Machine-Learning-Aided Self-Powered Assistive Physical Therapy Devices

Stretchable Polymer Hydrogels Based Flexible Triboelectric Nanogenerators for Self-Powered Bioelectronics

The rapid development of flexible electronics has led to unprecedented social and economic improvements. But conventional power devices cannot adapt to the advances of flexible electronics. Triboelectric nanogenerators (TENGs) have been used as robust power sources to transform ambient mechanical energy into electricity, thus meeting the power requirements of flexible electronics. Hydrogels are widely used for soft bioelectronics owing to the decent stretchability and biocompatibility. This Review presents the recent progress in the use of hydrogels for TENGs and self-powered hydrogel bioelectronics, including hydrogel synthesis, hydrogel TENGs fabrication, and their applications in wearable electricity generation, self-powered active sensing, and therapeutics. Hydrogel-enabled TENGs are emerging as a novel form of soft bioelectronics. We provided a critical analysis of hydrogel TENGs and insights into future opportunities and directions of this rapidly evolving field. These advancements will push the boundaries of hydrogel bioelectronics and contribute to the development of personalized healthcare solutions.

Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements

The integration of artificial intelligence (AI) technologies into physical and mental rehabilitation has the potential to significantly transform these fields. AI innovations, including machine learning algorithms, natural language processing, and computer vision, offer occupational therapists advanced tools to improve care quality. These technologies facilitate more precise assessments, the development of tailored intervention plans, more efficient treatment delivery, and enhanced outcome evaluation. This review explores the integration of AI across various aspects of rehabilitation, providing a thorough examination of recent advancements and current applications. It highlights how AI applications, such as natural language processing, computer vision, virtual reality, machine learning, and robotics, are shaping the future of physical and mental recovery in occupational therapy.

E-Skin and Its Advanced Applications in Ubiquitous Health Monitoring

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems.

Using augmented intelligence to improve long term outcomes

PURPOSE OF REVIEW

For augmented intelligence (AI) tools to realize their potential, critical care clinicians must ensure they are designed to improve long-term outcomes. This overview is intended to align professionals with the state-of-the art of AI.

RECENT FINDINGS

Many AI tools are undergoing preliminary assessment of their ability to support the care of survivors and their caregivers at multiple time points after intensive care unit (ICU) discharge. The domains being studied include early identification of deterioration (physiological, mental), management of impaired physical functioning, pain, sleep and sexual dysfunction, improving nutrition and communication, and screening and treatment of cognitive impairment and mental health disorders.Several technologies are already being marketed and many more are in various stages of development. These technologies mostly still require clinical trials outcome testing. However, lacking a formal regulatory approval process, some are already in use.

SUMMARY

Plans for long-term management of ICU survivors must account for the development of a holistic follow-up system that incorporates AI across multiple platforms. A tiered post-ICU screening program may be established wherein AI tools managed by ICU follow-up clinics provide appropriate assistance without human intervention in cases with less pathology and refer severe cases to expert treatment.

Advances in Machine Learning for Wearable Sensors

Recent years have witnessed tremendous advances in machine learning techniques for wearable sensors and bioelectronics, which play an essential role in real-time sensing data analysis to provide clinical-grade information for personalized healthcare. To this end, supervised learning and unsupervised learning algorithms have emerged as powerful tools, allowing for the detection of complex patterns and relationships in large, high-dimensional data sets. In this Review, we aim to delineate the latest advancements in machine learning for wearable sensors, focusing on key developments in algorithmic techniques, applications, and the challenges intrinsic to this evolving landscape. Additionally, we highlight the potential of machine-learning approaches to enhance the accuracy, reliability, and interpretability of wearable sensor data and discuss the opportunities and limitations of this emerging field. Ultimately, our work aims to provide a roadmap for future research endeavors in this exciting and rapidly evolving area.

