Regarding high-sensitivity human wrist joint motion monitoring in exercise rehabilitation; we develop a pair of novel wearable and sensitivity-enhanced plastic optical fiber (POF) strain sensors consisting of an etched grating fiber and a side-polished fiber stitched into a polyamide wrist brace. The two flexible and surface-treated fibers are; respectively; featured with an etched periodic gratings with a pitch of 6 mm and a depth of 0.5 mm and a D-shaped side-polished zone of ~300 µm depth and ~30 mm length; which, correspondingly, show the sensitivities of around 0.0176/° and 0.0167/° in a normalized bending angle by far larger than a conventional commercial POF, because it achieves a more sensitive strain-induced evanescent field interaction with the side-machined fibers. Moreover, in terms of the sensor response to bending deformation in the range of −40°~+40°, the former exhibits a better sensitivity in lower angle change, while the latter is superior as the bending angle increases; thereby arranging the two modified POFs separately at the side and back of the human wrist, in order to decouple the wrist joint behaviors induced by typical flexion-extension or abduction-adduction movements. Then, the circular and pentagonal wrist motion trajectory patterns are investigated, to demonstrate the maximum average single-axis motion error of 2.94° via the transformation of spatial angle to plane coordinate for the fabricated couple of POF sensors, which is lower than a recognized standard of 5°, thus suggesting the great potential in wearable exercise rehabilitation of human joints in the field of medical treatment and healing.

Given the intrinsic low energy and high consumption characteristics of sensor nodes, it is imperative to explore strategies for achieving energy-efficient routing within wireless sensor networks (WSNs). A significant body of existing research on clustered routing algorithms for WSNs has concentrated on employing heuristic optimization algorithms to facilitate the selection of routing paths. However, once the number of sensor nodes or the deployment environment changes, the algorithm’s performance can fluctuate significantly, potentially requiring redesign and retuning. In this article, we propose the clustered routing algorithm based on forwarding mechanism optimization (CRFMO), which defines separate routing rules for intracluster and intercluster communication, providing suitable communication paths for nodes. The algorithm eschews the complex procedure of parameter tuning during the routing path selection process and contributes to expediting WSN deployment and balancing node load pressure, ultimately extending the network’s operational lifespan. Simulation outcomes reveal that, in comparison to LEACH-IACA and IMP-LEACH, the CRFMO algorithm markedly enhances energy distribution balance, equalizes the burden among nodes, sustains high network coverage over an extended period, which enhances the quality of network monitoring, and significantly extends the lifetime of the network.

This study proposes a high-sensitivity resonant graphene accelerometer based on a pressure-induced sensing mechanism. The accelerometer design encompasses an optical fiber and a vacuum-sealed graphene resonator affixed to a silicon sensitive film, incorporating a proof mass. This indirect sensing mechanism effectively mitigates the vibration mode aliasing of graphene and the proof mass while ensuring a minimal energy loss in the operating resonator. The mechanical vibration of graphene is excited and detected through an all-fiber optical system. Notably, the proposed sensor demonstrates a sensitivity of 34.3 kHz/g within the range of 0–3.5 g, which is eight times higher than comparable accelerometers utilizing a proof mass on a graphene membrane. This work exhibits a novel, to the best of our knowledge, approach to an acceleration measurement using 2D resonators, exhibiting distinct advantages in terms of compact size and heightened sensitivity.

Wearable data gloves often suffer from electromagnetic interference, insufficient substrate stability, and limited capability for multi-degree-of-freedom motion measurement. To address these limitations, a flexible glove incorporating a hybrid POF-FBG sensing scheme was designed and fabricated. Plastic optical fibers (POFs) were side-polished and patterned with long-period gratings to improve sensitivity to wrist flexion-extension and abduction-adduction. Then fiber Bragg gratings (FBGs) were embedded in a polydimethylsiloxane substrate and encapsulated using thermoplastic polyurethane fixtures to reduce the influence of skin stretching and improve measurement accuracy of finger-joint angle. Moreover, a thermoplastic polyurethane skeleton with an adaptive sliding-rail structure was 3D printed to maintain the stability of the sensor placement at the joints. Experimental results demonstrated the mean absolute errors of 4.06°, 1.38° and 1.70° for wrist flexion-extension, abduction-adduction and finger-joint bending, respectively, along with excellent gesture classification using a support vector machine algorithm, which indicates great potential in virtual reality interaction and hand rehabilitation applications.

Wireless Sensor Network (WSN) is an important part of the Internet of Things (IoT), which are used for information exchange and communication between smart objects. In practical applications, WSN lifecycle can be influenced by the unbalanced distribution of node centrality and excessive energy consumption, etc. In order to overcome these problems, a heterogeneous wireless sensor network model with small world characteristics is constructed to balance the centrality and enhance the invulnerability of the network. Also, a new WSN centrality measurement method and a new invulnerability measurement model are proposed based on the WSN data transmission characteristics. Simulation results show that the life cycle and data transmission volume of the network can be improved with a lower network construction cost, and the invulnerability of the network is effectively enhanced.