Monitoring biophysical signals such as body or organ movements and other physical phenomena is necessary for patient rehabilitation. However, stretchable flexible pressure sensors with high sensitivity and a broad range that can meet these requirements are still lacking. Herein, we successfully monitored various vital biophysical features and implemented in-sensor dynamic deep learning for knee rehabilitation using an ultrabroad linear range and high-sensitivity stretchable iontronic pressure sensor (SIPS). We optimized the topological structure and material composition of the electrode to build a fully stretching on-skin sensor. The high sensitivity (12.43 kPa−1), ultrabroad linear sensing range (1 MPa), high pressure resolution (6.4 Pa), long-term durability (no decay after 12000 cycles), and excellent stretchability (up to 20%) allow the sensor to maintain operating stability, even in emergency cases with a high sudden impact force (near 1 MPa) applied to the sensor. As a practical demonstration, the SIPS can positively track biophysical signals such as pulse waves, muscle movements, and plantar pressure. Importantly, with the help of a neuro-inspired fully convolutional network algorithm, the SIPS can accurately predict knee joint postures for better rehabilitation after orthopedic surgery. Our SIPS has potential as a promising candidate for wearable electronics and artificial intelligent medical engineering owing to its unique high signal-to-noise ratio and ultrabroad linear range.

Flexible pressure sensors play an important role in flexible robotics, human-machine interaction (HMI), and human physiological information. However, most of the reported flexible pressure sensors suffer from a highly nonlinear response and a significant decrease in sensitivity at high pressures. Herein, we propose a flexible novel iontronic pressure sensor based on monolayer molybdenum disulfide (MoS2). Based on the unique structure and the excellent mechanical properties as well as the large intercalation capacitance of MoS2, the prepared sensor holds an ultra-high sensitivity (Smax = 89.75 kPa−1) and a wide sensing range (722.2 kPa). Further, the response time and relaxation time of the flexible sensor are only 3 ms, respectively, indicating that the device can respond to external pressure rapidly. In addition, it shows long-term cycling stability (over 5000 cycles with almost no degradation) at a high pressure of 138.9 kPa. Finally, it is demonstrated that the sensor can be used in physiological information monitoring and flexible robotics. It is anticipated that our prepared sensor provide a reliable approach to advance the theory and practicality of the flexible sensor electronics.

amphibious vehicle for water resistance are complex and difficult to accurately estimate oh, analyzing the force characteristics affecting water sailing by sailing amphibious vehicle posture resistance characteristics of the vehicle. Amphibian Vehicle analyze the composition of the water resistance, friction analysis and calculation methods, shape resistance and wave resistance were studied.

Amphibious vehicle shape changing shape structure, compared with the hull, its water resistance factors more and more computationally complex. Calculated using the traditional theory it is difficult to accurately estimate their water resistance force and distribution. In this paper, the water resistance FLUENT software simulation analysis can more accurately calculate and analyze the water Amphibious Vehicle force distribution. This paper has established amphibious vehicle CFD models, set the environmental parameters and boundary conditions, the amphibious vehicle water resistance characteristics were simulated. Amphibious vehicles obtained two-phase distribution of water vapor, the body and the body surface pressure distribution free surface profile, the final calculation of the amphibious vehicle water resistance values.