Comprehensive Musculoskeletal Care Platform Enabling At-home

Patient Care

Ghazal Ershadi

1 a

, Serena Hughes

2 b

, Raja Sundaram

3 c

and Majid Sarrafzadeh

1 d

Computer Science Department, University of California Los Angeles, Los Angeles, U.S.A.

Bioinformatics Department, University of California Los Angeles, Los Angeles, U.S.A.

CEO, Plethy, Inc, San Jose, U.S.A.

Keywords:

Wearable Technology, Musculoskeletal Disorders, Rehabilitation Exercises, Remote Patient Monitoring.

Abstract:

Pain and stiffness in the musculoskeletal system cause limited range of motion and loss of mobility. Reme-

diation includes home care programs, physical therapy, medication, and if necessary, surgery. Much of the

recovery occurs at home. Active engagement by patients in their care program is crucial for successful clini-

cal recovery. Recovering from a partial or total joint replacement requires active participation in an exercise

program to help minimize swelling and improve motion and strength. On the other hand, non-surgical mus-

culoskeletal indications, also require a personalized exercise plan for recovery. Patients need support with

following their care at home. Remote care programs and monitoring offer convenient, safe, and time and

cost-efﬁcient care, including at-home physical therapy programs, and have broad clinical scope. We studied

one such program. This paper reports the results of a platform that digitizes care programs for all muscu-

loskeletal conditions, engages patients at their convenience, while providing visibility to recovery progress via

patient-reported and sensor-generated data. We introduce Plethy Recupe which is a comprehensive platform

for musculoskeletal care with a joint motion sensor, intuitive app, and intelligent clinical dashboard. Six exer-

cises were selected from this platform and 10 individuals were asked to perform the exercises to evaluate the

accuracy of the range of motion (ROM) and complete a questionnaire on the usability of the solution.

1 INTRODUCTION

The rehabilitation process is crucial for recovery af-

ter major surgical procedures and for the manage-

ment of chronic conditions. Rehabilitation exercises

may also be performed prior to surgery. This is

known as pre-habilitation and is also thought to effec-

tively improve postoperative functional performance

and strength gain (Topp et al., 2009). Typically, the

rehabilitation process involves many in-person, one-

on-one sessions with a physical therapist. In between

these regular sessions, the patient will be expected

to repeat rehabilitation exercises at home, unsuper-

vised. In order to get further feedback on their per-

formance, the patient will have to book another ap-

pointment with his/her physical therapist. This takes

up the limited time of the physical therapist, requires

means to travel to and from their ofﬁce, and can be

https://orcid.org/0000-0003-0174-4266

https://orcid.org/0000-0001-9207-1452

https://orcid.org/0000-0001-5825-0836

https://orcid.org/0000-0001-8407-8689

costly. This process is thus time and labor-intensive.

It is estimated that as of 2019, there were 2.4 billion

people globally with a condition that would beneﬁt

from rehabilitation and that the need for rehabilitation

worldwide will increase over time (Cieza et al., 2020).

Remote monitoring of physical training sessions can

improve both the efﬁciency and effectiveness of this

care.

Remote monitoring is convenient, safe, and time

and cost-efﬁcient. (Tack, 2021) In addition, it offers

improvements to the quality of care that patients re-

ceive. Patients can be monitored more frequently, can

receive objective, real-time feedback on their perfor-

mance, and will have a record of consistent, thorough

data from their exercise sessions. The solution we

choose to study was Plethy Recupe, a comprehensive

system for remote care focused on all musculoskeletal

conditions. The system consists of a wearable sensor,

an intuitive phone application, and a dashboard for the

healthcare provider.

The sensor is simple, small, and versatile. While

other remote patient monitoring systems achieve ﬁner

190

Ershadi, G., Hughes, S., Sundaram, R. and Sarrafzadeh, M.

Comprehensive Musculoskeletal Care Platform Enabling At-home Patient Care.

DOI: 10.5220/0010868300003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 2: HUCAPP, pages

190-196

ISBN: 978-989-758-555-5; ISSN: 2184-4321

granularity through a more complex, multi-sensor

system (Lorussi et al., 2018) (Ramkumar et al., 2019),

the singular Plethy Recupe sensor prioritizes ease of

use by patients at home. This simple setup reduces

inaccuracies due to user error and is neither intimidat-

ing nor discouraging to a broad spectrum of patients.

