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.

Achieving large-scale, cost-effective, and reproducible manufacturing of stem cells with the existing devices is challenging. Traditional single-use cell-bag bioreactors, limited by their rigid and single-point sensors, struggle with accuracy and scalability for high-quality cell manufacturing. Here, we introduce a smart bioreactor system that enables multi-spatial sensing for real-time, wireless culture monitoring. This scalable system includes a low-profile, label-free thin-film sensor array and electronics integrated with a flexible cell bag, allowing for simultaneous assessment of culture properties such as pH, dissolved oxygen, glucose, and temperature, to receive real-time feedback for up to 30 days. The experimental results show the accurate monitoring of time-dynamic and spatial variations of stem cells and myoblast cells with adjustable carriers from a plastic dish to a 2-liter cell bag. These advances open up the broad applicability of the smart sensing system for large-scale, lower-cost, reproducible, and high-quality engineered cell manufacturing for broad clinical use.

Recent advancements in printing technologies allow for fabricating various wearable sensors, circuits, and integrated electronics. However, most printing tools have limited ranges of handling ink viscosity, a short working distance, and a limited feature size for developing sophisticated electronics. Here, this paper introduces an all-in-one integrated wearable electronic system via multilayer, multinanomaterial printing. Versatile, high-resolution aerosol-jet printing could successfully print Cu nanoparticles, Ag nanoparticles, PEDOT:PSS, and polyimide (PI) to manufacture nanomembrane composite structures, including skin-contact electrodes and wireless circuits. The printed polymer, PEDOT:PSS deposited on the Cu, ensures biocompatibility when making direct skin contact while enhancing electrical conductivity for electrodes. A self-assembled monolayer facilitates better adhesion of Cu nanoparticles on the PI. Also, using intensive pulsed light, a photonic sintering method minimizes Cu-oxidation while avoiding thermal damage. The combined experimental and computational study shows the mechanical flexibility and reliability of the printed integrated device. With human subjects, the flexible wireless bioelectronic system demonstrates superior performance in detecting high-fidelity physiological signals on the skin, including electromyograms, electrooculograms, electrocardiograms, and motions, proving its potential applications in portable human healthcare and persistent human-machine interfaces.

Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing. This system includes an array of a nanomembrane, laser-induced graphene strain sensors, and flexible electronics integrated with textiles for wireless detection of different body motions and workouts. With multiple human subjects, we demonstrate the system's performance in real-time prediction of eight different activities, including resting, walking, running, squatting, walking upstairs, walking downstairs, push-ups, and jump roping, with an accuracy of 95.3%. The class of technologies, integrated as full body-worn textile electronics and interactive pairing with smartwatches and portable devices, can be used in real-world applications such as ambulatory health monitoring via conjunction with smartwatches and feedback-enabled customized rehabilitation workouts.

Sweat electrolyte analysis using potentiometric systems is a promising approach for continuous health monitoring. However, despite its potential, temperature-induced measurement errors remain a critical challenge, and, to our knowledge, no study has effectively addressed this issue for accurate potentiometric sensing during physiological activities. Here, we present a temperature-compensated flexible microsensor integrated with a wireless potentiometric measurement circuit for real-time sweat analysis. The wearable system features an array of microsensors for simultaneous detection of pH, Na⁺, K⁺, and skin temperature, enabling real-time dynamic temperature compensation. A PEDOT:PSS/graphene ion-to-charge transducer enhances sensitivity through superior electron acceptor properties and an expanded electroactive surface area. The incorporation of a Nafion top layer ensures 2-week-long stability by facilitating selective cation transport while mitigating sensor degradation. With temperature compensation, the wireless wearable device measures an accurate level of electrolytes under extreme temperature variations (8 to 56 °C), including outdoor exercises and exposure to dry saunas, to assess the necessity of temperature correction. This work collectively establishes a robust, high-performance platform for continuous monitoring of sweat biomarkers, thus advancing wearable diagnostic technology for personalized healthcare applications.

Preclinical studies are crucial for developing amyotrophic lateral sclerosis drugs. Current FDA-approved drugs have been created by monitoring limb muscle function and histological analysis of amyotrophic lateral sclerosis model animals. Drug candidates for this disease have yet to be tested for bulbar-onset type due to the limitations of traditional preclinical tools: excessive animal use and discrete detection of disease progress. Here, our study introduces an all-in-one, wireless, integrated wearable system for facilitating continuous drug efficacy assessment of dysphagia-related muscles in animals during natural eating behaviors. By incorporating a kirigami-based strain-isolation mechanism, this device mounted on the skin of animals mitigates electromyography signal contamination caused by unpredictable animal movements. Our findings indicate this system, measuring the progression of motor neuron denervation, offers high precision in monitoring drug effects on dysphagia-responsible bulbar muscles. This study paves the way for more humane and efficient approaches to developing treatment solutions for degenerative neuromuscular diseases.