Bodynetic¶
AI · Computer Vision · Mobile · Android
An AI-powered posture analysis system that uses computer vision to detect, assess, and report on musculoskeletal alignment in real time, guiding users through corrective exercises and tracking progress over time.
The Problem¶
Musculoskeletal dysfunction, driven largely by poor posture and sedentary behaviour is one of the most prevalent and underdiagnosed health issues globally. Office workers, athletes, and rehabilitation patients all share a common gap: there is no reliable, accessible way to monitor body alignment outside a clinical setting.
The consequences are real. Postural dysfunction leads to chronic pain, reduced mobility, and long-term injury risk, yet most people only seek help after symptoms become debilitating. For physical therapists and wellness practitioners, the challenge runs deeper because manual assessments are time-consuming, inconsistent across practitioners, and impossible to perform continuously or remotely.
Existing tools fell short in three key areas:
- Access: quality posture analysis required in-person clinical visits, which limited reach and frequency
- Consistency: subjective manual assessments varied between practitioners and lacked objective data
- Continuity: there was no way to track postural changes or exercise adherence over time
Wellness programs, corporate health initiatives, and fitness coaches all needed a scalable, evidence-based solution. One that could meet users where they are, whether at home, in the gym, or in the clinic, and deliver personalised, data-driven feedback without constant expert involvement.
The Solution¶
Bodynetic delivers an intelligent posture analysis system that leverages computer vision and pose estimation models to analyze body alignment from standard camera input. The system provides:
- Real-time posture detection using pose landmark models (MediaPipe)
- Automated assessment reports flagging deviations and risk areas
- Progress tracking over time for therapy and wellness programs
- Mobile-accessible interface for use in clinic or at home
- Guided corrective exercises with AI feedback on form
Technology Used¶
| Layer | Stack |
|---|---|
| Computer Vision | Python · MediaPipe · OpenCV |
| Backend | Python · FastAPI |
| Mobile | Android |
| Reporting | Jinja2 |
| Infrastructure | GCP · Docker · Fastlane |