Other work in the ﬁeld of remote rehabilitation has

also used the entire phone as the sensor itself (Cho-

miak et al., 2019). The Plethy Recupe sensor is only a

little larger than a quarter, making it easy to wear and

unobstructed while exercising. Other research spe-

cializing in remote monitoring of knees has also used

sensors that are embedded in a wearable knee sleeve

(Ramkumar et al., 2019). By using a sensor that can

be attached to different locations, the Plethy Recupe

solution can be used for various joints. In addition to

the knee, the sensor can be used for the ankle, hip,

lower back, shoulder, elbow, and wrist, etc. This also

allows for future expansion of the Plethy Recupe sys-

tem to other regions, surgeries, and conditions.

The phone application is compatible with both

iOS and Android, making Plethy Recupe accessible

to nearly any patient with a smart device. The app is

designed to be straightforward and helpful for patient

motivation and recovery. It includes video demon-

strations of how to perform exercises, training videos

on sensor placement, and live visualization of the pa-

tient’s range of motion. In addition, it counts success-

ful repetitions of an exercise in real-time and gener-

ates range of motion measurements. Upon start-up, it

surveys patient symptoms, level of pain, reminds pa-

tients when to exercise, when to take medications, and

of upcoming surgery-related events. All of this data

is available to the clinician as well.

The remainder of the paper is structured as fol-

lows. Section 2 provides the requisite background

on importance of musculoskeletal conditions. Sec-

tion 3 discusses the system speciﬁcation of the Plethy

Recupe, including hardware implementation, exercise

recognition algorithm, mobile application, and clin-

ical dashboard. Section 4 introduces the six exer-

cises that are evaluated in the experiments. Section 5

presents the result of the experiments and preliminary

evaluation of the Plethy Recupe. Finally, we conclude

and propose our future research direction in Section 6.

2 CLINICAL BACKGROUND

The World Health Organization names musculoskele-

tal conditions as the leading contributor to disabil-

ity worldwide. There are over 150 diagnoses which

qualify as musculoskeletal, affecting the joints, bones,

muscles, spine, and connective tissue. (World Health

Organization, 2021) These conditions are highly

prevalent and cost our society a large sum in both

treatment costs, lower quality of life, and loss of in-

come due to inability to work. In the United States

in 2018, one out of two adults were diagnosed with a

musculoskeletal condition, totaling $124 million peo-

ple (Bone and Joint Initiative, 2018). Through di-

rect and indirect costs, musculoskeletal disorders cost

the United States $150 billion and are expected to

increase by $73 between 2014 - 2024 (Optum, Inc.,

2019). As such, musculoskeletal pre-rehabilitation,

treatment, and rehabilitation are of high importance

to the well-being of society and economy.

In this study, the focus is on musculoskeletal exer-

cises intended to strengthen the knee and ankle joints,

as arthritis in the knee is the most common muscu-

loskeletal indication. Each exercise in the app in-

cludes a brief description of how to perform it along

with a video demonstration. The six exercises tested

are Long Arc Knee Extension, Heel Slides with Quad

Sets, Toe Raises, Reverse Toe Raises, Ankle Pump,

and Seated Ankle Pump. The ﬁrst two exercises tar-

get muscles that support the knee and the remaining

four strengthen the ankle. For example, the Long Arc

Knee Extension exercise strengthens the quadriceps

muscles which support the range of mobility of the

knee.

It is estimated that in 2010, 1.52% of the United

States population underwent total knee replacement

surgery. This is approximately 4.7 million individ-

uals (3.0 million women and 1.7 million men) who

had this procedure done. These numbers only account

for those who had total knee replacement surgery.

In addition, there are millions of people with partial

surgeries or musculoskeletal conditions that arose for

other reasons such as overuse or traumatic impact.

Musculoskeletal problems in the foot and ankle are

experienced by approximately 1 out of every 5 peo-

ple. Further, it has been found that the number of total

ankle replacement surgeries performed in the United

States increased by 261% from 2005 to 2014. In this

same time period, ankle fracture surgeries increased

by 82% (Burton et al., 2020). As such, the develop-

ment of a rehabilitation program with exercises for

knee and ankle recovery like the ones tested here is

crucial and increasingly critical.

