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Self-Adaptive Motion Tracking against On-body Displacement of Flexible Sensors

Chengxu Zuo · Fang Jiawei · Shihui Guo · Yipeng Qin

Great Hall & Hall B1+B2 (level 1) #1014
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Tue 12 Dec 3:15 p.m. PST — 5:15 p.m. PST


Flexible sensors are promising for ubiquitous sensing of human status due to their flexibility and easy integration as wearable systems. However, on-body displacement of sensors is inevitable since the device cannot be firmly worn at a fixed position across different sessions. This displacement issue causes complicated patterns and significant challenges to subsequent machine learning algorithms. Our work proposes a novel self-adaptive motion tracking network to address this challenge. Our network consists of three novel components: i) a light-weight learnable Affine Transformation layer whose parameters can be tuned to efficiently adapt to unknown displacements; ii) a Fourier-encoded LSTM network for better pattern identification; iii) a novel sequence discrepancy loss equipped with auxiliary regressors for unsupervised tuning of Affine Transformation parameters.

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