Computational Design of Physiological Sensors
Aditya, Arshad, Featured Project, Steimle
(Nature Communications)
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.
Our computational design tool enables users to quickly design custom physiological sensing devices with few clicks. This offers a direct, fast, and user-friendly way of setting body dimensions, selecting the modalities the sensor will be able to capture (EMG, ECG, and/or EDA), and selecting specific muscles for EMG sensing. The sensing quality of one or multiple modalities can be easily prioritized by moving a slider. Similarly, the priority of a compact sensor vs. the highest possible sensing quality can be continuously adjusted.
The computational design approach and the optimizer are generic. The generated designs can be implemented with either commercial gel electrodes or dry electrodes fabricated
with conductive materials. We demonstrate two application cases to showcase the benefits of the proposed approach for applications of physiological sensing beyond the medical
field, such as interactive sports devices, gaming, and virtual reality. Applications in these areas benefit from devices that have a small footprint while capturing multiple biosignals. Moreover, they impose high demands on ergonomic wearability to not obstruct body movement.
