starting the data collection after the patient is ready)

are done on a web interface that runs on any browser

(Figure 2). The browser runs standard HTML

(hypertext markup language) and JavaScript. The

commands issued from the browser to the chair (red

arrows) and the responses and messages from the

chair to the browser (orange arrows) utilize MQTT

(message queuing telemetry transport), which is the

de facto standard for IoT devices. This allows the user

interface to run on virtually any device – personal

computers, laptops, cell phones, etc.

After data from a trial has been collected, it is sent

to the cloud-based server via a standard HTML POST

request (blue arrow). The server runs standard PHP

(hypertext processor) to receive, store, and provide

access to the patient trial data. PHP is supported on

virtually all servers without customization, which

provides excellent system portability. The stored data

on the server can be accessed from the browser

interface (green arrow) if the proper permission is

granted. This is important for the confidentiality of

the patient data. The stored data can be graphed, and

various statistics, such as the times of sit-to-stand

transitions, can be computed.

Figure 2: This diagram displays the data and command flow

for the IoT Chair.

We recruited male and female adults ages 18

years and older. Any participants in a wheelchair or

regularly used assistive devices, including canes and

walkers, were excluded from this study. If they had

good leg strength and only occasionally needed the

use of canes or walkers, they were included in this

study. If the participant appeared fatigued, struggling,

or imbalanced, the test was immediately stopped to

prevent a fall. If needed, a walker was also placed in

Measuring Fall Risk Using the Internet-of-Things Chair

355

front of the chair for the participants to hold onto if

they felt fatigued or might fall. Once the participant

required the assistance of the walker, the trial was

immediately ended. The participants could also

voluntarily end the study if they felt tired or unable to

continue by verbally informing us or by raising either

their right or left hand.

A questionnaire was given to all participants, who

recorded their age, use of the assistive walking

devices, history of falls, and any musculoskeletal

pain. Vital signs, including height, weight, body mass

index (BMI), blood pressure, and heart rate, were

measured in all participants.

Participants were given instructions on the 30CST

and the 5xSST. The participant needed to have their

feet flat on the floor, sit in the middle of the chair, and

have their hands on the opposite shoulder with their

arms against the chest. When instructed to “Go,” the

participant needed to go from sitting to a full standing

position and then sit back down again. Data collection

was initiated by clicking a “Start” button on the

custom-designed, secure IoT Chair browser (website)

hosted by the web server. The IoT Chair

programming automatically recorded the number of

sit-stand-sit cycles in 30 seconds. For the 30CST test,

the participants needed to repeat this cycle as many

times as they could in 30 seconds. During the 30CST,

the time required to do the first five sit-stand-sit

cycles was used to record the 5xSST. In essence, the

30 CSST and 5xSST tests were done simultaneously

for efficiency and participant convenience. Besides

automatic recording by the IoT Chair programming,

we manually recorded how long it took to do the first

five sit-stand cycles (5xSST) and the number of sit-

stand cycles completed in 30 seconds (30CST).

3 RESULTS

There were 224 participants in this clinical study.

Fifty-six participants occasionally used assistive

walking devices such as walkers and canes. Seventy-

three participants had fallen within the past year. Two

hundred and eight participants described either some

joint or back pain.

The IoT Chair programming default setting (on

the browser) allowed 30 seconds to complete the

30CST and 5SST tests. Thirty seconds was chosen

because that is the time needed for the 30CST. The

slowest 5xSST completion time cut-off is 10.8

seconds (for people 70 years and above). That means

anyone taking longer than 10.8 seconds is considered

to have failed the 5xSST. Thirty seconds would be

more than sufficient time to test the 5xSST. Test

Figure 3 shows the results of a typical 30CST and

5xSST trial. An important note was that the IoT Chair

could automatically record, with a precision of 12.5

milliseconds, how long it took for the person to do 5x

sit-stand cycles. In contrast, the human observer

recordings only measured the 5xSST to the seconds.

Figure 3: A typical example of a completed IoT Chair

clinical trial, with the orange dot recording a "stand" and

the green dot recording a "sit". The blue graph showed the

patient's total weight sitting (140 lb) and at full standing (0

lb). The total time elapsed is 30 seconds (i.e. 30,000 ms),

and 8 sit-stand cycles were completed for the 30 CST. The

cursor is on the fifth completed sit-stand cycle, displaying

the amount of time (18.55 seconds) needed to complete the

5xSST test.

In the 30CST trials, the mean in observer-

recorded sit-stand cycles was 7.72 cycles (median

7.0, SD 3.74, 95% CI 0.53) compared to the mean IoT

Chair-recorded sit-stand cycles was 6.93 cycles

(median 7.0, SD 3.76, 95% CI 0.56). This data is

displayed in a box plot analysis in Figure 4, showing

that the two different methods have overlapping 50%

quartiles.

Figure 4: The 30CST Observer (visually) recorded method

and IoT Chair (automatic) recorded method have an

overlapping 50% quartile range.

For the 5xSST, the mean time to complete the test

recorded by the observer was 19.83 seconds (median

18.03, SD 8.83, 95% CI 18.03) compared to the IoT

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

356

Chair mean time of 21.33 seconds (median 20.95, SD

6.41, 95% CI 0.98). This data is displayed on a box

plot diagram in Figure 5, showing that the two

different measuring methods are not statistically

different.

Figure 5: The Observer (visually) recorded method and IoT

Chair (automatic) recorded method for the 5xSST have

medians within the comparison 50% quartile box plot,

meaning the difference between these two methods is not

statistically significant. It is important to note that the IoT

Chair was programmed to not record after 30 seconds,

affecting the difference in the IoT Chair vs Observer box

plot range.

