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

• URL: http://arxiv.org/abs/2209.04042v3
• Date: Tue, 27 Aug 2024 17:24:51 GMT
• Title: Assessing Lower Limb Strength using Internet-of-Things Enabled Chair
• Authors: Chelsea Yeh, Hanna Kaitlin Dy, Phillip Schodinger, Hudson Kaleb Dy,
• Abstract summary: The project seeks to measure and assess the progress of individuals by sensors attached to chairs. Pressure sensors are attached to various locations on a chair, including but not limited to the seating area, backrest, hand, rests, and legs. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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