Flexible Metasurfaces for Multifunctional Interfaces

Optical metasurfaces, capable of manipulating the properties of light with a thickness at the subwavelength scale, have been the subject of extensive investigation in recent decades. This research has been mainly driven by their potential to overcome the limitations of traditional, bulky optical devices. However, most existing optical metasurfaces are confined to planar and rigid designs, functions, and technologies, which greatly impede their evolution toward practical applications that often involve complex surfaces. The disconnect between two-dimensional (2D) planar structures and three-dimensional (3D) curved surfaces is becoming increasingly pronounced. In the past two decades, the emergence of flexible electronics has ushered in an emerging era for metasurfaces. This review delves into this cutting-edge field, with a focus on both flexible and conformal design and fabrication techniques. Initially, we reflect on the milestones and trajectories in modern research of optical metasurfaces, complemented by a brief overview of their theoretical underpinnings and primary classifications. We then showcase four advanced applications of optical metasurfaces, emphasizing their promising prospects and relevance in areas such as imaging, biosensing, cloaking, and multifunctionality. Subsequently, we explore three key trends in optical metasurfaces, including mechanically reconfigurable metasurfaces, digitally controlled metasurfaces, and conformal metasurfaces. Finally, we summarize our insights on the ongoing challenges and opportunities in this field.

Materials-Driven Soft Wearable Bioelectronics for Connected Healthcare

In the era of Internet-of-things, many things can stay connected; however, biological systems, including those necessary for human health, remain unable to stay connected to the global Internet due to the lack of soft conformal biosensors. The fundamental challenge lies in the fact that electronics and biology are distinct and incompatible, as they are based on different materials via different functioning principles. In particular, the human body is soft and curvilinear, yet electronics are typically rigid and planar. Recent advances in materials and materials design have generated tremendous opportunities to design soft wearable bioelectronics, which may bridge the gap, enabling the ultimate dream of connected healthcare for anyone, anytime, and anywhere. We begin with a review of the historical development of healthcare, indicating the significant trend of connected healthcare. This is followed by the focal point of discussion about new materials and materials design, particularly low-dimensional nanomaterials. We summarize material types and their attributes for designing soft bioelectronic sensors; we also cover their synthesis and fabrication methods, including top-down, bottom-up, and their combined approaches. Next, we discuss the wearable energy challenges and progress made to date. In addition to front-end wearable devices, we also describe back-end machine learning algorithms, artificial intelligence, telecommunication, and software. Afterward, we describe the integration of soft wearable bioelectronic systems which have been applied in various testbeds in real-world settings, including laboratories that are preclinical and clinical environments. Finally, we narrate the remaining challenges and opportunities in conjunction with our perspectives.

Self-healing electrical bioadhesive interface for electrophysiology recording

Electrical bioadhesive interfaces (EBIs) are standing out in various applications, including medical diagnostics, prosthetic devices, rehabilitation, and human-machine interactions. Nonetheless, crafting a reliable and advanced EBI with comprehensive properties spanning electrochemical, electrical, mechanical, and self-healing capabilities remains a formidable challenge. Herein, we develop a self-healing EBI by thoughtfully integrating conducting polymer nanofibers and a typical bioadhesive within a robust hydrogel matrix. The accomplished EBI demonstrates extraordinary adhesion (lap shear strength of 197 kPa), exceptional electrical conductivity (2.18 S m-1), and outstanding self-healing performance. Taking advantage of these attributes, we integrated the EBI into flexible skin electrodes for surface electromyography (sEMG) signal recording from forearm muscles. The engineered skin electrodes exhibit robust adhesion to the skin even when sweating, rapid self-healing from damage, and seamless real-time signal recording with a higher signal-to-noise ratio (39 dB). Our EBI, along with its skin electrodes, offers a promising platform for tissue-device integration, health monitoring, and an array of bioelectronic applications.