4 DISCUSSION

By using the Box Plot analysis, the 50% quartiles for

the observer method and the IoT Chair overlapped on

both the 30CST and 5xSST, meaning that the IoT

chair results were not statistically different compared

to the observer method. That means both methods had

similar results, and the IoT Chair results were just as

reliable as the standard observer method for the

30CST and 5xSST.

Being able to analyze each sit-stand cycle and its

characteristics is very useful. For instance, the IoT

Chair browser displays the sit-stand cycles as a graph,

showing time on the X-axis and weight on the Y-axis.

Thus, the IoT Chair programming can calculate how

long each sit-stand cycle takes. Patients who are

slower with the first or last sit-stand cycle may

indicate leg weakness. Initially, these participants

may need to build momentum going from sitting to

standing. They may initially sit longer or stand

longer. Figure 6 shows an example of a person with

difficulty in the first sit-stand cycle, with a pause in

the standing phase. At the end of the trial, if the

participants are slower in a sit-stand cycle, this may

also indicate increasing fatigue (Figure 7). Increasing

fatigue would be a risk for falls. Again, this nuanced

data would not be recorded via the human observer

method.

Figure 6: In this trial, the graph clearly depicted the initially

slower first sit-stand cycle compared to the other sit-stand-

sit cycles, with a pause in the standing phase.

Figure 7: This graph shows a prolonged sit-stand cycle near

the end, at about 20 seconds.

Since the chair measures the weight placed on

each chair leg, we can make inferences about a

participant's balance issues. For instance, in Figure 8,

the person consistently placed higher pressure on the

left front and left back chair leg. This difference in

chair leg pressure may indicate that the person has a

right-sided weakness and favors his left leg. Some

possibilities for favoring one side may be due to a

history of stroke, vestibular, or balance issues. This

type of information is not available with the

traditional observer counting method. A physician or

physical therapist can use this additional information

to diagnose leg weakness or imbalance better and

improve patient treatments and outcomes.

Measuring Fall Risk Using the Internet-of-Things Chair

357

Figure 8: The different color lines indicate the weight

exerted on each chair leg. As the legend describes RB (blue

line)= weight on right back IoT Chair leg, RF (orange line)=

weight on right front leg, LF (green line)= weight on left

front leg, LB (red line)= weight on left back leg, Total

(purple line)= weight on all four legs.

As seen in Figure 9, the IoT Chair also calculated

and displayed the rate of change in weight or weight

velocity of each sit-stand and stand-sit curve. The

weight velocity measures the weight change placed

on the chair over time, which can be a proxy for how

fast the person goes from sitting to standing and from

standing to sitting. These values can help predict if a

person is at a greater risk of falling. For instance,

participants with a faster change in weight exerted on

the chair indicate they can sit or stand quickly due to

greater lower extremity strength. A slower change in

weight exerted on the chair indicates a slower speed

in sitting or standing, suggesting that the person may

be weaker or have more instability and, thus, are at a

greater risk for falls. Figure 9 shows an overall

decreasing rate of weight change amplitude over

subsequent sit-stand cycles starting at about the

halfway point (15 seconds) of the 30CST. This

decreasing rate of weight change can indicate that the

patient has increasing leg muscle weakness and may

be at higher risk of falls compared to a person with a

consistent rate of weight change amplitudes

throughout the trial.

Figure 9: This graph shows a 30-CST trial with the rate of

weight change (orange line) or weight change rate.

The graphs in the IoT Chair sit-to-stand trials

(Figure 10) even have an interesting pattern

reminiscent of an electrocardiogram (EKG) of the

heart. In reading an EKG, the physician looks at the

rhythm, rate, and type of electrical pattern peaks and

troughs to determine different heart conditions

(Hockstad, n.d.). The IoT Chair data can be viewed

similarly. Each person has a different rhythm, rate,

and pattern of sitting and standing. Future studies can

see if the IoT Chair graph patterns can be used to help

determine the patient's leg strength and fall risks.

Figure 10: This figure shows the pattern of a participant

performing 15 total sit-stand cycles (sit-stand-sit again) and

shows a particular, repetitive pattern.

More clinical tests can be done on the IoT Chair

to gather data that can be applied to a broader patient

population, including patients with certain comorbid

conditions such as pain, obesity, heart disease, and

lung disease. Machine learning has been widely used

in various applications (Yeh & Khan, 2022) and can

be applied to the IoT Chair data. Machine learning

can help determine normal or abnormal rates of

weight changes placed on the chair and how much

uneven pressure on the four different chair legs can

indicate leg weakness. Algorithms can also be

developed to determine how much standing or sitting

time is normal or abnormal.

5 CONCLUSION

Clinical testing for the 30CST and the 5xSST on the

IoT Chair developed by Lee et al. showed that the

chair not only provided automatic data collection and

freed up the work of the observer, but the chair was

easy to use for both observer and participant, just like

a manual chair. Furthermore, the measurements were

accurate and reliable, as shown by the box plot

analysis. The chair also produced additional data that

was unavailable using the manual observer method.

The IoT Chair displayed the completion time for each

IoTBDS 2025 - 10th International Conference on Internet of Things, Big Data and Security

358

sit-stand cycle, the amount of time spent sitting and

standing, the amount of weight placed on each chair

leg, and the rate of weight change placed on the chair.

This additional data, along with the traditional

measurements of time and the number of sit-stand

cycles, can more precisely help doctors give earlier

and better predictions of fall risks and leg weakness

in patients. In turn, preventing falls would improve

quality of life, increase life expectancy in older

Americans, and save on the enormous annual

healthcare costs.

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