MoPFormer: Motion-Primitive Transformer for Wearable-Sensor Activity Recognition

• URL: http://arxiv.org/abs/2505.20744v2
• Date: Tue, 28 Oct 2025 03:32:37 GMT
• Title: MoPFormer: Motion-Primitive Transformer for Wearable-Sensor Activity Recognition
• Authors: Hao Zhang, Zhan Zhuang, Xuehao Wang, Xiaodong Yang, Yu Zhang,
• Abstract summary: Motion-Primitive Transformer (MoPFormer) is a novel framework that enhances interpretability by tokenizing inertial measurement unit signals into meaningful motion primitives.<n>MoPFormer can be pre-trained using a masked motion-modeling objective that reconstructs missing primitives.<n>Experiments on six HAR benchmarks demonstrate that MoPFormer not only outperforms state-of-the-art methods but also successfully generalizes across multiple datasets.
• Score: 16.764919107712146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human Activity Recognition (HAR) with wearable sensors is challenged by limited interpretability, which significantly impacts cross-dataset generalization. To address this challenge, we propose Motion-Primitive Transformer (MoPFormer), a novel self-supervised framework that enhances interpretability by tokenizing inertial measurement unit signals into semantically meaningful motion primitives and leverages a Transformer architecture to learn rich temporal representations. MoPFormer comprises two stages. The first stage is to partition multi-channel sensor streams into short segments and quantize them into discrete ``motion primitive'' codewords, while the second stage enriches those tokenized sequences through a context-aware embedding module and then processes them with a Transformer encoder. The proposed MoPFormer can be pre-trained using a masked motion-modeling objective that reconstructs missing primitives, enabling it to develop robust representations across diverse sensor configurations. Experiments on six HAR benchmarks demonstrate that MoPFormer not only outperforms state-of-the-art methods but also successfully generalizes across multiple datasets. More importantly, the learned motion primitives significantly enhance both interpretability and cross-dataset performance by capturing fundamental movement patterns that remain consistent across similar activities, regardless of dataset origin.

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