Biomaterials and bioelectronics for self-powered neurostimulation

Self-powered neurostimulation via biomaterials and bioelectronics innovation has emerged as a compelling approach to explore, repair, and modulate neural systems. This review examines the application of self-powered bioelectronics for electrical stimulation of both the central and peripheral nervous systems, as well as isolated neurons. Contemporary research has adeptly harnessed biomechanical and biochemical energy from the human body, through various mechanisms such as triboelectricity, piezoelectricity, magnetoelasticity, and biofuel cells, to power these advanced bioelectronics. Notably, these self-powered bioelectronics hold substantial potential for delivering neural stimulations that are customized for the treatment of neurological diseases, facilitation of neural regeneration, and the development of neuroprosthetics. Looking ahead, we expect that the ongoing advancements in biomaterials and bioelectronics will drive the field of self-powered neurostimulation toward the realization of more advanced, closed-loop therapeutic solutions, paving the way for personalized and adaptable neurostimulators in the coming decades.

Artificial Intelligence-Powered Electronic Skin

Skin-interfaced electronics is gradually changing medical practices by enabling continuous and noninvasive tracking of physiological and biochemical information. With the rise of big data and digital medicine, next-generation electronic skin (e-skin) will be able to use artificial intelligence (AI) to optimize its design as well as uncover user-personalized health profiles. Recent multimodal e-skin platforms have already employed machine learning (ML) algorithms for autonomous data analytics. Unfortunately, there is a lack of appropriate AI protocols and guidelines for e-skin devices, resulting in overly complex models and non-reproducible conclusions for simple applications. This review aims to present AI technologies in e-skin hardware and assess their potential for new inspired integrated platform solutions. We outline recent breakthroughs in AI strategies and their applications in engineering e-skins as well as understanding health information collected by e-skins, highlighting the transformative deployment of AI in robotics, prosthetics, virtual reality, and personalized healthcare. We also discuss the challenges and prospects of AI-powered e-skins as well as predictions for the future trajectory of smart e-skins.

Implantable Triboelectric Nanogenerators for Self-Powered Cardiovascular Healthcare

Triboelectric nanogenerators (TENGs) have gained significant traction in recent years in the bioengineering community. With the potential for expansive applications for biomedical use, many individuals and research groups have furthered their studies on the topic, in order to gain an understanding of how TENGs can contribute to healthcare. More specifically, there have been a number of recent studies focusing on implantable triboelectric nanogenerators (I-TENGs) toward self-powered cardiac systems healthcare. In this review, the progression of implantable TENGs for self-powered cardiovascular healthcare, including self-powered cardiac monitoring devices, self-powered therapeutic devices, and power sources for cardiac pacemakers, will be systematically reviewed. Long-term expectations of these implantable TENG devices through their biocompatibility and other utilization strategies will also be discussed.

Optical Integration in Wearable, Implantable and Swallowable Healthcare Devices

Recent advances in materials and semiconductor technologies have led to extensive research on optical integration in wearable, implantable, and swallowable health devices. These optical systems utilize the properties of light─intensity, wavelength, polarization, and phase─to monitor and potentially intervene in various biological events. The potential of these devices is greatly enhanced through the use of multifunctional optical materials, adaptable integration processes, advanced optical sensing principles, and optimized artificial intelligence algorithms. This synergy creates many possibilities for clinical applications. This Perspective discusses key opportunities, challenges, and future directions, particularly with respect to sensing modalities, multifunctionality, and the integration of miniaturized optoelectronic devices. We present fundamental insights and illustrative examples of such devices in wearable, implantable, and swallowable forms. The constant pursuit of innovation and the dedicated approach to critical challenges are poised to influence diverse fields.

Tracing the Flu Symptom Progression via a Smart Face Mask

Respiration and body temperature are largely influenced by the highly contagious influenza virus, which poses persistent global public health challenges. Here, we present a wireless all-in-one sensory face mask (WISE mask) made of ultrasensitive fibrous temperature sensors. The WISE mask shows exceptional thermosensitivity, excellent breathability, and wearing comfort. It offers highly sensitive body temperature monitoring and respiratory detection capabilities. Capitalizing on the advances in the Internet of Things and artificial intelligence, the WISE mask is further demonstrated by customized flexible circuitry, deep learning algorithms, and a user-friendly interface to continuously recognize the abnormalities of both the respiration and body temperature. The WISE mask represents a compelling approach to tracing flu symptom progression in a cost-effective and convenient manner, serving as a powerful solution for personalized health monitoring and point-of-care systems in the face of ongoing influenza-related public health concerns.

