Locally Controlled Sensing Properties of Stretchable Pressure Sensors Enabled by Micro-Patterned Piezoresistive Device Architecture.

For wearable health monitoring systems and soft robotics, stretchable/flexible pressure sensors have continuously drawn attention owing to a wide range of potential applications such as the detection of human physiological and activity signals, and electronic skin (e-skin). Here, we demonstrated a highly stretchable pressure sensor using silver nanowires (AgNWs) and photo-patternable polyurethane acrylate (PUA). In particular, the characteristics of the pressure sensors could be moderately controlled through a micro-patterned hole structure in the PUA spacer and size-designs of the patterned hole area. With the structural-tuning strategies, adequate control of the site-specific sensitivity in the range of 47~83 kPa-1 and in the sensing range from 0.1 to 20 kPa was achieved. Moreover, stacked AgNW/PUA/AgNW (APA) structural designed pressure sensors with mixed hole sizes of 10/200 µm and spacer thickness of 800 µm exhibited high sensitivity (~171.5 kPa-1) in the pressure sensing range of 0~20 kPa, fast response (100~110 ms), and high stretchability (40%). From the results, we envision that the effective structural-tuning strategy capable of controlling the sensing properties of the APA pressure sensor would be employed in a large-area stretchable pressure sensor system, which needs site-specific sensing properties, providing monolithic implementation by simply arranging appropriate micro-patterned hole architectures.

Wide-Range Motion Recognition Through Insole Sensor Using Multi-Walled Carbon Nanotubes and Polydimethylsiloxane Composites.

High linearity/sensitivity and a wide dynamic sensing range are the most desirable features for pressure sensors to accurately detect and respond to external pressure stimuli. Even though a number of recent studies have demonstrated a low-cost pressure sensing device for a smart insole system by using scalable and deformable conductive materials, they still lack stretchability and desirable properties such as high sensitivity, hysteresis, linearity, and fast response time to obtain accurate and reliable data. To resolve this issue, a flexible and stretchable piezoresistive pressure sensor with high linear response over a wide pressure range is developed and integrated in a wearable insole system. The sensor uses multi-walled carbon nanotubes and polydimethylsiloxane (MWCNT/PDMS) composites with gradient density double-stacked configuration as well as randomly distributed surface microstructure (RDSM). The randomly distributed surface of the MWCNT/PDMS composite is easily and non-artificially generated by the evaporation of residual IPA solvent during a composite curing process. Due to two functional features consisting of the double-stacked composite configuration with different gradient MWCNT density and RDSM, the pressure sensor shows high linear sensitivity (∼82.5 kPa) and a pressure range of 0-1 MPa, providing extensive potential applications in monitoring human motions. Moreover, for a practical wearable application detecting the user's real-time motions, a custom-designed output signal acquisition system has been developed and integrated with the insole pressure sensor. As a result, the insole sensor can successfully detect walking, running, and jumping movements and can be used in daily life to monitor gait patterns by virtue of its long-term stability.

A Behavior-Learned Cross-Reactive Sensor Matrix for Intelligent Skin Perception.

Mimicking human skin sensation such as spontaneous multimodal perception and identification/discrimination of intermixed stimuli is severely hindered by the difficulty of efficient integration of complex cutaneous receptor-emulating circuitry and the lack of an appropriate protocol to discern the intermixed signals. Here, a highly stretchable cross-reactive sensor matrix is demonstrated, which can detect, classify, and discriminate various intermixed tactile and thermal stimuli using a machine-learning approach. Particularly, the multimodal perception ability is achieved by utilizing a learning algorithm based on the bag-of-words (BoW) model, where, by learning and recognizing the stimulus-dependent 2D output image patterns, the discrimination of each stimulus in various multimodal stimuli environments is possible. In addition, the single sensor device integrated in the cross-reactive sensor matrix exhibits multimodal detection of strain, flexion, pressure, and temperature. It is hoped that his proof-of-concept device with machine-learning-based approach will provide a versatile route to simplify the electronic skin systems with reduced architecture complexity and adaptability to various environments beyond the limitation of conventional "lock and key" approaches.