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Awareness, knowledge, attitudes, and barriers of app-based human motion capture technology for rehabilitation, among physiotherapists- An exploratory study
*Corresponding author: Raveena Kini, Department of Physiotherapy, Mahatma Gandhi Mission’s College of Physiotherapy, Navi Mumbai, Maharashtra, India. raveenarkini@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Kini R, Pandhare S. Awareness, knowledge, attitudes, and barriers of app-based human motion capture technology for rehabilitation, among physiotherapists- An exploratory study. Adesh Univ J Med Sci Res. doi: 10.25259/AUJMSR_50_2025
Abstract
Objectives:
With increasing availability of resources in a cost-effective manner, technology must be put to use to maximize the benefits in the physiotherapy assessment and rehabilitation. For that, the preliminary step is to identify the existing awareness, knowledge, practices and barriers of the app-based motion capture technology among physiotherapists.
Material and Methods:
An indigenous questionnaire was formulated which included 4 domains viz awareness, knowledge, attitudes and barriers. It which was later content and face-validated with the help of experts. After the pilot testing it was sent to physiotherapy professional in Maharashtra in the form of google forms.
Results:
About 24.26% (n=49) were not aware of the term MOCAP in rehabilitation. In the quiz of the knowledge section, the participants got a median score of 10 with interquartile range of 8 to 11 marks. There were significant associations between awareness of app-based MOCAP technology among participants with that of highest degree of participants, age and years of passing and between scores of the quiz in the knowledge section with that of age and degree of the participants.
Conclusion:
We concluded that there is a gap in the awareness of MOCAP among physiotherapy professionals as presented from knowledge quiz scores, attitudes and barriers obtained in the survey.
Keywords
Awareness
Motion capture
Physiotherapists
Rehabilitation
INTRODUCTION
With technological advancement, artificial intelligence (AI) is revolutionizing fields like healthcare in transformative ways, and it is being integrated into clinical practice as well.[1] AI conducts tasks such as decision-making, language understanding, problem-solving, and visual perception, which require human intelligence, in a fraction of a second.[2] AI is used for diagnosis, in performing minimally invasive surgeries with precision, for teleconsultations, and for analyzing data for more informed decisions, to name a few.[3]
Just like it has promoted ease of operation in the medical field, AI technology has made its way into rehabilitation through a combination of healthcare needs, advancements in AI algorithms, and the development of new tools and devices.[4] Various studies attribute AI in capturing human motion with remarkable objectivity, precision, personalization, and efficiency.[5] AI and virtual assistants can guide patients through home exercises, ensuring they perform them correctly even when they are not under the direct supervision of a physiotherapist.[6] There are applications which can provide reminders, encouragement, and correction based on real-time data from wearable devices and cameras, and also track the range of motion, muscle activity, and posture.[6]
AI has significantly advanced motion capture (MOCAP) technology, making it an increasingly effective tool in physiotherapy rehabilitation.[4] This combination has streamlined the diagnosis, monitoring, and treatment processes in several ways. AI enhances MOCAP technology by providing more accurate data analysis.[5] Traditional MOCAP systems relied on markers and sensors attached to the patient’s body.[7] AI combined with computer vision allows for app-based markerless MOCAP using cameras and sensors, providing detailed analysis of movement without invasive tools.[2,4,7] This helps physiotherapists assess patient movements in real-time, making the diagnosis more precise.
A study done by Steffensen et al.[7] states that markerless MOCAP technology provides equivalent efficacy in comparison to marker-based MOCAP technology. With the advent of wearable sensors in devices such as smart watches/smartphones human MOCAP is now easily possible without the need for extensive machinery.[8] A study done by Lind et al. further adds to the evidence of the use of MOCAP technology of wearable devices in preventing work-related musculoskeletal disorders by providing real-time data that give a fair idea of the ergonomics followed by the person wearing it.[9] Thus, with increasing availability of resources in a cost-effective manner, technology must be put to use to maximize the benefits in the physiotherapy assessment and rehabilitation. For that, the preliminary step is to identify the existing awareness, knowledge, practices, and barriers of the app-based MOCAP technology among physiotherapists practicing in and around our area. This information will help us to identify if there is a further need to educate the physiotherapists on the same.
