Soft actuators produce the mechanical force needed for the functional movements of soft robots, but they suffer from critical drawbacks since previously reported soft actuators often rely on electrical wires or pneumatic tubes for the power supply, which would limit the potential usage of soft robots in various practical applications. In this article, we review the new types of untethered soft actuators that represent breakthroughs and discuss the future perspective of soft actuators. We discuss the functional materials and innovative strategies that gave rise to untethered soft actuators and deliver our perspective on challenges and opportunities for future-generation soft actuators.

Although many people suffer from sleep disorders, most are undiagnosed, leading to impairments in health. The existing polysomnography method is not easily accessible; it's costly, burdensome to patients, and requires specialized facilities and personnel. Here, we report an at-home portable system that includes wireless sleep sensors and wearable electronics with embedded machine learning. We also show its application for assessing sleep quality and detecting sleep apnea with multiple patients. Unlike the conventional system using numerous bulky sensors, the soft, all-integrated wearable platform offers natural sleep wherever the user prefers. In a clinical study, the face-mounted patches that detect brain, eye, and muscle signals show comparable performance with polysomnography. When comparing healthy controls to sleep apnea patients, the wearable system can detect obstructive sleep apnea with an accuracy of 88.5%. Furthermore, deep learning offers automated sleep scoring, demonstrating portability, and point-of-care usability. At-home wearable electronics could ensure a promising future supporting portable sleep monitoring and home healthcare.

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.

Sleep stage classification is an essential process of diagnosing sleep disorders and related diseases. Automatic sleep stage classification using machine learning has been widely studied due to its higher efficiency compared with manual scoring. Typically, a few polysomnography data are selected as input signals, and human experts label the corresponding sleep stages manually. However, the manual process includes human error and inconsistency in the scoring and stage classification. Here, we present a convolutional neural network (CNN)-based classification method that offers highly accurate, automatic sleep stage detection, validated by a public dataset and new data measured by wearable nanomembrane dry electrodes. First, our study makes a training and validation model using a public dataset with two brain signal and two eye signal channels. Then, we validate this model with a new dataset measured by a set of nanomembrane electrodes. The result of the automatic sleep stage classification shows that our CNN model with multi-taper spectrogram pre-processing achieved 88.85% training accuracy on the validation dataset and 81.52% prediction accuracy on our laboratory dataset. These results validate the reliability of our classification method on the standard polysomnography dataset and the transferability of our CNN model for other datasets measured with the wearable electrodes.

Recent studies in functional nanomaterials with advanced macro, micro, and nano-scale structures have yielded substantial improvements in human-interfaced strain sensors for motion and gesture recognition. Furthermore, fundamental advances in nanomaterial printing have been developed and leveraged to translate these materials and mechanical innovations into practical applications. Significant progress in machine learning for human-interfaced strain sensing has unlocked numerous opportunities to improve lives and the human experience through healthcare innovations, sports performance monitoring, and human-machine interfaces. However, several key challenges still must be overcome if strain sensors are to become ubiquitous tools for human motion recognition. This review begins with a summary of the critical strain-sensing mechanisms employed today and how recent works have sought to push their boundaries. It then proceeds to cover the primary functional materials used in wearable strain sensors from a performance and printability perspective. Next is a review of recent advances in nanomaterial printing to produce the complex structures necessary for functional devices. Next, we summarize machine learning approaches for human gesture recognition and the myriad applications and use cases for human-interfaced strain sensors. Finally, it concludes with a discussion of challenges and opportunities for future research in the field.