PosePilot: An Edge-AI Solution for Posture Correction in Physical Exercises

TL;DR

PosePilot is an edge-AI system that combines pose recognition with real-time personalized feedback to improve posture correction in exercises like Yoga, enabling accurate, instant guidance suitable for deployment on portable devices.

Contribution

This work introduces PosePilot, a novel edge-AI system integrating advanced pose recognition models with real-time corrective feedback for physical exercises.

Findings

Effective real-time posture correction in Yoga demonstrated

High accuracy in pose recognition using LSTM and BiLSTM models

Feasible deployment on edge devices for at-home use

Abstract

Automated pose correction remains a significant challenge in AI-driven fitness systems, despite extensive research in activity recognition. This work presents PosePilot, a novel system that integrates pose recognition with real-time personalized corrective feedback, overcoming the limitations of traditional fitness solutions. Using Yoga, a discipline requiring precise spatio-temporal alignment as a case study, we demonstrate PosePilot's ability to analyze complex physical movements. Designed for deployment on edge devices, PosePilot can be extended to various at-home and outdoor exercises. We employ a Vanilla LSTM, allowing the system to capture temporal dependencies for pose recognition. Additionally, a BiLSTM with multi-head Attention enhances the model's ability to process motion contexts, selectively focusing on key limb angles for accurate error detection while maintaining computational efficiency. As part of this work, we introduce a high-quality video dataset used for evaluating our models. Most importantly, PosePilot provides instant corrective feedback at every stage of a movement, ensuring precise posture adjustments throughout the exercise routine. The proposed approach 1) performs automatic human posture recognition, 2) provides personalized posture correction feedback at each instant which is crucial in Yoga, and 3) offers a lightweight and robust posture correction model feasible for deploying on edge devices in real-world environments.