MATERIAL AND METHODS
Study design
It was a qualitative study conducted on the graduated physiotherapists of Maharashtra, which received ethical approval from the Institutional Research Review Committee (IRRC No: MGM/COP/IRRC/36/2023 dated on October 15th, 2023).
Study population
The study included graduated physiotherapists from Maharashtra, completed at least 4.5 years of Bachelor of Physiotherapy (BPTh) degree, who are clinicians (hospital-based, clinic-based, and/or freelancers), academicians, researchers, or individuals pursuing postgraduation. It was ensured that they were into patient care and practicing professionals for at least 6 months after graduation, who were willing to participate in the study. Physiotherapists who are not indulged in any form of patient care such as those indulged in any administrative work, organizing workshops, and marketing or without a degree of the above-mentioned adequate duration or not consenting to participation were excluded from the study.
Sample size and sampling technique
A sample size of 161 was calculated using Openepi Software (version 3.01) based on the estimated population size of 7500 registered physiotherapists as provided by the website of the Indian Association of Physiotherapists and a confidence interval of 80%. However, 202 respondents filled out the questionnaire as we opted for the Snowball sampling technique.
Methodology
The study methodology was divided into four parts, namely designing an indigenous questionnaire, estimating the content and face validation of the indigenous questionnaire, pilot testing of the questionnaire, and eventual administration to the participants.
Designing the questionnaire
An indigenous questionnaire was prepared, which included the basic demographics of participants with their brief work profile and four domains namely awareness, knowledge, attitudes, and barriers. The awareness domain included 7 sub-questions, the knowledge domain included 14 sub-questions, and the attitudes domain included 5 sub-questions, whereas the practice domain included 3 sub-questions.
Content and face validation
After the formulation of the questionnaire, it was handed over to the experts in the field, who helped in content and face validation of the questionnaire. Changes suggested by them were incorporated, as per feasibility, and finalized, where a content validation index of 0.9 was obtained.
Pilot testing
A pilot test was conducted on 10 participants to check the feasibility of questionnaire administration and understandability of all the sub-questions. Based on the suggestions obtained, the questionnaire was finalized. Test– retest reliability was also ascertained, wherein the same participants were asked to refill the questionnaire after 15 days to mitigate the effects of recall. The data from the first and second sets of answers of the same respondents were tabulated into Excel, and the reliability coefficient came out to be 0.76, indicating good reliability.
Administration of participants
The questionnaire was converted into a Google form and was sent to the participants via social media platforms. Prior informed consent was obtained from the participants. Participants were selected on the basis of inclusion and exclusion criteria. Participants filled out and submitted the online validated questionnaire. Stringent measures were well implemented to safeguard participant confidentiality. A few physiotherapy practitioners, academicians, researchers were contacted from different academic and clinical institutes and details of their colleagues were taken and they were further asked to share the forms with the respective colleagues who fit the criteria of the study, which included graduated physiotherapists from Maharashtra, who completed the 4.5 years of BPTh degree, who are either clinicians (hospital-based, clinic-based, and/or freelancers), academicians, researchers, or individuals pursuing postgraduation, who were into patient care for at least 6 months and were willing to participate in the study after ticking on the consent form. Furthermore, all basic demographic and work profiles were requested to be filled at the beginning of the questionnaire. These were again verified after they were submitted to ensure they fit the inclusion criteria to ensure appropriate sample representativeness. They were approached online through social media, email, and online messengers, and offline by visiting accessible physiotherapy teaching institutions, hospitals, and physiotherapy clinics. Three reminders were given at regular intervals to the potential participants every week who had not filled the form in 1st attempt, living across different cities of Maharashtra.
Statistical analysis
Data were tabulated and coded in Microsoft Excel, followed by descriptive analysis to summarize the data.
