基于生物运动结构的自监督学习，提升可穿戴设备动作识别效果
Bio-Inspired Self-Supervised Learning for Wrist-worn Accelerometer Data
- 用运动子单元理论将动作片段化为可学习的语义单元
- 在28000小时数据上预训练，6个独立测试集均超越现有方法
- 适合做可穿戴健康监测与低标注数据场景下的动作识别
可穿戴加速度计支持大规模健康监测，但因标注数据稀缺，难以学习鲁棒的人体动作表征。现有自监督学习方法将传感器信号视为无结构的时间序列，忽略了人体运动的潜在生物学结构，而我们认为这一因素对动作识别至关重要。本文提出一种基于运动子单元理论的新型分词策略，该理论认为连续手腕运动由称为子运动的基本函数构成。我们将分词定义为动作片段，即有限序列子运动的可计算单元。通过掩码重建这些分词对Transformer编码器进行预训练，使学习重点从局部波形形态转向高层结构与时间组织。模型在约28,000小时、11,000名参与者的NHANES语料库上预训练，在六个主体无关的动作识别基准上均优于强基线。代码与预训练权重已公开。
原文摘要 · Abstract (English)
Wearable accelerometers enable large-scale health monitoring, yet learning robust human-activity representations has been constrained by scarce labeled data. While self-supervised learning offers a remedy, existing methods treat sensor streams as unstructured time series, overlooking the underlying biological structure of human movement, a factor we argue is critical for effective Human Activity Recognition (HAR). We introduce a novel tokenization strategy grounded in the submovement theory of motor control, which posits that continuous wrist motion is composed of elementary basis functions called submovements. We define our token as the movement segment, a computationally tractable unit of motion composed of a finite sequence of submovements. By pretraining a Transformer encoder via masked reconstruction of these tokens, we shift the learning focus from local waveform morphology to high-level structural and temporal organization. Pretrained on the NHANES corpus (approximately 28k hours; 11k participants), our representations outperform strong wearable SSL baselines across six subject-disjoint HAR benchmarks. Code and pretrained weights are available at https://prithvitarale.github.io/biopm-site/.
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