​Real-time Fitness Assistance via WiFi

Synopsis

Workers cannot dedicate appropriate time during the day to travel to dedicated exercises places, resulting in a population that either does not exercise, or attempts to exercise at home. Unfortunately, those who work out at home have difficulty analyzing their exercise form, which could result in harmful muscle strain or other personal injury. Personalized trainers, while possibly effective, run a significant cost. Fit-bits cannot measure exercise form, only heart rate and select other things. Smart sensor suites are expensive and have to be attached to a body to be effective, which is uncomfortable, unnatural, and may impede exercise form. Our solution is a device-free personalized fitness assistant that analyzes the channel state information of existing WiFi infrastructure. Our system detects four common exercises (push-ups, squats, sit-ups/crunches, and curls) and analyzes several factors to assess the workout quality for the user.

Tools

We used a TP-LINK router with 2.4GHz and 5GHz frequencies. We used a Dell Laptop with an Ubuntu 14.02 kernel and an Intel WiFi Wireless Link 5300 MIMO radio, also known as the IWL5300. We used the Linux 802.11n CSI tool (created by Daniel Halperin, Wenjun Hu, Anmol Sheth, and David Wetherall), implemented in Matlab and C, to extract the CSI from the channel measurements. For the two-layer deep neural network, we used Python and Tensorflow.

People

Justin Esposito Electrical and Computer Engineering Class of 2022 Rutgers University

Sachin Mathew Electrical and Computer Engineering Class of 2022 Rutgers University

Amit Patel Electrical and Computer Engineering Class of 2022 Rutgers University

Rishika Sakhuja Electrical and Computer Engineering Class of 2023 Rutgers University

Kushaan Misra High School Student Class of 2020 Singapore American School