RESULTS
In the study, about 82.7% (n = 167) were female and 17.3% (35) were male. About 50.5% (n = 102) had BPTh, 49% (n = 99) had Master of Physiotherapy (MPTh), and only 0.5% (n = 1) had a fellowship as the highest physiotherapy degree. About 33.2% (n = 67) specifically practiced musculoskeletal physiotherapy, 15.8% (n = 32) specifically practiced neurophysiotherapy, 11.4% (n = 23) specifically practiced cardiovascular and respiratory physiotherapy, 9.4% (n = 19) practiced sports physiotherapy, 5.4% (n = 11) practiced community physiotherapy, and 1% (n = 2) practiced oncophysiotherapy. The remaining 23.8% (n = 48) practiced a mix of all specialties. The further demographic details are explained in Table 1.
| Outcome assessed | Mean±SD | Frequency (%) |
|---|---|---|
| Age (years) | 25.46±3.53 | 21–25 years: 70.3 (n=142) |
| 26–30 years: 22.77 (n=46) | ||
| >31 years: 6.93 (n=14) | ||
| Area of residence | - | Mumbai: 64.85 (n=131) |
| Navi Mumbai: 19.31 (n=39) | ||
| Pune: 3.96 (n=8) | ||
| Nanded: 1.49 (n=3) | ||
| Nashik: 2.97 (n=6) | ||
| Nagpur: 2.97 (n=6) | ||
| Solapur: 0.5 (n=1) | ||
| Pen: 0.5 (n=1) | ||
| Navapur: 0.5 (n=1) | ||
| Chandrapur: 0.5 (n=1) | ||
| Jalgaon: 0.5 (n=1) | ||
| Sangli: 1 (n=2) | ||
| Ratnagiri: 0.5 (n=1) | ||
| Latur: 0.5 (n=1) | ||
| Experience (years) | 2.48±3.24 | 0.5–1 years: 48.5 (n=98) |
| 1.1–3 years: 34.2 (n=69) | ||
| 3.1–6 years: 10.5 (n=21) | ||
| >6 years: 6.8 (n=14) | ||
| Work profile | - | Pursuing postgraduation- 45 (n=91) |
| Clinician (Hospital/Clinic)- 43.6 (n=88) | ||
| Academician- 6.9 (n=14) | ||
| Physiotherapy researcher- 1 (n=2) | ||
| Freelancer- 3.5 (n=7) |
SD: Standard deviation
Awareness
The 7 sub-questions of the awareness domain are described in Table 2. It showed that 24.26% (n = 49) were not aware of the term MOCAP among our participants.
| S. No. | Awareness domain | Yes (%) | No (%) | Maybe (%) |
|---|---|---|---|---|
| 1 | Aware of the use of artificial intelligence in physiotherapeutic assessment | 69.8 (n=141) | 13.86 (n=28) | 16.34 (n=33) |
| 2 | Aware of the use of artificial intelligence in physiotherapeutic rehabilitation | 68.81 (n=139) | 13.37 (n=27) | 17.82 (n=36) |
| 3 | Aware of the term “MOCAP technology” | 47.52 (n=96) | 24.26 (n=49) | 28.22 (n=57) |
| 4 | Aware of the use of motion capture in physiotherapy assessment and rehabilitation | 50 (n=101) | 24.75 (n=50) | 25.25 (n=51) |
| 5 | Aware of devices used for motion capture | 44.05 (n=91) | 28.22 (n=57) | 26.73 (n=54) |
| 6 | Aware of applications used in smartphones and wearable devices for human motion capture | 58.42 (n=118) | 18.81 (n=38) | 22.77 (n=46) |
| 7 | Aware of the benefits and drawbacks of app-based motion capture technology in rehabilitation | 54.46 (n=110) | 24.75 (n=50) | 20.79 (n=42) |
MOCAP: Motion capture
Knowledge
The knowledge section was a quiz of 14 marks, with each correct answer holding a mark. The participants got a median score of 10 with an interquartile range of 8–11 marks. Table 3 demonstrates the number of participants who gave the correct answer in each sub-question.
| Question | No of correct answers % (n) |
|---|---|
| 1. What is MOCAP? | 84.2 (n=170) |
| 2. What are the types of MOCAP? | 81.7 (n=165) |
| 3. What is marker-based MOCAP technology? | 78.2 (n=158) |
| 4. What is markerless MOCAP technology? | 78.2 (n=158) |
| 5. What is the key feature of marker-less motion capture technology? | 65.8 (n=133) |
| 6. Which of the following software/apps are used to capture human motions? | 19.3 (n=39) |
| 7. Microsoft Kinect is used for what? | 83.7 (n=169) |
| 8. What is the use of an app named Kinovea? | 79.2 (n=160) |
| 9. Which of the following domains support motion capture? | 55.4 (n=112) |
| 10. What is the potential disadvantage of app-based human motion capture technology? | 56.9 (n=115) |
| 11. Which of the following patients’ conditions can benefit from app-based motion capture technology? | 17.8 (n=36) |
| 12. How does app-based motion capture technology typically provide feedback to users in rehabilitation? | 73.8 (n=149) |
| 13. What type of data can be obtained from motion capture technology in rehabilitation? | 93.1 (n=188) |
| 14. Which aspects of rehabilitation does app-based MOCAP technology primarily target? | 64.4 (n=130) |
MOCAP: Motion capture
Attitudes
The existing attitudes of the participants were analyzed which are depicted in Table 4.
