  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.
