Summary of Intelligent Repetition Counting For Unseen Exercises: a Few-shot Learning Approach with Sensor Signals, by Yooseok Lim et al.
Intelligent Repetition Counting for Unseen Exercises: A Few-Shot Learning Approach with Sensor Signals
by Yooseok Lim, Sujee Lee
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method develops a deep metric-based few-shot learning approach to automatically count exercise repetitions by analyzing IMU signals. The technique is designed to handle both existing and novel exercises, redefining the counting task as a few-shot classification problem. A Siamese network with triplet loss optimizes the embedding space to distinguish between peak and non-peak frames. Evaluation results demonstrate an 86.8% probability of accurately counting ten or more repetitions within a single set across 28 different exercises. The model’s ability to generalize across various exercise types makes it a strong candidate for real-time implementation in fitness and healthcare applications, particularly in robotics and healthcare where it can automate systems that reflect human movement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to count exercise repetitions is developed using sensors that track human movement. This helps robots and healthcare devices understand when someone is doing an exercise correctly. The system can learn from a few examples of different exercises and then recognize new ones it hasn’t seen before. It’s like teaching a robot or device to count how many times you do a specific move, even if you try something new. |
Keywords
» Artificial intelligence » Classification » Embedding space » Few shot » Probability » Siamese network » Triplet loss
