Assessing Lower Limb Strength using Internet-of-Things Enabled Chair
This is an incremental application of existing IoT and ML technologies to monitor rehabilitation progress for individuals in therapy.
The paper tackles the problem of assessing lower limb strength in rehabilitation by using IoT-enabled chairs with pressure sensors to collect data during sit-to-stand and stand-to-sit transitions, processing it with machine learning models to estimate strength and weakness, but no concrete numbers are provided for results.
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.