This review summarizes recent progress in developing wireless, batteryless, fully implantable biomedical devices for real-time continuous physiological signal monitoring, focusing on advancing human health care. Design considerations, such as biological constraints, energy sourcing, and wireless communication, are discussed in achieving the desired performance of the devices and enhanced interface with human tissues. In addition, we review the recent achievements in materials used for developing implantable systems, emphasizing their importance in achieving multi-functionalities, biocompatibility, and hemocompatibility. The wireless, batteryless devices offer minimally invasive device insertion to the body, enabling portable health monitoring and advanced disease diagnosis. Lastly, we summarize the most recent practical applications of advanced implantable devices for human health care, highlighting their potential for immediate commercialization and clinical uses.

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Here, we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities, which are simultaneously computed to determine the user’s intended movement. Cloud-based deep learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 500–550 ms response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87 mm at maximum. The intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.

Continuous monitoring of physiological signals from the human body is critical in health monitoring, disease diagnosis, and therapeutics. Despite the needs, the existing wearable medical devices rely on either bulky wired systems or battery-powered devices needing frequent recharging. Here, we introduce a wearable, self-powered, thermoelectric flexible system architecture for wireless portable monitoring of physiological signals without recharging batteries. This system harvests an exceptionally high open circuit voltage of 175-180 mV from the human body, powering the wireless wearable bioelectronics to detect electrophysiological signals on the skin continuously. The thermoelectric system shows long-term stability in performance for 7 days with stable power management. Integrating screen printing, laser micromachining, and soft packaging technologies enables a multilayered, soft, wearable device to be mounted on any body part. The demonstration of the self-sustainable wearable system for detecting electromyograms and electrocardiograms captures the potential of the platform technology to offer various opportunities for continuous monitoring of biosignals, remote health monitoring, and automated disease diagnosis.

Sensory rehabilitation in pediatric patients with traumatic spinal cord injury is challenging due to the ongoing development of their nervous systems. However, these sensory problems often result in nonuse of the impaired limb, which disturbs impaired limb rehabilitation and leads to overuse of the contralateral limb and other physical or psychological issues that may persist. Here, we introduce a soft nanomembrane sensor-enabled wearable glove system that wirelessly delivers a haptic sensation from the hand with tactile feedback responses for sensory impairment assistance. The smart glove system uses gold nanomembranes, copper-elastomer composites, and laser-induced graphene for the sensitive detection of pressure, temperature, and strain changes. The nanomaterial sensors are integrated with low-profile tactile actuators and wireless flexible electronics to offer real-time sensory feedback. The wearable system's thin-film sensors demonstrate 98% and 97% accuracy in detecting pressure and finger flexion, respectively, along with a detection coverage of real-life temperature changes as an effective rehabilitation tool. Collectively, the upper-limb sensory impairment assistance system embodies the latest in soft materials and wearable technology to incorporate soft sensors and miniaturized actuators and maximize its compatibility with human users, offering a promising solution for patient sensory rehabilitation.

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical‐grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami‐structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high‐quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post‐stroke patients captures the system's significance in measuring multiple physiological signals in real‐time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non‐invasive alternative for monitoring swallowing and aspiration events.

Recent advances in wearable technologies have enabled ways for people to interact with external devices, known as human-machine interfaces (HMIs). Among them, electrooculography (EOG), measured by wearable devices, is used for eye movement-enabled HMI. Most prior studies have utilized conventional gel electrodes for EOG recording. However, the gel is problematic due to skin irritation, while separate bulky electronics cause motion artifacts. Here, we introduce a low-profile, headband-type, soft wearable electronic system with embedded stretchable electrodes, and a flexible wireless circuit to detect EOG signals for persistent HMIs. The headband with dry electrodes is printed with flexible thermoplastic polyurethane. Nanomembrane electrodes are prepared by thin-film deposition and laser cutting techniques. A set of signal processing data from dry electrodes demonstrate successful real-time classification of eye motions, including blink, up, down, left, and right. Our study shows that the convolutional neural network performs exceptionally well compared to other machine learning methods, showing 98.3% accuracy with six classes: the highest performance till date in EOG classification with only four electrodes. Collectively, the real-time demonstration of continuous wireless control of a two-wheeled radio-controlled car captures the potential of the bioelectronic system and the algorithm for targeting various HMI and virtual reality applications.