Stretchable Thermoelectric Fibers with Three-Dimensional Interconnected Porous Network for Low-Grade Body Heat Energy Harvesting

Electricity generation from body heat has garnered significant interest as a sustainable power source for wearable bioelectronics. In this work, we report stretchable n-type thermoelectric fibers based on the hybrid of Ti3C2Tx MXene nanoflakes and polyurethane (MP) through a wet-spinning process. The proposed fibers are designed with a 3D interconnected porous network to achieve satisfactory electrical conductivity (σ), thermal conductivity (κ), and stretchability simultaneously. We systematically optimize the thermoelectric and mechanical traits of the MP fibers and the MP-60 (with 60 wt % MXene content) exhibits a high σ of 1.25 × 103 S m-1, an n-type Seebeck coefficient of -8.3 μV K-1, and a notably low κ of 0.19 W m-1 K-1. Additionally, the MP-60 fibers possess great stretchability and mechanical strength with a tensile strain of 434% and a breaking stress of 11.8 MPa. Toward practical application, a textile thermoelectric generator is constructed based on the MP-60 fibers and achieves a voltage of 3.6 mV with a temperature gradient between the body skin and ambient environment, highlighting the enormous potential of low-grade body heat energy harvesting.

Functional material-mediated wireless physical stimulation for neuro-modulation and regeneration

Nerve injuries and neurological diseases remain intractable clinical challenges. Despite the advantages of stem cell therapy in treating neurological disorders, uncontrollable cell fates and loss of cell function in vivo are still challenging. Recently, increasing attention has been given to the roles of external physical signals, such as electricity and ultrasound, in regulating stem cell fate as well as activating or inhibiting neuronal activity, which provides new insights for the treatment of neurological disorders. However, direct physical stimulations in vivo are short in accuracy and safety. Functional materials that can absorb energy from a specific physical field exerted in a wireless way and then release another localized physical signal hold great advantages in mediating noninvasive or minimally invasive accurate indirect physical stimulations to promote the therapeutic effect on neurological disorders. In this review, the mechanism by which various physical signals regulate stem cell fate and neuronal activity is summarized. Based on these concepts, the approaches of using functional materials to mediate indirect wireless physical stimulation for neuro-modulation and regeneration are systematically reviewed. We expect that this review will contribute to developing wireless platforms for neural stimulation as an assistance for the treatment of neurological diseases and injuries.

Bio-inspired artificial synaptic transistors: evolution from innovative basic units to system integration

The investigation of transistor-based artificial synapses in bioinspired information processing is undergoing booming exploration, and is the stable building block for brain-like computing. Given that the storage and computing separation architecture of von Neumann construction is not conducive to the current explosive information processing, it is critical to accelerate the connection between hardware systems and software simulations of intelligent synapses. So far, various works based on a transistor-based synaptic system successfully simulated functions similar to biological nerves in the human brain. However, the influence of the semiconductor and the device structural design on synaptic properties is still poorly linked. This review concretely emphasizes the recent advances in the novel structure design of semiconductor materials and devices used in synaptic transistors, not only from a single multifunction synaptic device but also to system application with various connected routes and related working mechanisms. Finally, crises and opportunities in transistor-based synaptic interconnection are discussed and predicted.

Multichannel Gradient Piezoelectric Transducer Assisted with Deep Learning for Broadband Acoustic Sensing

As an important part of human-machine interfaces, piezoelectric voice recognition has received extensive attention due to its unique self-powered nature. However, conventional voice recognition devices exhibit a limited response frequency band due to the intrinsic hardness and brittleness of piezoelectric ceramics or the flexibility of piezoelectric fibers. Here, we propose a cochlear-inspired multichannel piezoelectric acoustic sensor (MAS) based on gradient PVDF piezoelectric nanofibers for broadband voice recognition by a programmable electrospinning technique. Compared with the common electrospun PVDF membrane-based acoustic sensor, the developed MAS demonstrates the greatly 300%-broadened frequency band and the substantially 334.6%-enhanced piezoelectric output. More importantly, this MAS can serve as a high-fidelity auditory platform for music recording and human voice recognition, in which the classification accuracy rate can reach up to 100% in coordination with deep learning. The programmable bionic gradient piezoelectric nanofiber may provide a universal strategy for the development of intelligent bioelectronics.

