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Summary

This summary is machine-generated.

This study introduces a real-time algorithm for detecting repeating patterns in time-series data, like athlete movements. The method accurately identifies intervals of recurrence (IoR) on wearable devices with minimal lag.

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Area of Science:

Data Science
Signal Processing
Wearable Technology

Background:

Repeating patterns, or intervals of recurrence (IoR), are common in time-series data, particularly from wearable sensors tracking athletic activities.
Existing methods for detecting IoR struggle with variations and adjacent repeats in real-world data streams.

Purpose of the Study:

To develop an efficient, online, one-pass, and real-time algorithm for finding and tracking intervals of recurrence (IoR) in time-series data.
To provide a theoretical analysis of IoR behavior and derive fundamental properties for practical application.
To demonstrate the algorithm's robustness and accuracy compared to state-of-the-art techniques.

Main Methods:

An efficient, online, one-pass, real-time algorithm was designed for detecting and tracking intervals of recurrence (IoR).
Theoretical analysis was conducted to understand IoR behavior and derive fundamental properties.
A wearable device was developed to implement and evaluate the algorithm in a user study.

Main Results:

The algorithm achieves high accuracy (over 70% F1-Score) in detecting repeating activities on edge devices.
Real-time performance was demonstrated with a lag of only 1.5 seconds.
The proposed method shows superior accuracy and robustness to signal variations compared to existing state-of-the-art algorithms.

Conclusions:

The developed algorithm offers an efficient and accurate solution for real-time detection of intervals of recurrence (IoR) in time-series data.
The method is robust to variations in repeating patterns, making it suitable for real-world applications like athlete movement analysis.
This approach advances the capabilities of edge devices in analyzing complex time-series data streams.