PhysioBuddy: A Real World Django React System for Remote Physiotherapy Pose Assessment

Abstract - PhysioBuddy, an AI-driven web application. This system utilizes state-of-the-art Computer Vision models to perform real-time Human Pose Estimation (HPE) on user video streams, ensuring the correct execution of orthopedic exercises. The primary objective is to provide instant, actionable form correction and repetition counting, thereby maximizing therapeutic efficacy and preventing injury. The methodology centers on a decoupled architecture using Django and React, communicating via WebSockets for low-latency feedback. Results demonstrate the capability to process video frames and return form validation offering a viable and scalable alternative to in-person supervision. The application offers a secure platform where doctors can prescribe exercises and monitor patient compliance and performance. Key Words: Human Pose Estimation, Real-time Systems, Django Channels, Computer Vision, Physiotherapy, Rehabilitation.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-10-08
DOI
https://doi.org/10.55041/ijsrem52915
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
0.00
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article

PhysioBuddy: A Real World Django React System for Remote Physiotherapy Pose Assessment

Sachin Bhoite, Prof. Meera Sawalkar, Omkar V. Gite, Atharva Wattamwar et al.
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Stroke Rehabilitation and Recovery
article

PhysioBuddy: A Real World Django React System for Remote Physiotherapy Pose Assessment

Sachin Bhoite, Prof. Meera Sawalkar, Omkar V. Gite, Atharva Wattamwar, Kalpesh Gawas
article en

Abstract

Abstract - PhysioBuddy, an AI-driven web application. This system utilizes state-of-the-art Computer Vision models to perform real-time Human Pose Estimation (HPE) on user video streams, ensuring the correct execution of orthopedic exercises. The primary objective is to provide instant, actionable form correction and repetition counting, thereby maximizing therapeutic efficacy and preventing injury. The methodology centers on a decoupled architecture using Django and React, communicating via WebSockets for low-latency feedback. Results demonstrate the capability to process video frames and return form validation offering a viable and scalable alternative to in-person supervision. The application offers a secure platform where doctors can prescribe exercises and monitor patient compliance and performance. Key Words: Human Pose Estimation, Real-time Systems, Django Channels, Computer Vision, Physiotherapy, Rehabilitation.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(10)
Openalex Percentile: Top 65%
Stroke Rehabilitation and Recovery
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