| Attitudes domain | Agree (%) |
|---|---|
| 1. App-based MOCAP technology can be used in assessment and rehabilitation in acute, subacute as well as chronic musculoskeletal conditions | 67.32 (n=136) |
| 2. App-based MOCAP technology can be used in assessment and rehabilitation in acute, subacute as well as chronic neurological conditions | 70.5 (n=141) |
| 3. App-based MOCAP technology can be used in assessment and rehabilitation of cardiovascular and respiratory conditions | 66.34 (n=134) |
| 4. The use of MOCAP must be included in the curriculum of physiotherapy | 93.56 (n=189) |
| 5. Further researches and innovations must be done in the use of MOCAP with human rehabilitation | 95.54 (n=193) |
MOCAP: Motion capture
Barriers
This domain included three questions namely the challenges faced by the physiotherapists while implementing app-based MOCAP, the technological barriers in the adoption and finally drawbacks of storing and retrieving kinematic and kinetic data. The in-depth analysis of the same is done in Graphs 1-3.
- Analysis of barriers in the implementation of app based motion capture. BPTh: Bachelor of Physiotherapy.
- Analysis of technological barriers in the adoption of motion capture in rehabilitation.
- Analysis of drawbacks of storing and retrieving kinematic and kinetic data in motion capture.
To further find the association of awareness with gender, highest degree (BPTh/MPTh/others), area of practice (clinician/academician/researcher), Chi-square test was applied.
There was no association of awareness and gender as well as awareness with area of practice (P > 0.05), however, there was an association between awareness and highest degree (P < 0.05) as shown in Table 5.
| Association of awareness with | Pearson’s Chi-square coefficient | P-value* |
|---|---|---|
| Gender | 0.46 | 0.977 |
| Highest degree | 14.88 | 0.005 |
| Area of practice | 9.026 | 0.340 |
Likewise, linear regression (analysis of variance) was used to check an association between awareness and age, year of passing, years of work experience, and scores of the quiz taken in the knowledge domain, as shown in Table 6.
| Association of awareness with | B | Standard error | t-value | P-value* |
|---|---|---|---|---|
| Age | −0.102 | 0.040 | −2.544 | 0.012 |
| Year of passing | −0.154 | 0.044 | −3.511 | 0.001 |
| Work experience | −0.044 | 0.042 | −1.038 | 0.301 |
| Score on knowledge quiz | 0.027 | 0.027 | 1.029 | 0.305 |
Likewise, linear regression was used to assess an association between the scores in the knowledge domain and age, year of passing, work experience, gender, degree, and area of practice, as shown in Table 7. There was an association between the scores of the knowledge quiz and both age and the highest academic degree.
| Association of scores of knowledge quiz with | B | Standard error | t-value | P-value* |
|---|---|---|---|---|
| Age | −0.269 | 0.106 | −2.541 | 0.012 |
| Year of passing | −0.196 | 0.119 | −1.645 | 0.101 |
| Work experience | 0.110 | 0.116 | 0.950 | 0.343 |
| Gender | 0.129 | 0.418 | 0.308 | 0.758 |
| Highest degree | −0.606 | 0.333 | −1.821 | 0.070 |
| Area of experience | 0.125 | 0.180 | 0.693 | 0.489 |
DISCUSSION
The study reveals that there is limited awareness about the use of app-based markerless MOCAP technology in assessment and rehabilitation among the physiotherapists practicing in Maharashtra. Moreover, the median score of the participants in the quiz in the knowledge domain was 10 with an interquartile range of 8–11 marks. In addition, there were significant associations between awareness of app-based MOCAP technology among participants and the highest degree, age, and years of passing. Furthermore, there was a significant association between scores of the quiz in the knowledge section with of the age and degree of the participants. To the best of our knowledge, this is the first study to determine the awareness, knowledge, practices, and barriers of app-based MOCAP technology among physiotherapists practicing in Maharashtra.