Advances in Photoplethysmography for Personalized Cardiovascular Monitoring

Photoplethysmography (PPG) is garnering substantial interest due to low cost, noninvasiveness, and its potential for diagnosing cardiovascular diseases, such as cardiomyopathy, heart failure, and arrhythmia. The signals obtained through PPG can yield information based on simple analyses, such as heart rate. In contrast, when accompanied by the complex analysis of sophisticated signals, valuable information, such as blood pressure, sympathetic nervous system activity, and heart rate variability, can be obtained. For a complex analysis, a better understanding of the sources of noise, which create limitations in the application of PPG, is needed to get reliable information to assess cardiovascular health. Therefore, this Special Issue handles literature about noises and how they affect the waveform of the PPG caused by individual variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external factors (e.g., motion artifact, ambient light, and applied pressure to the skin). It also covers the issues that still need to be considered in each situation.

Self-Powered Smart Gloves Based on Triboelectric Nanogenerators

The hands are used in all facets of daily life, from simple tasks such as grasping and holding to complex tasks such as communication and using technology. Finding a way to not only monitor hand movements and gestures but also to integrate that data with technology is thus a worthwhile task. Gesture recognition is particularly important for those who rely on sign language to communicate, but the limitations of current vision-based and sensor-based methods, including lack of portability, bulkiness, low sensitivity, highly expensive, and need for external power sources, among many others, make them impractical for daily use. To resolve these issues, smart gloves can be created using a triboelectric nanogenerator (TENG), a self-powered technology that functions based on the triboelectric effect and electrostatic induction and is also cheap to manufacture, small in size, lightweight, and highly flexible in terms of materials and design. In this review, an overview of the existing self-powered smart gloves will be provided based on TENGs, both for gesture recognition and human-machine interface, concluding with a discussion on the future outlook of these devices.

Debonding-On-Demand Polymeric Wound Patches for Minimal Adhesion and Clinical Communication

Herein, a multifunctional bilayer wound patch is developed by integrating a debonding-on-demand polymeric tissue adhesive (DDPTA) with an ionic conducting elastomer (ICE). As a skin adhesive layer, the DDPTA is soft and adherent at skin temperature but hard and non-tacky when cooled, so it provides unique temperature-triggered quick adhesion and non-forced detachment from the skin. During use, the dense surface of the DDPTA prevents blood infiltration and reduces unnecessary blood loss with gentle pressing. Moreover, its hydrophobic matrix helps to repel blood and prevents the formation of clots, thus precluding wound tearing during its removal. This unique feature enables the DDPTA to avoid the severe deficiencies of hydrophilic adhesives, providing a reliable solution for a wide range of secondary wound injuries. The DDPTA is versatile in that it can be covered with ICE to configure a DDPTA@ICE patch for initiating non-verbal communication systems by the fingers, leading toward sign language recognition and a remote clinical alarm system. This multifunctional wound patch with debonding-on-demand can promote a new style of tissue sealant for convenient clinical communication.

Using Deep Learning for Task and Tremor Type Classification in People with Parkinson's Disease

Hand tremor is one of the dominating symptoms of Parkinson's disease (PD), which significantly limits activities of daily living. Along with medications, wearable devices have been proposed to suppress tremor. However, suppressing tremor without interfering with voluntary motion remains challenging and improvements are needed. The main goal of this work was to design algorithms for the automatic identification of the tremor type and voluntary motions, using only surface electromyography (sEMG) data. Towards this goal, a bidirectional long short-term memory (BiLSTM) algorithm was implemented that uses sEMG data to identify the motion and tremor type of people living with PD when performing a task. Moreover, in order to automate the training process, hyperparamter selection was performed using a regularized evolutionary algorithm. The results show that the accuracy of task classification among 15 people living with PD was 84±8%, and the accuracy of tremor classification was 88±5%. Both models performed significantly above chance levels (20% and 33% for task and tremor classification, respectively). Thus, it was concluded that the trained models, based on using purely sEMG signals, could successfully identify the task and tremor types.

