Abstract
Electro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions.
Highlights
Electro-physiological sensing devices are becoming increasingly common in diverse applications
Traditional manual placement of electro-physiological sensing electrodes relies on placing electrodes at specific locations, usually called keypoints, following a set of heuristic rules and placement guides presented in literature[9,15,16,17,18]
The results reported in this article demonstrate the feasibility and effectiveness of computationally designing and optimizing multimodal electrophysiological sensor layouts
Summary
Electro-physiological sensing devices are becoming increasingly common in diverse applications Designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Prior work has contributed epidermal sensors for capturing biosignals of various modalities, such as muscle movements using electromyogram (EMG)[2], cardiac activity using electrocardiogram (ECG)[6], or the electrical activity of the brain using electroencephalogram (EEG)[7] This new class of highly ergonomic devices promises to make electrophysiological sensing more widespread and opens up highly relevant new avenues in diverse fields, comprising wearable computing, augmented and virtual reality, entertainment computing, and human–machine interaction. We propose a computational design approach to tackle this problem (Fig. 1) It automates the design of electrode layouts for epidermal electrophysiological sensors that can sense biosignals of one or multiple modalities. We show that an optimization approach can be employed for creating compact wearable devices that can measure multiple biosignal modalities
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