# MobiPhysio: A 2D video dataset of physiotherapy exercises for AI-driven assessment and monitoring

## TL;DR

MobiPhysio is a dataset of 2D videos of physiotherapy exercises that helps develop AI systems for assessing and monitoring rehabilitation without using special sensors.

## Contribution

The dataset introduces a realistic, annotated 2D video collection of physiotherapy exercises for AI research and development.

## Key findings

- The dataset includes 3686 videos of 9 exercises performed by 58 participants under varied real-world conditions.
- Videos are annotated with scores from an expert-designed questionnaire to assess exercise accuracy.
- The dataset supports AI research for physiotherapy using common 2D cameras like mobile phones.

## Abstract

We present MobiPhysio, a 2D video-based dataset designed to support AI-driven physiotherapy assessment and monitoring. The dataset contains 3686 segmented videos of 9 Active Range of Motion physiotherapy exercises performed by 58 male and female participants. The recordings are done under the variations in lighting, camera angles, occlusion, and jitter in order to mimic real-world conditions. Data collection occurred in two phases: first from non-expert participants at Stamford University Bangladesh, and later from expert participants at the Department of Physiotherapy and Rehabilitation, Jashore University of Science and Technology. The entire process was conducted under the guidance of certified physiotherapists. Each video is further annotated with assessment scores derived from the exercise-specific Exercise Accuracy Assessment Questionnaire (EAAQ), developed under expert guidance. This dataset would enable researchers to build and test AI-powered physiotherapy and rehabilitation systems, examine human motion, and create exercise monitoring solutions using available 2D camera devices like mobile phones without the need of external body-reliant sensors.