Machine-learning-based posture identification from dynamic seat sensors

Sitting for long periods of time has negative health effects that could be solved by changing posture throughout the day. The solution lies in the use of sit-stand desks, active seats, and automated reminders to change position. For this purpose, this project focuses on developing software that can intelligently determine a person’s posture using sensors located in a dynamic seat. Data will be collected from people using the Formid Dynamic Seat, which will then be used to develop and test machine learning algorithms. The results will benefit the industry partner in that they will be able to incorporate the resulting software into a mobile app that can let the person know that they need to move.

Intern: 
Ghazal Farhani
Faculty Supervisor: 
Ana Luisa Trejos
Province: 
Ontario
University: 
Partner: 
Partner University: 
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