Evidence map›Paper›PMID 42812736›Full record

ReviewCureus2026

Integration of Biomechanical Analysis and Motion Capture Technology in Physical Therapy and Rehabilitation: A Systematic Review.

Chetana A Pawar, Sandeep Shinde, Durga A Jagdale

Abstract readReview
In one paragraph

Review in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Chetana A PawarDepartment of Musculoskeletal Sciences, Krishna College of Physiotherapy, Krishna Vishwa Vidyapeeth (Deemed to Be University), Karad, IND.
Sandeep ShindeDepartment of Musculoskeletal Sciences, Krishna College of Physiotherapy, Krishna Vishwa Vidyapeeth (Deemed to Be University), Karad, IND.
Durga A JagdaleDepartment of Musculoskeletal Sciences, Krishna College of Physiotherapy, Krishna Vishwa Vidyapeeth (Deemed to Be University), Karad, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomechanical analysis and motion-capture technology have emerged as valuable tools for the objective assessment of human movement in physical therapy and rehabilitation. These technologies provide quantitative information on gait, balance, posture, joint kinematics, and functional performance, thereby supporting clinical decision-making and individualized rehabilitation. Despite increasing clinical adoption, evidence regarding their effectiveness across different rehabilitation populations remains heterogeneous. This systematic review aimed to evaluate the integration of biomechanical analysis and motion-capture technology in physical therapy and rehabilitation. A systematic literature search was conducted across major electronic databases to identify randomized controlled trials evaluating the integration of biomechanical analysis and motion-capture technologies within physical therapy and rehabilitation. Studies that investigated biomechanical analysis, motion-capture systems, wearable sensors, virtual reality, and markerless motion-capture technologies in physical therapy interventions were considered. Methodological quality was assessed using the Cochrane Risk of Bias 2 tool, and a narrative synthesis was performed because of clinical and methodological heterogeneity. The evidence encompassed neurological, musculoskeletal, pediatric, geriatric, and oncological rehabilitation populations. Interventions incorporated Kinect-based rehabilitation, markerless motion capture, wearable sensor systems, virtual reality, robot-assisted gait training, movement biofeedback, and sensor-augmented telerehabilitation. Overall, the evidence indicated improvements in gait, balance, postural control, movement quality, functional mobility, limb-loading symmetry, upper limb function, and quality of life across various rehabilitation settings. Risk of bias assessment indicated generally low risk of bias or some concerns, although concerns related to deviations from intended interventions and reporting practices were observed across the evidence base. The integration of biomechanical analysis and motion-capture technology may enhance objective movement assessment and support functional recovery across diverse rehabilitation populations. These technologies may facilitate individualized rehabilitation and evidence-based clinical decision-making through quantitative movement analysis and feedback. However, methodological heterogeneity highlights the need for standardized assessment protocols and high-quality trials to establish their long-term clinical effectiveness and support broader implementation in rehabilitation practice.

Indexed as

biomechanical analysisbiomechanicsgait analysismarkerless motion capturemotion capturephysical therapyvirtual realitywearable sensors

Identifiers

PMID42812736
PMCPMC13621436

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.