tinyML Asia 2022 Video Poster: tinyRadar for fitness: A radar-based contactless activity tracker…

tinyRadar for fitness: A radar-based contactless activity tracker for edge computing
Satyapreet SINGH, Masters Student, NeuRonICS Lab, Indian Institute of Science

xercising regularly is essential for maintaining a healthy lifestyle. Studies have shown that knowing parameters such as calories burnt, step counts, and miles travelled helps track fitness goals and maintain motivation [1]. Modern fitness trackers aim to provide these metrics in real-time, with features like ubiquitous connectivity and lightweight. Most fitness trackers are either wearables or camera-based. Wearable fitness trackers can cause discomfort during exercise, and exchanging users’ data over the internet poses a privacy threat [2] [3]. The camera-based fitness trackers also often pose a privacy risk. Certain works prevent this by hiding the face of users through complex algorithms, adding to the computation cost requiring high-end processing computers to make readings available in real-time [4]. We propose tinyRadar as a contactless fitness tracker which identifies the exercise performed by the user and computes its repetition counts. It comes in a small form factor and preserves the user’s privacy as it provides point cloud data, making it a suitable choice for a fitness tracker in a smart home setting.

As can be seen from Figure 1, it comprises a Texas Instruments (TI) IWR1843 mmWave radar board as a sensing and processing modality and an ESP32 module for results transmission to the user’s smartphone through Bluetooth Low Energy (BLE). The mmWave radar processes the information received from the target environment to create a Velocity-Time map which contains unique signatures for different human activities. A three-layered Convolutional Neural Network (CNN) deployed on the radar board classifies the exercise performed by the user with the Velocity-Time map as input in one of the following eight categories: Crossover toe touch, Crunches, Jogging, Lateral squats, Lunges, Hand rotation, Squats, and Rest. The repetition count algorithm runs parallelly on board to compute the repetition counts. It provides a real-time subject-independent classification accuracy of 97% and repetition counts with an accuracy of ±4 counts.

References:

https://www.runnersworld.com/news/a40774442/fitness-trackers-help-you-move-more-study/
Fitnessarmbaender – Nur zwei von zwoelf sind gut, test.de, December 27, 2015. Retrieved on

January 6, 2016
Sly, Liz (29 January 2018). ”U.S. soldiers are revealing sensitive and dangerous information by

jogging”. The Washington Post. Retrieved 29 January 2018.
Rushil Khurana, Karan Ahuja, Zac Yu, Jennifer Mankoff, Chris Harrison, and Mayank Goel. 2018.

GymCam: Detecting, Recognizing and Tracking Simultaneous Exercises in Unconstrained Scenes. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 4, Article 185 (December 2018), 17 pages. https://doi.org/10.1145/3287063

Figure 1: tinyRadar as a contactless fitness tracker. (a) IWR1843 mmWave radar board and ESP32 integrated inside a housing (b) Breakout view of the tinyRadar assembly where the radar board and ESP32 are connected using an interface board (c) Functional block diagram showing the RF front end, radar signal processing and classification module along with the result transmission module comprising of ESP32. The results are transmitted over BLE to a smartphone.

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