uLift: Adaptive Workout Tracker Using a Single Wrist-Worn Accelerometer

Abstract

The emergence of wearable devices has motivated people to actively log their daily exercise routines using smart apps. However, most current exercise trackers focus on aerobic exercises, and thus provide limited functionality for tracking and analyzing anaerobic workouts involving complex and repetitive movements. To fill this gap, we developed uLift, an adaptive workout tracker that uses only a single wrist-worn accelerometer and has four main functions: workout detection, repetition counting, workout classification, and quality assessment. First, uLift detects a binary workout state from continuous signals using the weighted sum of autocorrelation. Second, repetition counting is conducted by filtering out unwanted peaks. Third, the segments of a workout are used to generate a representative template for workout classification using the distances calculated from dynamic time warping. Finally, to assess workout quality, the form score is computed by evaluating the consistency across repetitions. As uLift does not require a training process, it can easily add new workouts or delete existing ones using an instant adaptation process. For the evaluation of uLift, we collected a multi-joint workout dataset comprising 15 workouts from 35 participants in a gymnasium. To allow for natural and individual variability, we provided the participants with minimum instructions. The dataset was open-sourced to facilitate future research on anaerobic workout analysis. As a result, uLift achieved 93.09% accuracy for workout detection, mean counting error of 0.61, and classification accuracy of 90.06%. The form score was significantly different among the three subgroups of participants, divided by workout experience.

Keywords

• Anaerobic workout
• autocorrelation
• classification algorithms
• dynamic time warping
• inertial measurement unit
• quality assessment