Awareness of app-based MOCAP technology among physiotherapists
In our study, about 24.26% (n = 49) were not aware of MOCAP, 24.75% (n = 50) were not aware of the use of MOCAP in physiotherapeutic assessment and rehabilitation, and 28.22% (n = 57) were not aware of the devices used for MOCAP. This is in consensus with a study done by Umer et al., which stated that there was reduced awareness of the use of AI among participants who are medical students and professionals in Pakistan.[10] This could be because technological resources are not majorly included in the curriculum of state health universities directly. Furthermore, in our study, the majority of the population were either pursuing postgraduation or were clinical professionals. Previous studies have shown a reduced amount of evidence-based practice among physiotherapy students in view of a lack of formal training and poor resource availability to access paid articles.[11] This could lead to reduced awareness and knowledge on MOCAP, which is not even a part of most state university curricula as of today.
Knowledge of app-based MOCAP technology among physiotherapists
While majority of the people answered questions on what is MOCAP, types of MOCAP, type of data obtained, and functioning of MOCAP, not much knowledge was noted on the various apps used for MOCAP, the types of patients who can be assessed or rehabilitated through MOCAP. Apart from the lack of awareness and absence of the above-mentioned aspects in learning curriculum, there could be reservations of app prescriptions to patients among physiotherapists similar to those mentioned in a study done by Rowe and Sauls[12] Without practice and clinical application there does not arise the need to update one’s knowledge in any aspect like apps used for MOCAP or its application among patient population. Thus, this could lead to limited knowledge among our participants.
Attitudes and barriers on the use of app-based MOCAP technology among physiotherapists
The majority of participants agreed that app-based MOCAP could be more useful in neurological and musculoskeletal conditions as compared to cardiorespiratory conditions. A study done by Bayoumy et al. stated that in the era of remote, decentralized and increasingly personalized patient care which has increased after COVID-19, the cardiovascular community must familiarize itself with the wearable technologies which are easily accessible on the application store and thereby have proposed the recommendations to navigate these challenges and an “ABCD” guide for clinicians to ensure optimal use of the applications in the rehabilitations.[13] If as physiotherapy professionals we inculcate evidence in our practice, we will not only benefit but also assist the patient to achieve optimal rehabilitation outcomes. However, the majority of the participants also agreed that it should be included in the physiotherapy learning curriculum and that further research needs to be conducted to ensure better awareness among the physiotherapists, which is definitely required.
Among the barriers, lack of awareness and knowledge, resistance from patients to include MOCAP in rehabilitation, and difficulty in integration of technology in existing workloads in the clinic with limited resources available, were cited as major reasons. This is in consensus with a study done by Alsobhi et al., which was a mixed-method study that gave insights into the barriers of AI in rehabilitation.[14]
Association of awareness and knowledge with various participants’ demographic and work profiles
It was also noted that there was a positive association of awareness about MOCAP with the highest degree of the participants and negative association of awareness about MOCAP with age and years of clinical experience. This indicates that the younger generation with a higher attained degree was more aware than the more senior physiotherapy practitioners of different calibers. This is in consensus with a study done by Thai et al. wherein they stated that despite the fact that the youth were more aware about how to use AI in healthcare, yet they had limited trust over the working of AI.[15]
CONCLUSION
As per our study results, there is a need to generate awareness among the physiotherapy professionals of various calibers on use of application-based, markerless MOCAP, which is previously known to show tremendous assistance in physiotherapeutic rehabilitation. The study also explored the attitudes of the physiotherapists and the likely barriers to the implementation of MOCAP in rehabilitation. Moreover, physiotherapists with higher degrees, lesser age, and fewer years of experience had better awareness of MOCAP as compared to the rest.
Acknowledgments:
We would like to thank all the participants for participating in our study. We are also grateful to the experts for validating our questionnaire.
Ethical approval:
The research/study approved by the Institutional Review Board at Mahatma Gandhi Mission’s College of Physiotherapy, Navi Mumbai, number MGM/COP/IRRC/36/2023, dated 15th October 2023.
Declaration of patient consent:
The authors certify that they have obtained all appropriate patient consent.
Conflicts of interest:
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.
Financial support and sponsorship: Nil.
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