The problem GymAI solves

GymAI: Transforming Personal Fitness

Introduction

GymAI stands as a pioneering solution in personal fitness, providing an antidote to the one-size-fits-all workout plan and reducing the risk of injury from incorrect exercise execution.

Personalized Training

• Utilizes advanced AI algorithms for bespoke workout plans.
• Adapts routines in real time based on individual performance data.
• Targets specific goals, such as muscle development, endurance, or weight loss.
• Moves beyond generic programs to one that evolves with physiological feedback.

Real-time Feedback Mechanism

• Acts as a digital personal trainer for immediate exercise correction.
• Prevents the formation of bad habits by correcting posture and technique on the spot.
• Essential for safe training, particularly for novices or those resuming after a break.
• Measures workout correctness, ensuring exercises are performed accurately.

Enhanced Workout Effectiveness

• Each workout is tailored to maximize effectiveness and align with personal fitness goals.
• Dynamic personalization of workouts leads to more efficient and effective sessions.

Safety and Prevention

• Real-time feedback mitigates the risk of injury due to improper form.
• Promotes a safer workout environment by providing instant corrective feedback.

Challenges I ran into

Challenge: Real-Time Feedback Accuracy

The Bug

During the development of GymAI, we encountered a hurdle in ensuring the accuracy of real-time feedback provided to users. Initial tests showed a discrepancy between the AI-generated feedback and the user's actual performance, a critical issue since incorrect feedback could lead to injuries or demotivation.

Investigating the Cause

The issue was traced to the machine learning model's interpretation of motion sensor data. It failed to account for individual body type and movement pattern variability, resulting in generic and sometimes inaccurate advice.

The Solution

To tackle this, we implemented a calibration phase for the user to perform basic exercises. This helped the AI to learn the user's unique movement patterns. Furthermore, we expanded our dataset to cover a broader spectrum of body types and movement styles, enhancing the model's accuracy.

Implementation

These adjustments allowed the model to create a personalized movement profile, significantly improving the relevance and precision of feedback. We also introduced a user feedback loop, enabling the AI to continually learn and adapt by flagging incorrect advice.

Overcoming the Challenge

By adopting a more personalized approach and integrating continuous learning mechanisms, the real-time feedback feature was substantially refined. This experience underscored the importance of personalization and adaptability in AI applications, especially those related to human safety and performance.