As part of the third year of my Mechanical Engineering degree at the University of Bristol, I completed an Individual Research Project on a topic of my choice. I chose to focus on sensors, specifically exploring how sensor technology can be used to analyse the quality of a person’s squat. One of my primary objectives was to develop a solution that is affordable, accessible, and easy to use for the general public. To achieve this, I opted to use a standard mobile phone in combination with a Human Pose Estimation model. This approach allowed the solution to run directly on a mobile device, provided the user was able to record themselves performing the movement.
Skills
- Machine Learning (TensorFlow)
- Data Processing
- Real-world testing
- Report Writing
Narrated Video
Abstract
The squat is a fundamental exercise performed daily that requires precise technique to avoid injuries.
Traditional methods for learning correct squat form are often costly or lack real-time feedback, limiting their
effectiveness and accessibility. This study addresses these limitations by developing an innovative real-time
analysis system using pose estimation and machine learning technologies. The system employs the MediaPipe
BlazePose model to capture and analyse squat motions frame-by-frame, extracting landmark data that is then
evaluated by a Squat Classification Model (SCM). Based on a 1D convolutional neural network, the SCM
assesses squat quality using three critical metrics: the optimal knee angle between 55° and 65°, correct gaze
direction (forwards or upwards), and spine neutrality. Trained on the synthetic InfiniteForm dataset, the SCM
achieved an overall accuracy of approximately 90% after 50 epochs, with the depth measurement showing
particularly strong performance (average metric of 89.9%). However, the model was less effective at
recognising incorrect gaze and spine alignments, with average metrics of 63.1% and 55.9%, respectively. Realworld testing confirmed these findings, highlighting the model’s robustness in depth classification but
indicating areas for improvement in gaze and spine analysis. Future research will refine the SCM by utilising
a more suitable dataset and extending the model’s application to other exercises and sports, potentially
enhancing training techniques and injury prevention strategies across various physical activities.