​Real-time Fitness Assistance via WiFi

Problem

Workers cannot dedicate appropriate time during the day to travel to dedicated exercise places. This results in people either not exercising, or attempting to exercise in an office/home environment. Unfortunately, it is difficult to analyze the form of their exercises without incurring the significant cost of a personal trainer or the discomfort of smart sensors on their person. Not performing exercises correctly could lead to improper muscle strain or other personal injury.

Solution

Our solution is a device-free personalized fitness assistant that analyzes the channel state information of existing WiFi infrastructure. Our system detects four exercises (push-ups, squats, sit-ups/crunches, and curls) and analyzes several factors to assess workout quality and provide a workout review to improve an individual's exercises.

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