3 MATERIAL AND METHODS

The Plethy Recupe Solution is composed of three

main components: a wearable sensor, a smartphone

application, and a clinical dashboard as in ﬁgure 1.

The sensor unit is designed to measure the range of

Comprehensive Musculoskeletal Care Platform Enabling At-home Patient Care

191

Figure 1: Components of the platform: a sensor unit, linked

smartphone application, and clinic dashboard.

joint angle movement using an IMU. It is mounted on

a casing that’s easy to wear and attached to any part

of the body using a reusable adhesive. The sensor unit

is programmed with different algorithms to count the

number of successful repetitions along with the range

of movement angle for each exercise. The readings

of the sensor unit are transmitted to an application

via Bluetooth. The application stores the recordings

in the cloud-based database along with the number

of successful repetitions. It also displays the num-

ber of care sessions including the home exercise pro-

gram. The dashboard enables the healthcare provider

to monitor the patient’s functionality and progress

throughout the entire care journey ( pre-surgery to

post-surgery) or the non-surgical treatment process.

3.1 Hardware

An Inertial Measurement Unit (IMU) has been uti-

lized to measure the joint angle and count the num-

ber of repetitions per exercise. It is a 9-axis motion

tracking device that incorporates a triple-axis gyro-

scope, a triple-axis accelerometer, and a triple-axis

magnetometer all integrated into a Quad Flat No-

leads (QFN) package. The IMU precisely offers full

9-axis Motion Fusion performance with its dedicated

C sensor bus. It involves a total of nine 16-bit

analog-to-digital converters (ADCs) for 3-axis gyro-

scope, 3-axis accelerometer, and 3-axis magnetome-

ter output digitization. In this work, three vibratory

MEMS rate gyroscopes are used, which detect rota-

tion around the 3-axis, and acceleration around a par-

ticular axis that causes displacement on the associ-

ated proof mass with a measurement range of up to ±

16g. The Coriolis effect (McDonald, 1952) induces

a vibration detected by a capacitive pickoff while the

gyroscope is rotated along either of the axes. Then,

a voltage that is proportional to the angular velocity

is generated. This voltage is digitized using 16-bit

ADCs with a range of up to ± 2000 degrees per sec-

ond (dps).(Invensense TDK, 2021)

The IMU data is transmitted to the phone applica-

tion via Bluetooth 5 connection. The Bluetooth mod-

ule communicates with the IMU via I

C bus at 400

kHz frequency. Low energy consumption, small area

requirements (a 6.5mm × 6.5mm package), and sim-

pliﬁed development costs of this module make it per-

fectly ﬁtted for wearable devices.(Silicon Labs, 2017)

The system chip is equipped with a square TACT

switch with no stem on it (ALPS, 2021) and a

through-hole mount battery holder that provides solid

electrical contact for a lithium coin battery. They are

all housed in an 18mmDia × 9mmH Recupe package.

The housing consists of a bottom and a cap which are

detachable in case the battery has to be replaced and

they can be coupled afterward. With a little press on

the housing, the sensor starts connecting to the paired

smartphone with a blinking blue light. The sensor is

automatically turned off after 15 seconds if it cannot

ﬁnd the paired phone and have a successful connec-

tion via Bluetooth. This can occur if the Recupe ap-

plication is closed on the smartphone, or if the smart-

phone is out of the sensor range. The sensor blinking

green light conﬁrms that it is successfully connected

to the smartphone.

3.2 Algorithm

In order to count the repetitions per exercise, the al-

gorithm properly fuses Gyroscope and Accelerometer

inputs. The conﬁdence on each sensor can be con-

ﬁgured independently. With the help of sensor fu-

sion, the strengths of the gyroscope and accelerome-

ter are obtained and the effects of weaknesses in each

are mitigated. The algorithm accounts for calibra-

tion of gyroscope signal and accelerometer, and post-

processing of algorithm output. In this algorithm, an-

gular velocity is also considered as a metric.

The gyroscope model is as in equation 1, where ω

is the true angular velocity, b is bias, and σ is addi-

tive zero-mean Gaussian noise. The static bias can be

calibrated out. The white noise is mainly due to the

nature of the sensor.

ω = ω + b + η, η ∼ N(0,σ

gyro

)) (1)

The equation 2 is used to calculate the orienta-

tion resulting from the gyroscope measurements. This

equation is the result of applying Taylor expansion to

gyroscope measurements.

