Computer‐Aided Design of Integrated Digital Strain Sensors for Hardware‐Based Recognition and Quantification of Human Movements

• Published in: Advanced Sensor Research Vol. 4; no. 4
• Main Authors: Gasvoda, Hudson, Li, Mengchu, Pader, Andrea, Altay, Rana, Cmager, Nick, Pandey, Tripti, Tseng, Tsun‐Ming, Araci, Ismail Emre
• Format: Journal Article
• Language: English
• Published: Wiley-VCH 01.04.2025
• Subjects: digital strain sensors; electrofluidic switches; microfluidics; wearable sensor design; wearable strain sensors
• ISSN: 2751-1219; 2751-1219
• DOI: 10.1002/adsr.202400146

An integrated strain sensor system that has a unique response to a specific (set of) human movement(s) has the potential to impact various musculoskeletal health tracking applications akin to the step counter's impact on physical activity tracking. It is determined that an open circuit state of a sensor can be used as such a unique response. With this consideration, a digital strain sensor (DigSS) that exhibits a binary (i.e., ON/OFF) response when a threshold strain level is exceeded is developed. The channel geometry dependence of the corner flow in capillaric strain sensors (CSS) resulting in an electrofluidic switch is used. It is demonstrated that through the coalescence and breakup of a liquid meniscus, DigSS operates for hundreds of cycles with a strain limit of detection of 0.0026. To facilitate integration, a linear optimization‐based computer‐aided design tool for the integrated DigSS (iDigSS) is created. Experimental validation shows that the iDigSS distinguishes a target strain‐field profile from 35 of 36 theoretically distinguishable profiles without requiring signal processing. Human subject trials demonstrate the system's ability to differentiate a specific shoulder movement from five others and to wirelessly record wrist extension counts and durations. The digital capillaric strain sensors (DigSSs) that have a binary ON/OFF response to a tunable strain threshold enable tracking of human movements without complex signal processing algorithms for musculoskeletal health applications. The integrated DigSSs are designed by linear optimization‐based analysis of skin strain profiles and tested on human subjects demonstrating the feasibility of the concept.