In the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients’ intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly influenced by environmental factors. To address the above issues, multisensory integration-based EMG pattern recognition (PR) techniques have been developed in recent years, and fruitful results have been demonstrated in diverse rehabilitation scenarios, such as achieving high locomotion detection and prosthesis control accuracy. Owing to the importance and rapid development of the EMG centered multisensory fusion technologies in rehabilitation, this paper reviews both theories and applications in this emerging field. The principle of EMG signal generation and the current pattern recognition process are explained in detail, including signal preprocessing, feature extraction, classification algorithms, etc. Mechanisms of collaborations between two important multisensory fusion strategies (kinetic and kinematics) and EMG information are thoroughly explained; corresponding applications are studied, and the pros and cons are discussed. Finally, the main challenges in EMG centered multisensory pattern recognition are discussed, and a future research direction of this area is prospected.

This study showed that subclinical hypothyroidism appeared in the participants who took the 400-μg I supplement, which provided a total iodine intake of ∼800 μg/d. Thus, we caution against a total daily iodine intake that exceeds 800 μg/d in China and recommend further research to determine a safe daily upper limit.

This paper presents a flexible graphene/polyvinylidene difluoride (PVDF)/graphene sandwich for three-dimensional touch interactivity. Here, x-y plane touch is sensed using graphene capacitive elements, while force sensing in the z-direction is by a piezoelectric PVDF/graphene sandwich. By employing different frequency bands for the capacitive- and force-induced electrical signals, the two stimuli are detected simultaneously, achieving three-dimensional touch sensing. Static force sensing and elimination of propagated stress are achieved by augmenting the transient piezo output with the capacitive touch, thus overcoming the intrinsic inability of the piezoelectric material in detecting nontransient force signals and avoiding force touch mis-registration by propagated stress.

Among diverse wearable techniques, insole‐based plantar pressure monitoring systems have surged as a leading technology to monitor patient's chronic disease progression. Such technological feat has been made possible due to the strong correlation between gait and disease status. Hence, insole‐based plantar pressure monitoring techniques are growing rapidly worldwide; with several research institutions and enterprises showing an increased interest in the field. This review intends to first explain the working principles of mainstream insole plantar sensing techniques and design considerations such as sensing material selection and electronics design requirements, and then the state‐of‐the‐art algorithms for plantar pressure distribution reconstruction. Following, this article will discuss disease monitoring applications and the extraction of disease features. Finally, insight regarding common challenges and their potential solutions within the field would be elucidated.

Cochlear hair cells are critical for the conversion of acoustic into electrical signals and their dysfunction is a primary cause of acquired hearing impairments, which worsen with aging. Piezoelectric materials can reproduce the acoustic-electrical transduction properties of the cochlea and represent promising candidates for future cochlear prostheses. The majority of piezoelectric hearing devices so far developed are based on thin films, which have not managed to simultaneously provide the desired flexibility, high sensitivity, wide frequency selectivity, and biocompatibility. To overcome these issues, we hypothesized that fibrous membranes made up of polymeric piezoelectric biocompatible nanofibers could be employed to mimic the function of the basilar membrane, by selectively vibrating in response to different frequencies of sound and transmitting the resulting electrical impulses to the vestibulocochlear nerve. In this study, poly(vinylidene fluoride-trifluoroethylene) piezoelectric nanofiber-based acoustic circular sensors were designed and fabricated using the electrospinning technique. The performance of the sensors was investigated with particular focus on the identification of the resonance frequencies and acoustic-electrical conversion in fibrous membrane with different size and fiber orientation. The voltage output (1–17 mV) varied in the range of low resonance frequency (100–400 Hz) depending on the diameter of the macroscale sensors and alignment of the fibers. The devices developed can be regarded as a proof-of-concept demonstrating the possibility of using piezoelectric fibers to convert acoustic waves into electrical signals, through possible synergistic effects of piezoelectricity and triboelectricity. The study has paved the way for the development of self-powered nanofibrous implantable auditory sensors.

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increasingly problematic. Unlike the Von Neumann computing platform, the human brain relies on neurons and synapses for storage and computation, which do not have clear boundaries between them. Therefore, nanodevices that mimic synapses, for high-efficiency computing, have been investigated; among these nanodevices, memristors have attracted most attention because of their low power consumption, high integration density, and the ability to simulate synaptic plasticity, which meet the standards of neuromorphic computing. [4] The first report on the resistive switching phenomenon dates back to the 1960s; [5] since early theories were insufficient to explain this phenomenon, research had been done on it. It was not until the memristor was theoretically proposed in 1971, that the mechanisms underpinning the resistive switching became abundant. [6] The first memristor was manufactured by Hewlett-Packard in 2008. [7] Since then, memristors made of diverse materials have been successfully studied, including conductive filament memristors, magnetic tunnel junctions, ferroelectric tunnel junctions, phase-change memristors, and so on (Figure 1). These devices have been used for storage and computing purposes. [8,9] In recent years, there have been many reviews investigating neuromorphic computing from the perspectives of device electrical properties, [9,10] resistive switching materials, [11,12] memristive synapses and neurons, [13] algorithm optimization, [14] and circuit design. [15] Different from the existing literature, we discuss the possibility of achieving brain-like computing from the perspective of memristor technology and review the establishment of spiking neural network neuromorphic computing systems. In this article, we first review the resistive switching mechanisms of different types of memristors and focus on factors, which affect device stability and the corresponding optimization measures that have been applied. Furthermore, we study the stochasticity, power consumption, switching speed, retention, endurance, and other properties of memristors, which are the basis for neuromorphic computing implementations. We then review various memristor-based neural networks and the building of spike neural network neuromorphic computing systems. Finally, we shed light upon the major challenges and offer our perspectives and opinions for memristor-based brainlike computing systems.The memristor is a resistive switch where its resistive state is programable based on the applied voltage or current. Memristive devices are thus capable of storing and computing information simultaneously, breaking the Von Neumann bottleneck. Since the first nanomemristor made by Hewlett-Packard in 2008, advances so far have enabled nanostructured, low-power, high-durability devices that exhibit superior performance over conventional CMOS devices. Herein, the development of memristors based on different physical mechanisms is reviewed. In particular, device stability, integration density, power consumption, switching speed, retention, and e...

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