Computational design and optimization of electro-physiological sensors

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.

A Wearable Master–Slave Rehabilitation Robot Based on an Epidermal Array Electrode Sleeve and Multichannel Electromyography Network

“…To construct the EMG network, according to the muscles related to hand movement reported in previous studies, [ 24 ] the 12 positions of the EMG sensor were regarded as the network nodes, including the flexor support band (FSB), proximal flexor digitorum (PFD), flexor digitorum (FD), flexor digitorum superficialis (FDS), flexor carpi ulnaris (FCU), flexor carpi radialis (FCR), extensor support band (ESB), proximal extensor digitorum (PED), extensor digitorum (ED), extensor digiti minimi (EDM), extensor carpi ulnaris (ECU), and extensor carpus radialis (ECR). Then, coherence is used to measure the interaction strength between different nodes.…”

Rapid Self-Healing Hydrogel with Ultralow Electrical Hysteresis for Wearable Sensing

“…The captured ECG, EMG, and EOG signals effectively displayed the P, Q, R, S, and T waves of cardiac activity, neuromuscular activities, and eye movements. 69,70 There were no significant quantitative differences between the measurements taken with the hydrogel electrodes before and after the healing process. Real-time demonstrations showcasing these applications are shown in Movie S4.…”

Artificial Intelligence and Biosensors in Healthcare and its Clinical Relevance: A Review

“…In addition to all these de-599 velopments, computational tools and AI-assisted approaches 600 are being actively explored to automate and customize the 601 design of biosensing wearables. For instance, Nittala et al [97] 602 developed a computational design tool built with an inte-603 grated predictive model to optimize the design of multi-modal 604 electro-physiological sensing devices. (d) AI-assisted fabrication and optimization of multi-modal electro-physiological sensing devices [97].…”

“…For instance, Nittala et al [97] 602 developed a computational design tool built with an inte-603 grated predictive model to optimize the design of multi-modal 604 electro-physiological sensing devices. (d) AI-assisted fabrication and optimization of multi-modal electro-physiological sensing devices [97]. (e) Ultra-thin and skin-conformable strain sensors on a decal transfer substrate, employed to detect subtle human body movements [98].…”