Progress in Hybridization of Covalent Organic Frameworks and Metal-Organic Frameworks

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) hybrid materials are a class of porous crystalline materials that integrate MOFs and COFs with hierarchical pore structures. As an emerging porous frame material platform, MOF/COF hybrid materials have attracted tremendous attention, and the field is advancing rapidly and extending into more diverse fields. Extensive studies have shown that a broad variety of MOF/COF hybrid materials with different structures and specific properties can be synthesized from diverse building blocks via different chemical reactions, driving the rapid growth of the field. The allowed complementary utilization of π-conjugated skeletons and nanopores for functional exploration has endowed these hybrid materials with great potential in challenging energy and environmental issues. It is necessary to prepare a "family tree" to accurately trace the developments in the study of MOF/COF hybrid materials. This review comprehensively summarizes the latest achievements and advancements in the design and synthesis of MOF/COF hybrid materials, including COFs covalently bonded to the surface functional groups of MOFs (MOF@COF), MOFs grown on the surface of COFs (COF@MOF), bridge reaction between COF and MOF (MOF+COF), and their various applications in catalysis, energy storage, pollutant adsorption, gas separation, chemical sensing, and biomedicine. It concludes with remarks concerning the trend from the structural design to functional exploration and potential applications of MOF/COF hybrid materials.

Kirigami-Inspired Pressure Sensors for Wearable Dynamic Cardiovascular Monitoring

Continuously and accurately monitoring pulse-wave signals is critical to prevent and diagnose cardiovascular diseases. However, existing wearable pulse sensors are vulnerable to motion artifacts due to the lack of proper adhesion and conformal interface with human skin during body movement. Here, a highly sensitive and conformal pressure sensor inspired by the kirigami structure is developed to measure the human pulse wave on different body artery sites under various prestressing pressure conditions and even with body movement. COMSOL multiphysical field coupling simulation and experimental testing are used to verify the unique advantages of the kirigami structure. The device shows a superior sensitivity (35.2 mV Pa^-1 ) and remarkable stability (>84 000 cycles). Toward practical applications, a wireless cardiovascular monitoring system is developed for wirelessly transmitting the pulse signals to a mobile phone in real-time, which successfully distinguished the pulse waveforms from different participants. The pulse waveforms measured by the kirigami inspired pressure sensor are as accurate as those provided by the commercial medical device. Given the compelling features, the sensor provides an ascendant way for wearable electronics to overcome motion artifacts when monitoring pulse signals, thus representing a solid advancement toward personalized healthcare in the era of the Internet of Things.

Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery

Background

Low cardiac output syndrome (LCOS) is the most serious physiological abnormality with high mortality for patients after cardiac surgery. This study aimed to explore the multidimensional data of clinical features and outcomes to provide individualized care for patients with LCOS.

Methods

The electronic medical information of the intensive care units (ICUs) was extracted from a tertiary hospital in South China. We included patients who were diagnosed with LCOS in the ICU database. We used the consensus clustering approach based on patient characteristics, laboratory data, and vital signs to identify LCOS subgroups. The consensus clustering method involves subsampling from a set of items, such as microarrays, and determines to cluster of specified cluster counts (k). The primary clinical outcome was in-hospital mortality and was compared between the clusters.

Results

A total of 1,205 patients were included and divided into three clusters. Cluster 1 (n = 443) was defined as the low-risk group [in-hospital mortality =10.1%, odds ratio (OR) = 1]. Cluster 2 (n = 396) was defined as the medium-risk group [in-hospital mortality =25.0%, OR = 2.96 (95% CI = 1.97–4.46)]. Cluster 3 (n = 366) was defined as the high-risk group [in-hospital mortality =39.2%, OR = 5.75 (95% CI = 3.9–8.5)].

