Soft bioelectronics enable noninvasive, continuous monitoring of physiological signals, essential for precision health care. However, capturing biosignals during physical activity, particularly biomechanical signals like cardiac mechanics, remains challenging due to motion-induced interference. Inspired by starfish’s pentaradial symmetry, we introduce a starfish-like wearable bioelectronic system designed for high-fidelity signal monitoring during movement. The device, featuring five flexible, free-standing sensing arms connected to a central electronic hub, substantially reduces mechanical interference and enables high-fidelity acquisition of cardiac electrical (electrocardiogram) and mechanical (seismocardiogram and gyrocardiogram) signals during motion when coupled with signal compensation and machine learning. Using these three cardiac signal types as inputs, machine learning models deployed on smart devices achieve real-time, high-accuracy (more than 91%) diagnoses of heart conditions such as atrial fibrillation, myocardial infarction, and heart failure. These findings open previously undiscovered avenues by leveraging bioinspired device concepts combined with cutting-edge data science to boost bioelectronic performance and diagnostic precision.

Respiration signals reflect many underlying health conditions, including cardiopulmonary functions, autonomic disorders and respiratory distress, therefore continuous measurement of respiration is needed in various cases. Unfortunately, there is still a lack of effective portable electronic devices that meet the demands for medical and daily respiration monitoring. This work showcases a soft, wireless, and non‐invasive device for quantitative and real‐time evaluation of human respiration. This device simultaneously captures respiration and temperature signatures using customized capacitive and resistive sensors, encapsulated by a breathable layer, and does not limit the user's daily life. Further a machine learning‐based respiration classification algorithm with a set of carefully studied features as inputs is proposed and it is deployed into mobile clients. The body status of users, such as being quiet, active and coughing, can be accurately recognized by the algorithm and displayed on clients. Moreover, multiple devices can be linked to a server network to monitor a group of users and provide each user with the statistical duration of physiological activities, coughing alerts, and body health advice. With these devices, individual and group respiratory health status can be quantitatively collected, analyzed, and stored for daily physiological signal detections as well as medical assistance.

The properly applied pressure between the skin and hemostasis devices is an essential parameter for preventing bleeding and postoperative complications after a transradial procedure. However, this parameter is usually controlled based on the subjective judgment of doctors, which might cause insufficient hemostatic effect or thrombosis. Here this study develops a compact and wireless sensing system for continuously monitoring the pressure applied on the radial artery and wrist skin in clinical practice. A liquid metal (LM)‐based all‐soft pressure sensor is fabricated to enable conformal attachment between the device and skin even under large deformation conditions. The linear sensitivity of 0.007 kPa⁻¹ among the wide pressure range of 0–100 kPa is achieved and the real‐time detection data can be wirelessly transmitted to mobile clients as a reference pressure value. With these devices, detailed pressure data can be collected, analyzed, and stored for medical assistance as well as to improve surgery quality.