Implementing Pressure Sensing Technology for Healthcare Applications

Abstract

The demand for fitness solutions that are accessible, reasonably priced, and privacyfocused is rising, especially for applications that call for remote monitoring. Yoga is popular, but because of its complexity and range of poses that call for exact posture, it can be difficult to practice remotely. In order to categorize and estimate yoga poses without the need for camera-based monitoring, this project presents a novel pressure-sensing matrix mat (Asana mat) made of Velostat material. The real-time pressure distribution patterns recorded by the Asana mat are analyzed using deep learning models, such as convolutional neural network (CNN) and random forest for pose classification, and hybrid convolutional neural network long short-term memory (CNN-LSTM) architecture for pose estimation. The system is trained on diverse datasets collected from different users, poses, and execution styles to increase robustness. The results demonstrate a high classification accuracy of 98.05% for classifying 10 poses, making the system a non-invasive, user-friendly tool for improving yoga practice even for remote applications. Additionally, for pose estimation, a hybrid CNN-LSTM architecture was created, which achieved a root mean square error of 0.062 for a prediction every 10-sequence length (10 frames). As a result, this privacy- preserving system is advantageous in both therapeutic and home settings. Deep learning models and non-intrusive pressure sensors show promise for a variety of applications, such as personalized fitness coaching, quantitative physical rehabilitation, and healthcare monitoring.