Conclusion

Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.

Trajectory Planning and Simulation Study of Redundant Robotic Arm for Upper Limb Rehabilitation Based on Back Propagation Neural Network and Genetic Algorithm

In this study, a Back Propagation (BP) neural network algorithm based on Genetic Algorithm (GA) optimization is proposed to plan and optimize the trajectory of a redundant robotic arm for the upper limb rehabilitation of patients. The feasibility of the trajectory was verified by numerical simulations. First, the collected dataset was used to train the BP neural network optimized by the GA. Subsequently, the critical points designated by the rehabilitation physician for the upper limb rehabilitation were used as interpolation points for cubic B−spline interpolation to plan the motion trajectory. The GA optimized the planned trajectory with the goal of time minimization, and the feasibility of the optimized trajectory was analyzed with MATLAB simulations. The planned trajectory was smooth and continuous. There was no abrupt change in location or speed. Finally, simulations revealed that the optimized trajectory reduced the motion time and increased the motion speed between two adjacent critical points which improved the rehabilitation effect and can be applied to patients with different needs, which has high application value.

Ergonomic Design and Performance Evaluation of H-Suit for Human Walking

A soft exoskeleton for the hip flexion, named H-Suit, is developed to improve the walking endurance of lower limbs, delay muscle fatigue and reduce the activation level of hip flexors. Based on the kinematics and biomechanics of the hip joints, the ergonomic design of the H-Suit system is clearly presented and the prototype was developed. The profile of the auxiliary forces is planned in the auxiliary range where the forces start at the minimum hip angle, reach the maximum (120 N) and end at 90% of each gait cycle. The desired displacements of the traction unit which consist of the natural and elastic displacements of the steel cables are obtained by the experimental method. An assistance strategy is proposed to track the profile of the auxiliary forces by dynamically adjusting the compensation displacement Lc and the hold time Δt. The influences of the variables Lc and Δt on the natural gaits and auxiliary forces have been revealed and analyzed. The real profile of the auxiliary forces can be obtained and is consistent with the theoretical one by the proposed assistance strategy. The H-Suit without the drive unit has little effect on the EMG signal of the lower limbs. In the powered condition, the H-Suit can delay the muscle fatigue of the lower limbs. The average rectified value (ARV) slope decreases and the median frequency (MNF) slope increases significantly. Wearing the H-Suit resulted in a significant reduction of the vastus lateralis effort, averaged over subjects and walking speeds, of 13.3 ± 2.1% (p = 2 × 10−5).

Self-Powered Gesture Recognition Wristband Enabled by Machine Learning for Full Keyboard and Multicommand Input

Virtual reality is a brand-new technology that can be applied extensively. To realize virtual reality, certain types of human-computer interaction equipment are necessary. Existing virtual reality technologies often rely on cameras, data gloves, game pads, and other equipment. These equipment are either bulky, inconvenient to carry and use, or expensive to popularize. Therefore, the development of a convenient and low-cost high-precision human-computer interaction device can contribute positively to the development of virtual reality technology. In this study, a gesture recognition wristband that can realize a full keyboard and multicommand input is developed. The wristband is convenient to wear, low in cost, and does not affect other daily operations of the hand. This wristband is based on physiological anatomy as well as aided by active sensor and machine learning technology; it can achieve a maximum accuracy of 92.6% in recognizing 26 letters. This wristband offers broad application prospects in the fields of gesture command recognition, assistive devices for the disabled, and wearable electronics.

