Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.

Electronic skin (e-skin) has attracted tremendous interest due to its diverse potential applications, including in physiological signal detection, health monitoring, and artificial throats. However, the major drawbacks of traditional e-skin are the weak adhesion of substrates, incompatibility between sensitivity and stretchability, and its single function. These shortcomings limit the application of e-skin and increase the complexity of its multifunctional integration. Herein, the synergistic network of crosslinked SWCNTs within and between multilayered graphene layers was directly drip coated onto the PU thin film with self-adhesion to fabricate versatile e-skin. The excellent mechanical properties of prepared e-skin arise from the sufficient conductive paths guaranteed by SWCNTs in small and large deformation under various strains. The prepared e-skin exhibits a low detection limit, as small as 0.5% strain, and compatibility between sensitivity and stretchability with a gauge factor (GF) of 964 at a strain of 0–30%, and 2743 at a strain of 30–60%. In physiological signals detection application, the e-skin demonstrates the detection of subtle motions, such as artery pulse and blinking, as well as large body motions, such as knee joint bending, elbow movement, and neck movement. In artificial throat application, the e-skin integrates sound recognition and sound emitting and shows clear and distinct responses between different throat muscle movements and different words for sound signal acquisition and recognition, in conjunction with superior sound emission performance with a sound spectrum response of 71 dB (f = 12.5 kHz). Overall, the presented comprehensive study of novel materials, structures, properties, and mechanisms offers promising potential in physiological signals detection and artificial throat applications.

Surface electromyogram (sEMG)-based gesture recognition has emerged as a promising avenue for developing intelligent prostheses for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. By using cross-session calibration and increasing the amount of training data, it is possible to reduce these variations. The impact of varying the amount of calibration and training data on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of the amount of training data on benchmark performance. Two amputees who had undergone amputations years prior were recruited, and seven sessions of data were collected for analysis from each of them. Ninapro DB6, a publicly available database containing data from ten healthy subjects across ten sessions, was also included in this study. The experimental results show that the calibration data improved the average accuracy by 3.03%, 6.16%, and 9.73% for the two subjects and Ninapro DB6, respectively, compared to the baseline results. Moreover, it was discovered that increasing the number of training sessions was more effective in improving accuracy than increasing the number of trials. Three potential strategies are proposed in light of these findings to enhance cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, while also offering effective strategies to maximize the utilization of the entire dataset.

A miniature Fourier transform spectrometer is proposed using a thin-film lithium niobate electro-optical modulator instead of the conventional modulator made by titanium diffusion in lithium niobate. The modulator was fabricated by a contact lithography process, and its voltage-length and optical waveguide loss were 2.26 V·cm and 1.01 dB/cm, respectively. Based on the wavelength dispersion of the half-wave voltage of the fabricated modulator, the emission spectrum of the input signal was retrieved by Fourier transform processing of the interferogram, and the analysis of the experimental data of monochromatic light shows that the proposed miniaturized FTS can effectively identify the input signal wavelength.

Vibration sensors play a crucial role in resource exploration, structural health monitoring, and seismic activity detection. Conventional vibration sensors, primarily including electromagnetic, capacitive, piezoelectric, and fiber optic types, have been predominantly utilized in past vibration detection applications. However, with the development of deep-sea resources, vibration detection in such extreme environments demands sensors with low-frequency response, low noise, and high environmental resilience, requirements which are challenging for conventional sensors to meet. Due to their high sensitivity and low noise in the low-frequency domain, electrochemical vibration sensors have garnered increasing attention in recent years. Concurrently, advances in MEMS technology have driven the development of electrochemical vibration sensors towards cost-effectiveness, miniaturization, and low power consumption. This review summarizes the sensing mechanism models and noise models of electrochemical vibration sensors. It highlights the trends in the development of sensing electrodes, including miniaturization and integration facilitated by MEMS technology, while also outlining representative fabrication processes. Furthermore, the review summarizes key application domains encompassing geophones, hydrophones, and angular acceleration sensing. Finally, it concludes with a discussion of current major challenges and an outlook on future research directions.

The surface electromyogram (sEMG)--based gesture recognition to the development of intelligent prostheses has emerged as a promising avenue for upper limb amputees. However, the temporal variations in sEMG have rendered recognition models less efficient than anticipated. Cross-session calibration and training data indicate the capacity to reduce this variation. The impact of varying calibration and training data amounts on gesture recognition performance for amputees is still unknown. To assess these effects, we present four datasets for the evaluation of calibration data and examine the impact of training data volume on benchmark performance. Two amputees who had undergone amputations years prior were recruited and seven sessions of data were collected for analysis from each of them. The experimental results show that the calibration data improved the average accuracy per subject by 3.03% and 6.16%, respectively, compared to the baseline results. Moreover, it was found that to collect training data for modeling, boosting the number of training sessions proves more efficient than increasing the number of trials within a single session. Two potentially effective strategies are proposed in light of this research to enhance the cross-session models further. We consider these findings to be of the utmost importance for the commercialization of intelligent prostheses, as they demonstrate the criticality of gathering calibration and cross-session training data, as well as offer effective strategies for maximizing the utilization of the entire dataset.