Abstract 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.
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Integration of capillaric strain sensors toward recognition of human movements
Capillaric strain sensors (CSSs) operate based on the volume expansion of closed microfluidic networks in response to linear strain and have tunable directionality and sensitivity in a large range. The unique advantages of CSSs for integrated sensor development can simplify the human movement recognition by eliminating the need for intensive computational power and reliance on machine learning algorithms. We borrowed strategies from electrical digital circuits for the integration of CSSs in OR and AND configurations. We have fabricated devices according to these strategies. To validate their functionality, we first performed tests on a benchtop model. We have mapped the strain field on the sensors using digital image correlation and used it in combination with a mathematical procedure that we have developed to accurately predict the response of the integrated CSSs (iCSSs). Finally, we have skin mounted the iCSS patches (2 × 2 cm 2 ) and conducted tests on a human subject. The results demonstrate that skin-strain-field mapping will be an enabling tool for iCSS design toward the recognition of human movements.
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- Award ID(s):
- 2045087
- PAR ID:
- 10442727
- Date Published:
- Journal Name:
- Sensors & Diagnostics
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2635-0998
- Page Range / eLocation ID:
- 212 to 224
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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