θ(t + ∆t) ≈ θ(t) +

∂

∂t

θ(t)∆t + ε, ε ∼ O(∆t

) (2)

Given θ(t) which is the angle at last time step,

∂

∂t

θ(t) = ω which is the gyro measurement(angular

HUCAPP 2022 - 6th International Conference on Human Computer Interaction Theory and Applications

192

velocity), ∆t which is time step, and ε which is ap-

proximation error, we seek θ(t + ∆t) which is the an-

gle at current time step. When the sensor is used for

long periods, the approximation error leads to drift.

Since the duration it takes to do an exercise is short, it

can be neglected.

Linear acceleration is measured by equation 3.

With the accelerometer in a stationary position, the

linear acceleration results in the noisy gravity vector,

(g)

+ η. The direction of this vector is pointing up

with magnitude 9.81 m/s

= 1g. However, any accel-

eration yields the linear acceleration to a combined

vector of external forces,a

(l)

, and noisy gravity.

˜a = a

(g)

+ a

(l)

+ η, η ∼ N(0,σ

acc

)) (3)

Equation 4 relates the roll and pitch angles to the

normalized accelerometer readings. Depending on

the rotation sequences this matrix might hold differ-

ent row ordering.

ˆa =

˜a





−cos(−θ

)sin(−θ

)

cos(−θ

)cos(−θ

)

sin(−θ

)





(4)

By solving equation 4, the roll and pitch angles are

computed. A challenge to be addressed is that at mul-

tiples of 2π, the equations for the roll and pitch angles

have an inﬁnite number of solutions. It is somewhat

beneﬁcial to limit the range of the roll and pitch an-

gles to fall in the range of −π to π, but it still leads to

two distinct roll and pitch angle solutions. The solu-

tion is to limit either the angle of the roll or the angle

of the pitch to lie in between −

. In this work,

the role angle is restricted to −π to π, and the pitch

angle is ranged between −

The aim of this work is to be able to accurately

estimate 3D orientation using accelerometer and gy-

roscope data. To better estimate orientation, roll and

pitch angles that are estimated from accelerometer

data are fused with the gyroscope angle.

After calibrating gyroscope data by removing the

static bias from it, incorporating the output of the gy-

roscope to turn the angular velocity into an angular lo-

cation, applying a low pass ﬁlter to the accelerometer

data to eliminate the noise in the output, and ﬁnding

the accelerometer roll and pitch angles; the sensors’

data can be fused by equation 5. Alpha is a constant

weight that needs to be adjusted and Theta is a single

state orientation.

(t)

= α(θ

(t−1)

ω∆t) + (1 − α) − ( ˆa

+ a

)

(5)

Velocity information is taken into consideration as

well. Velocity is calculated as the integration of ac-

celerometer results fused with the raw gyroscope data.

Calculating the angular velocity gives us additional

metrics to characterize exercises.

Once the algorithm generates the orientation sig-

nal, a moving average ﬁlter is implemented to smooth

the output. The moving average ﬁlters the output de-

pending on the frequency of the data stream. Among

the Theta and velocity signals, we choose the one

that is appropriate depending on the exercise. Then

the chosen signal might need to be shrunk or ex-

panded to output the correct angle reading which can

be achieved by multiplying to a constant parameter,

Gamma. Finally, after selecting the proper ﬁltered

signal and scaling it, the number of peaks in the signal

are counted by setting correspondent threshold angles

per exercise.

Twenty individuals were asked to do 2 sets of 10

repetitions for each exercise and a brute force search

was run to ﬁnd the best Alpha (fusion weight param-

eter), Gamma (scaling signal parameter), and best-

ﬁtted threshold angles for the counter.

3.3 Application

The application is both Android and IOS compatible.

The Bluetooth connection status can be checked at the

bottom of the Application screen. Before turning the

sensor on, it shows ”Searching for Sensor”. By click-

ing the sensor on, the connection status updates to

”Sensor Connecting”. As soon as the sensor connects

to the app via Bluetooth, the care sessions associated

with the patient are displayed on the screen. The ap-

pearance of a blinking green light on the sensor, sig-

Figure 2: Application screen for a sample home screen.

