Assessing Lower Limb Strength using Internet-of-Things Enabled Chair

TL;DR

This paper presents a system that uses IoT-enabled pressure sensors and machine learning to assess lower limb strength during rehabilitation by analyzing pressure and motion data from a specially equipped chair.

Contribution

It introduces a novel integration of IoT sensors and machine learning for real-time lower limb strength assessment during sit-to-stand movements.

Findings

Successful data collection from pressure sensors during movements

Effective machine learning model estimates strength variations

Potential for remote rehabilitation monitoring

Abstract

This project describes the application of the technologies of Machine Learning and Internet-of-Things to assess the lower limb strength of individuals undergoing rehabilitation or therapy. Specifically, it seeks to measure and assess the progress of individuals by sensors attached to chairs and processing the data through Google GPU Tensorflow CoLab. Pressure sensors are attached to various locations on a chair, including but not limited to the seating area, backrest, hand rests, and legs. Sensor data from the individual performing both sit-to-stand transition and stand-to-sit transition provides a time series dataset regarding the pressure distribution and vibratory motion on the chair. The dataset and timing information can then be fed into a machine learning model to estimate the relative strength and weakness during various phases of the movement.