Wearable Pressure Sensors for Pulse Wave Monitoring

Cardiovascular diseases remain the leading cause of death worldwide. The rapid development of flexible sensing technologies and wearable pressure sensors have attracted keen research interest and have been widely used for long-term and real-time cardiovascular status monitoring. Owing to compelling characteristics, including light weight, wearing comfort, and high sensitivity to pulse pressures, physiological pulse waveforms can be precisely and continuously monitored by flexible pressure sensors for wearable health monitoring. Herein, an overview of wearable pressure sensors for human pulse wave monitoring is presented, with a focus on the transduction mechanism, microengineering structures, and related applications in pulse wave monitoring and cardiovascular condition assessment. The conceptualizations and methods for the acquisition of physiological and pathological information related to the cardiovascular system are outlined. The biomechanics of arterial pulse waves and the working mechanism of various wearable pressure sensors, including triboelectric, piezoelectric, magnetoelastic, piezoresistive, capacitive, and optical sensors, are also subject to systematic debate. Exemple applications of pulse wave measurement based on microengineering structured devices are then summarized. Finally, a discussion of the opportunities and challenges that wearable pressure sensors face, as well as their potential as a wearable intelligent system for personalized healthcare is given in conclusion.

Machine-Learning-Assisted Recognition on Bioinspired Soft Sensor Arrays

Soft interfaces with self-sensing capabilities play an essential role in environment awareness and reaction. The growing overlap between materials and sensory systems has created a myriad of challenges for sensor integration, including the design of a multimodal sensory, simplified system design capable of high spatiotemporal sensing resolution and efficient processing methods. Here we report a bioinspired soft sensor array (BOSSA) that integrates pressure and material sensing capabilities based on the triboelectric effect. Cascaded row + column electrodes embedded in low-modulus porous silicone rubber allow rich information to be captured from the environment and further analyzed by data-driven algorithms (multilayer perceptrons) to extract higher level features. BOSSA demonstrates the ability to identify 10 users (98.9%) and identify the placement or extraction of 10 objects (98.6%). Moreover, its scalable fabrication facilitates large-area sensor arrays with high spatiotemporal resolution and multimodal sensing abilities yet with a less complex system architecture. These features may be promising in the development of immersive sensing networks for intelligent monitoring and stimuli response in smart home/industry applications.

Progress of Advanced Devices and Internet of Things Systems as Enabling Technologies for Smart Homes and Health Care

In the Internet of Things (IoT) era, various devices (e.g., sensors, actuators, energy harvesters, etc.) and systems have been developed toward the realization of smart homes/buildings and personal health care. These advanced devices can be categorized into ambient devices and wearable devices based on their usage scenarios, to enable motion tracking, health monitoring, daily care, home automation, fall detection, intelligent interaction, assistance, living convenience, and security in smart homes. With the rapidly increasing number of such advanced devices and IoT systems, achieving fully self-sustained and multimodal intelligent systems is becoming more and more important to realize a sustainable and all-in-one smart home platform. Hence, in this Review, we systematically present the recent progress of the development of advanced materials, fabrication techniques, devices, and systems for enabling smart home and health care applications. First, advanced polymer, fiber, and fabric materials as well as their respective fabrication techniques for large-scale manufacturing are discussed. After that, functional devices classified into ambient devices (at home ambiance such as door, floor, table, chair, bed, toilet, window, wall, etc.) and wearable devices (on body parts such as finger, wrist, arm, throat, face, back, etc.) are presented for diverse monitoring and auxiliary applications. Next, the current developments of self-sustained systems and intelligent systems are reviewed in detail, indicating two promising research directions in this field. Last, conclusions and outlook pinpointed on the existing challenges and opportunities are provided for the research community to consider.

Piezoelectric nanogenerators for personalized healthcare

The development of flexible piezoelectric nanogenerators has experienced rapid progress in the past decade and is serving as the technological foundation of future state-of-the-art personalized healthcare. Due to their highly efficient mechanical-to-electrical energy conversion, easy implementation, and self-powering nature, these devices permit a plethora of innovative healthcare applications in the space of active sensing, electrical stimulation therapy, as well as passive human biomechanical energy harvesting to third party power on-body devices. This article gives a comprehensive review of the piezoelectric nanogenerators for personalized healthcare. After a brief introduction to the fundamental physical science of the piezoelectric effect, material engineering strategies, device structural designs, and human-body centered energy harvesting, sensing, and therapeutics applications are also systematically discussed. In addition, the challenges and opportunities of utilizing piezoelectric nanogenerators for self-powered bioelectronics and personalized healthcare are outlined in detail.