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Abstract The cost‐effective and scalable synthesis and patterning of soft nanomaterial composites with improved electrical conductivity and mechanical stretchability remains challenging in wearable devices. This work reports a scalable, low‐cost fabrication approach to directly create and pattern crumpled porous graphene/NiS₂nanocomposites with high mechanical stretchability and electrical conductivity through laser irradiation combined with electrodeposition and a pre‐strain strategy. With modulated mechanical stretchability and electrical conductivity, the crumpled graphene/NiS₂nanocomposite can be readily patterned into target geometries for application in a standalone stretchable sensing platform. By leveraging the electrical energy harvested from the kinetic motion from wearable triboelectric nanogenerator (TENG) and stored in micro‐supercapacitor arrays (MSCAs) to drive biophysical sensors, the system is demonstrated to monitor human motions, body temperature, and toxic gas in the exposed environment. The material selections, design strategies, and fabrication approaches from this study provide functional nanomaterial composites with tunable properties for future high‐performance bio‐integrated electronics. 

Abstract Silk nanofibers (SNFs) from abundant sources are low‐cost and environmentally friendly. Combined with other functional materials, SNFs can help create bioelectronics with excellent biocompatibility without environmental concerns. However, it is still challenging to construct an SNF‐based composite with high conductivity, flexibility, and mechanical strength for all SNF‐based electronics. Herein, this work reports the design and fabrication of Ti₃C₂Tx‐silver@silk nanofibers (Ti3C2Tx‐Ag@SNF) composites with multi‐dimensional heterogeneous conductive networks using combined in situ growth and vacuum filtration methods. The ultrahigh electrical conductivity of Ti₃C₂T_x‐Ag@SNF composites (142959 S m⁻¹) provides the kirigami‐patterned soft heaters with a rapid heating rate of 87 °C s⁻¹. The multi‐dimensional heterogeneous network further allows the creation of electromagnetic interference shielding devices with an exceptionally high specific shielding effectiveness of 10,088 dB cm⁻¹. Besides working as a triboelectric layer to harvest the mechanical energy and recognize the hand gesture, the Ti₃C₂T_x‐Ag@SNF composites can also be combined with an ionic layer to result in a capacitive pressure sensor with a high sensitivity of 410 kPa⁻¹in a large range due to electronic‐double layer effect. The applications of the Ti₃C₂T_x‐Ag@SNF composites in recognizing human gestures and human‐machine interfaces to wirelessly control a trolley demonstrate the future development of all SNF‐based electronics. 

Abstract General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants’ general movements can be captured digitally, but the lack of quantitative assessment and well‐trained clinical pediatricians presents an obstacle for many years to achieve wider deployment, especially in low‐resource settings. There is a high potential to explore wearable sensors for movement analysis due to outstanding privacy, low cost, and easy‐to‐use features. This work presents a sparse sensor network with soft wireless IMU devices (SWDs) for automatic early evaluation of general movements in infants. The sparse network consisting of only five sensor nodes (SWDs) with robust mechanical properties and excellent biocompatibility continuously and stably captures full‐body motion data. The proof‐of‐the‐concept clinical testing with 23 infants showcases outstanding performance in recognizing neonatal activities, confirming the reliability of the system. Taken together with a tiny machine learning algorithm, the system can automatically identify risky infants based on the GMs, with an accuracy of up to 100% (99.9%). The wearable sparse sensor network with an artificial intelligence‐based algorithm facilitates intelligent evaluation of infant brain development and early diagnosis of development disorders. 

Abstract Although increasing efforts have been devoted to the development of non‐invasive wearable electrochemical sweat sensors for monitoring physiological and metabolic information, most of them still suffer from poor stability and specificity over time and fluctuating temperatures. This study reports the design and fabrication of a long‐term stable and highly sensitive flexible electrochemical sensor based on nanocomposite‐modified porous graphene by facile laser treatment for detecting biomarkers such as glucose in sweat. The laser‐reduced and patterned stable conductive nanocomposite on the porous graphene electrode provides the resulting glucose sensor with an excellent sensitivity of 1317.69 µA mm⁻¹cm⁻²and an ultra‐low limit of detection of 0.079 µm. The sensor can also detect pH and exhibit extraordinary stability to maintain more than 91% sensitivity over 21 days in ambient conditions. Taken together with a temperature sensor based on the same material system, the dual glucose and pH sensor integrated with a flexible microfluidic sweat sampling network further results in accurate continuous on‐body glucose detection calibrated by the simultaneously measured pH and temperature. The low‐cost, highly sensitive, and long‐term stable platform could facilitate the early identification and continuous monitoring of different biomarkers for non‐invasive disease diagnosis and treatment evaluation. 

Prior research has validated Photoplethysmography (PPG) as a promising biomarker for assessing stress factors in construction workers, including physical fatigue, mental stress, and heat stress. However, the reliability of PPG as a stress biomarker in construction workers is hindered by motion artifacts (MA) - distortions in blood volume pulse measurements caused by sensor movement. This paper develops a deep convolutional autoencoder-based framework, trained to detect and reduce MA in MA-contaminated PPG signals. The framework's performance is evaluated using PPG signals acquired from individuals engaged in specific construction tasks. The results demonstrate the framework has effectiveness in both detecting and reducing MA in PPG signals with a detection accuracy of 93% and improvement in signal-to-noise ratio by over 88%. This research contributes to a more reliable and error-reduced usage of PPG signals for health monitoring in the construction industry. 

Construction workers often experience high levels of physical and mental stress due to the demanding nature of their work on construction sites. Real-time health monitoring can provide an effective means of detecting these stressors. Previous research in this field has demonstrated the potential of photoplethysmography (PPG), which represents cardiac activities, as a biomarker for assessing various stressors, including physical fatigue, mental stress, and heat stress. However, PPG acquisition during construction tasks is subject to several external noises, of which motion artifact is a major one. To address this, the study develops and examines an autoencoder network—a special type of artificial neural network—to remove PPG signals’ motion artifacts during construction tasks, thereby enhancing the accuracy of health assessments.Artifact-free PPG signals are acquired through subjects in a stationary position, which is used as the reference for training the autoencoder network. The network’s performance is examined with PPG signals acquired from the same subjects performing multiple construction tasks. The developed autoencoder network can increase the signal-to-noise ratio (SNR) by up to 33% for the corrupted signals acquired in a construction setting. This research contributes to the extensive and resilient use of PPG signals in health monitoring for construction workers.