Comprehensive Musculoskeletal Care Platform Enabling At-home Patient Care

193

nals that the connection is established, the connection

status at the bottom of the screen changes from ”Sen-

sor Connecting” to ”Sensor Ready” as well. Rele-

vant pre-surgery and post-surgery exercise program

is selected by the patient’s orthopedic surgeon and is

included in the care session of the patient. Before

starting the program exercises, the patients are asked

about their pain level. The care sessions are easy to

follow and each of the home exercises that appears

on the care session screen includes text and video in-

struction on how to correctly do the exercise. Fig-

ure 2 shows the screen of the application including

a sample exercise, Long Arc Knee Exercise. When

the sensor is stable, the start button in the bottom left

corner activates. By selecting the start button, it turns

into a pause option to allow patients to pause the ex-

ercise if they need to. The number of sets and repeti-

tions per set is determined by the patient’s doctor. In

Figure 2, there are 2 sets of 10 repetitions for Long

Arc Knee Exercise. The gauge at the right bottom

of the exercise screen tracks the angle measurements

in real-time. Whenever a repetition is successfully

completed, the table at the left bottom of the exercise

screen updates the repetitions.

The application records the medications that the

patients take and once the patients complete their ex-

ercise program, the application reminds them to take

their medications.

Moreover, patients’ symptoms are checked daily

through a survey by asking them about how they feel

compared to the last care session, how their exercises

go through the session, what their current pain level

is, and what they feel about their Recupe program.

The application assesses patients to follow their icing

and elevation instructions throughout the day and to

track their icing time by an ice countdown.

3.4 Dashboard

Plethy Recupe enables the patients’ care team to track

their progress and contact them as needed. Clinicians

can track patients’ progress while they manage their

care plan at home. The Plethy Recupe clinical dash-

board gives clinician and Recupe team authorized ac-

cess to the library of exercises and patients’ accounts.

They can monitor patient’s pain level, medication,

symptoms, physical therapy adherence, range of mo-

tion percentage, pre-operation checklist, and their el-

evation and icing. Figure 3 illustrates the look of

a patient’s account. Access authorizers may modify

the pre-operation and post-operation exercises per pa-

tient and the number of daily repetitions for each ex-

ercise. Moreover, they can write notes and activity

reports to their patients, set an appointment for them,

Figure 3: Sample screen from a patient account in dash-

board.

check the surveys that patients have ﬁlled out, sched-

ule their daily medications, and manage the instruc-

tions and checklists of pre-operation, post-operation,

and non-surgical programs. The Plethy Recupe care

team monitors the battery level of devices and no-

tify patients to exchange the battery of their device if

they need to. Registering the demographic character-

istics of a new patient, an account will be added to the

patients’ list. Then, clinicians can create a template

from the existing exercises in the library and assign

it to the account. There exist hundreds of exercises

in the library and it’s possible to add a new exercise

to the library along with the exercise description and

instruction video. The parameters and threshold an-

gles associated with the algorithm for the exercise are

set in the exercise dashboard proﬁle. Furthermore, in

the exercise dashboard proﬁle, its number of repeti-

tions, the duration, and hold time per repetition are all

deﬁned.

4 CLINICAL APPLICATION

The system has been tested on 10 healthy subjects, 1

female and 9 males with an average age of 53. They

were asked to do 6 exercises and ﬁll out the user sat-

isfaction survey that has been approved by our expert

clinicians. The participants are expected to complete

2 sets of 10 successful repetitions for each exercise.

They have been instructed on how to use the platform

and where to place the sensor regarding each exercise.

The exercises are as follows:

• Long Arc Knee Extension, participants are asked

to sit with their back against a chair and thighs

fully supported. They are instructed to lift the op-

erated foot up, straighten the knee, and hold for a

ﬁve-second count. They must not raise thigh off

of the chair.

• Heel Slides with Quad Sets, participants are

asked to lie down on their back with their not af-

fected leg bent at the knee. They are instructed to

tighten the thigh and buttocks of the affected leg

HUCAPP 2022 - 6th International Conference on Human Computer Interaction Theory and Applications

194

Table 1: Users’ scoring the 10-point Likert scale usability questions (strongly disagree (1) to strongly agree (10)).

