However, on-body displacement of sensors is inevitable since the device cannot be firmly worn at a fixed position across different sessions.

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.

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.

Imagine owning a smartphone or TV that could roll up to fit in your pocket. That could soon be reality thanks a new flexible sensor that can detect subtle differences in touch, including swiping and tapping. Researchers said their stretchable sensor could be used to build folding TV screens and tablets - and may even be used to make skin for robots. The team from the University of British Columbia in Vancouver used a highly conductive gel sandwiched between layers of silicone to make their bendable sensor. To create the sensor, a gel is poured out and combined with silicon-based materials that are stretchy and transparent.