Silent Impact: Tracking Tennis Shots from the Passive Arm

TL;DR

Silent Impact introduces a passive arm sensor system for tennis shot analysis, achieving high accuracy and user comfort, offering a less intrusive alternative to traditional racket-based sensors.

Contribution

We developed a neural network-based system that analyzes tennis shots using only passive arm data, demonstrating comparable accuracy to dominant arm methods and improved user comfort.

Findings

Achieved 88.2% classification accuracy

F1 score of 86.0% in shot detection

Participants reported less physical and mental burden

Abstract

Wearable technology has transformed sports analytics, offering new dimensions in enhancing player experience. Yet, many solutions involve cumbersome setups that inhibit natural motion. In tennis, existing products require sensors on the racket or dominant arm, causing distractions and discomfort. We propose Silent Impact, a novel and user-friendly system that analyzes tennis shots using a sensor placed on the passive arm. Collecting Inertial Measurement Unit sensor data from 20 recreational tennis players, we developed neural networks that exclusively utilize passive arm data to detect and classify six shots, achieving a classification accuracy of 88.2% and a detection F1 score of 86.0%, comparable to the dominant arm. These models were then incorporated into an end-to-end prototype, which records passive arm motion through a smartwatch and displays a summary of shots on a mobile app. User study (N=10) showed that participants felt less burdened physically and mentally using Silent Impact on the passive arm. Overall, our research establishes the passive arm as an effective, comfortable alternative for tennis shot analysis, advancing user-friendly sports analytics.