Questions

Subject ID

1 2 3 4 5 6 7 8 9 10 Average

Did you enjoy your experience with the system? 9 9 9 8 9 9 9 8 7 9 8.6 ± 0.7

Were you successful using the system? 10 9 9 9 10 10 9 9 7 9 9.1 ± 0.88

Is the information provided by the system clear? 9 9 8 9 9 9 9 8 8 10 8.8 ± 0.63

Do you ﬁnd the system easy to use? 9 8 9 8 9 9 8 9 7 9 8.5 ± 0.71

How easily did you learn to use the system? 9 9 8 8 10 9 8 8 8 9 8.6 ± 0.7

How accurate did you ﬁnd the angle measurement? 9 8 8 9 10 9 9 9 8 8 8.7 ± 0.67

How accurate did you ﬁnd the rep counts? 10 9 9 9 10 10 9 9 8 10 9.3 ± 0.67

Do you think that this system will be helpful for your rehabilitation? 10 9 9 9 10 10 9 9 9 9 9.3 ± 0.48

If prescribed by a healthcare practitioner, would you use the Recupe and perform the exercise program daily? 10 10 10 10 10 10 10 10 9 10 9.9 ± 0.32

and hold it for 5 seconds, then, bend their knee

and pull the heel towards the buttocks for 3 sec-

onds.

• Toe Raises , participants are asked to stand facing

the kitchen sink with a ﬁrm hold on the kitchen

sink. They are instructed to rise up on toes then

back on heels and stand as straight as possible.

• Reverse Toe Raises, participants are asked to

stand holding onto a chair or supportive object.

They are instructed to raise their toes and feet off

the ﬂoor, then, slowly lower toes back to the ﬂoor.

• Ankle Pump, participants are asked to place a pil-

low under the ankle, and lie ﬂat on the ﬂoor. They

are instructed to bend ankles to move feet up and

down. They must not make use of a chair to do

this exercise.

• Seated Ankle Pump, participants are asked to be-

gin sitting upright with one leg straight forward.

They are instructed to slowly pump their ankle

by bending their foot up toward their body, then

pointing their toes away from their body. They

must make sure to move their foot in a straight

line and try to keep the rest of their leg relaxed.

5 RESULTS AND DISCUSSION

The questionnaire that the subjects were asked to ﬁll

out after their experiment, is designed by our expert

clinicians and engineers to evaluate the system usabil-

ity and accuracy of exercises. The system usability

questions along with the participants’ 10-point Lik-

ert scale scores, where 1 strongly disagrees and 10

strongly agrees, are included in the Table 1. The aver-

age 10-point score given to each usability question is

determined in the last column of this table. Each par-

ticipant rated the exercises based on their experience,

the average scores given by the participants to each

of the exercises are visualized in Figure 4. The grey

error bar located in the center of the exercise bar rep-

resents the standard deviation of exercise scores. The

Users’ average range of motion for each exercise that

has been measured by the algorithm, is extracted from

the database, and Figure 5 indicates the participants’

average range of motion measured for each exercise.

The grey error bar in the middle of each exercise bar

identiﬁes the standard deviation of participants’ range

of motion. Based on the records, the average user sat-

isfaction of the platform is 93%±6.4, and the average

participants’ score to the accuracy of the 6 exercises

is 90% ± 4.6.

6 CONCLUSION AND FUTURE

WORK

In this paper, Plethy Recupe has been introduced to

assist the patients through their musculoskeletal reha-

bilitation beyond the clinical environment. The sys-

tem consists of a wearable sensor, a smartphone appli-

cation, and a clinical dashboard that enables therapists

to remotely set up speciﬁc rehabilitation programs de-

Figure 4: Average 10-point score given by participants to

each of the 6 exercises.

Figure 5: Average range of motion performed by partici-

pants for each of the 6 exercises.

Comprehensive Musculoskeletal Care Platform Enabling At-home Patient Care

195

pending on patient disorder and characteristics and to

monitor patient progress over time. With the aid of ac-

celerometer and gyroscope signals read by the single

IMU in the sensor unit, we count the successful ex-

ercise repetitions and guide the user to correctly per-

form the exercises. We picked 6 pre-tested exercises

from the library. These six exercises are under pilot

study in the hospitals we collaborate with as well. The

result of the study on 10 healthy subjects with the su-

pervision of a team of expert clinicians proves 90%

of the user satisfaction score for the exercises. The

major limitation of this study is that the system eval-

uation and the evaluation results are conducted only

on healthy subjects, however, our future prospective

is to extensively evaluate the Plethy platform by mus-

culoskeletal disorders patients.

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