Intelligent Repetition Counting for Unseen Exercises: A Few-Shot Learning Approach with Sensor Signals

• URL: http://arxiv.org/abs/2410.00407v2
• Date: Wed, 9 Oct 2024 06:37:36 GMT
• Title: Intelligent Repetition Counting for Unseen Exercises: A Few-Shot Learning Approach with Sensor Signals
• Authors: Yooseok Lim, Sujee Lee,
• Abstract summary: This study develops a method to automatically count exercise repetitions by analyzing IMU signals. We propose a repetition counting technique utilizing a deep metric-based few-shot learning approach. We show an 86.8% probability of accurately counting ten or more repetitions within a single set across 28 different exercises.
• Score: 0.4998632546280975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sensing technology has significantly advanced in automating systems that reflect human movement, particularly in robotics and healthcare, where it is used to automatically detect target movements. This study develops a method to automatically count exercise repetitions by analyzing IMU signals, with a focus on a universal exercise repetition counting task that counts all types of exercise movements, including novel exercises not seen during training, using a single model. Since peak patterns can vary significantly between different exercises as well as between individuals performing the same exercise, the model needs to learn a complex embedding space of sensor data to generalize effectively. To address this challenge,we propose a repetition counting technique utilizing a deep metric-based few-shot learning approach, designed to handle both existing and novel exercises. By redefining the counting task as a few-shot classification problem, the method is capable of detecting peak repetition patterns in exercises not seen during training. The approach employs a Siamese network with triplet loss, optimizing the embedding space to distinguish between peak and non-peak frames. Evaluation results demonstrate the effectiveness of the proposed approach, showing an 86.8% probability of accurately counting ten or more repetitions within a single set across 28 different exercises. This performance highlights the model's ability to generalize across various exercise types, including those not present in the training data. Such robustness and adaptability make the system a strong candidate for real-time implementation in fitness and healthcare applications.

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