Method for Extracting "One Strike" from Continuous Acceleration Data
Analysis
This article from Nislab discusses the crucial preprocessing step of isolating individual strikes from continuous motion data, specifically focusing on boxing and mass boxing applications using machine learning. The challenge lies in accurately identifying and extracting a single strike from a stream of data, including continuous actions and periods of inactivity. The article uses 3-axis acceleration data from smartwatches as its primary data source. The core of the article will likely detail the definition of a "single strike" and the methodology employed to extract it from the time-series data, with experimental results to follow. The context suggests a focus on practical application within the field of sports analytics and machine learning.
Key Takeaways
- •The article focuses on the preprocessing of acceleration data for analyzing boxing strikes.
- •The primary challenge is isolating individual strikes from continuous data.
- •The study uses 3-axis acceleration data from smartwatches.
“The most important and difficult preprocessing step when handling striking actions in boxing and mass boxing with machine learning is accurately extracting only one strike from continuous motion data.”