Name
Title | ||
|---|---|---|
An Online Unsupervised Dynamic Window Method to Track Repeating Patterns From Sensor Data |
Abstract | ||
|---|---|---|
Short bursts of repeating patterns [intervals of recurrence (IoR)] manifest themselves in many applications, such as in the time-series data captured from an athlete’s movements using a wearable sensor while performing exercises. We present an efficient, online, one-pass, and real-time algorithm for finding and tracking IoR in a time-series data stream. We provide a detailed theoretical analysis of the behavior of any IoR and derive fundamental properties that can be used on real-world data streams. We show that why our method, unlike current state-of-the-art techniques, is robust to variations in repeats of the same pattern adjacent to each other. To evaluate our algorithm, we build a wearable device that runs our algorithm to conduct a user study. Our results show that our algorithm can detect intervals of repeating activities on edge devices with high accuracy (over 70%
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-Score) and in a real-time environment with only a 1.5-s lag. Our experimental results from real-world datasets demonstrate that our approach outperforms state-of-the-art algorithms in both accuracy and robustness to variations of the signal of recurrence. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCYB.2020.3027714 | IEEE Transactions on Cybernetics |
Keywords | DocType | Volume |
Algorithms,Exercise Therapy,Humans,Movement,Time Factors,Wearable Electronic Devices | Journal | 52 |
Issue | ISSN | Citations |
6 | 2168-2267 | 0 |
PageRank | References | Authors |
0.34 | 26 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yousef Kowsar | 1 | 0 | 0.34 |
| Masud Moshtaghi | 2 | 195 | 15.96 |
| Eduardo Velloso | 3 | 0 | 0.34 |
| Christopher Leckie | 4 | 2422 | 155.20 |
| Lars Kulik | 5 | 1544 | 101.97 |