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The capability to record data in passive, image-based wearable sensors can simplify data readouts and eliminate the requirement for the integration of electronic components on the skin. Here, we developed a skin-strain-actuated microfluidic pump (SAMP) that utilizes asymmetric aspect ratio channels for the recording of human activity in the fluidic domain. An analytical model describing the SAMP’s operation mechanism as a wearable microfluidic device was established. Fabrication of the SAMP was achieved using soft lithography from polydimethylsiloxane (PDMS). Benchtop experimental results and theoretical predictions were shown to be in good agreement. The SAMP was mounted on human skin and experiments conducted on volunteer subjects demonstrated the SAMP’s capability to record human activity for hundreds of cycles in the fluidic domain through the observation of a stable liquid meniscus. Proof-of-concept experiments further revealed that the SAMP could quantify a single wrist activity repetition or distinguish between three different shoulder activities. 

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. 

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.