Wireless Flexible Potentiometric Microsensors for Temperature-Compensated Sweat Electrolyte Monitoring.

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.

Full Body-Worn Textile-Integrated Nanomaterials and Soft Electronics for Real-Time Continuous Motion Recognition Using Cloud Computing.

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.

Smart filtering facepiece respirator with self-adaptive fit and wireless humidity monitoring.

The widespread emergence of airborne diseases has transformed our lifestyle, and respirators have become an essential part of daily life. Nevertheless, finding respirators that fit well can be challenging due to the variety of human facial sizes and shapes, potentially compromising protection. In addition, the current respirators do not inform the user of the air quality in case of continuous long-term use.

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.

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.

Untethered soft actuators for soft standalone robotics.

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.

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications.

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications.

Printed Strain Sensors for Motion Recognition: A Review of Materials, Fabrication Methods, and Machine Learning Algorithms.

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.

At-home wireless sleep monitoring patches for the clinical assessment of sleep quality and sleep apnea.

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.

Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes.

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.