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A contactless electromyography (EMG) electrodes design and development for prosthetics, particularly the Touch Hand 3, was the main objective of this paper. The correlation between EMG electrodes and patch antenna are described, with the problem relating to the dimensions of the covidien electrodes. The purpose of this paper is to improve the signal strength of the EMG electrodes and having them to not be in contact with the skin to cause irritation in the person.

A combination of the contact covidien electrodes and aluminium foil was used to develop electrodes that were in a similar configuration than a Yagi antenna. Different layers of patch elements were designed, developed and implemented.

Different layers of Yagi-patch electrodes are tested with different volunteers and compared with the average signal strengths obtained from the covidien electrodes. An improvement in signal strength with the Yagi-patch electrodes has been found.

The purpose of the work was to design, develop and test EMG electrodes that are cost-effective, reusable and able to improve the signal strengths that are recorded, for better functionality of prosthetic devices.

The integration of EMG and antennae theory to implement a Yagi-patch EMG electrode to improve on signal reception. The electrodes have the properties of being cheap, easy available, can eliminate direct contact and avoiding patches on the skin. Comparison of different layered electrodes with the contactless electrodes close to the skin. Comparison of the different electrodes on a silicone sleeve, which are commonly worn by amputees, placed between the skin and the prosthetic’s socket. Testing the Yagi-patch electrodes with an application with the prosthetic Touch Hand, to allow for the control of a system such as the Touch Hand.

The purpose of this paper is to design and develop a new robotic device for the rehabilitation of the upper limbs. The authors are focusing on a new symmetrical robot which can be used to rehabilitate the right upper limb and the left upper limb. The robotic arm can be automatically extended or reduced depending on the measurements of the patient's arm. The main idea is to integrate electrical stimulation into motor rehabilitation by robot. The goal is to provide automatic electrical stimulation based on muscle status during the rehabilitation process.

The developed robotic arm can be automatically extended or reduced depending on the measurements of the patient's arm. The system merges two rehabilitation strategies: motor rehabilitation and electrical stimulation. The goal is to take the advantages of both approaches. Electrical stimulation is often used for building muscle through endurance, resistance and strength exercises. However, in the proposed approach the electrical stimulation is used for recovery, relaxation and pain relief. In addition, the device includes an electromyography (EMG) muscle sensor that records muscle activity in real time. The control architecture provides the ability to automatically activate the appropriate stimulation mode based on the acquired EMG signal. The system software provides two modes for stimulation activation: the manual preset mode and the EMG driven mode. The program ensures traceability and provides the ability to issue a patient status monitoring report.

The developed robotic device is symmetrical and reconfigurable. The presented rehabilitation system includes a muscle stimulator associated with the robot to improve the quality of the rehabilitation process. The integration of neuromuscular electrical stimulation into the physical rehabilitation process offers effective rehabilitation sessions for neuromuscular recovery of the upper limb. A laboratory-made stimulator is developed to generate three modes of stimulation: pain relief, massage and relaxation. Through the control software interface, the physiotherapist can set the exercise movement parameters, define the stimulation mode and record the patient training in real time.

There are certain constraints when applying the proposed method, such as the sensitivity of the acquired EMG signals. This involves the use of professional equipment and mainly the implementation of sophisticated algorithms for signal extraction.

Functional electrical stimulation and robot-based motor rehabilitation are the most important technologies applied in post-stroke rehabilitation. The main objective of integrating robots into the rehabilitation process is to compensate for the functions lost in people with physical disabilities. The stimulation technique can be used for recovery, relaxation and drainage and pain relief. In this context, the idea is to integrate electrical stimulation into motor rehabilitation based on a robot to obtain the advantages of the two approaches to further improve the rehabilitation process. The introduction of this type of robot also makes it possible to develop new exciting assistance devices.

The proposed design is symmetrical, reconfigurable and light, covering all the joints of the upper limbs and their movements. In addition, the developed platform is inexpensive and a portable solution based on open source hardware platforms which opens the way to more extensions and developments. Electrical stimulation is often used to improve motor function and restore loss of function. However, the main objective behind the proposed stimulation in this paper is to recover after effort. The novelty of the proposed solution is to integrate the electrical stimulation powered by EMG in robotic rehabilitation.

The purpose of this study is to present a novel hybrid closed-loop control method together with its performance validation for the dexterous prosthetic hand.

The hybrid closed-loop control is composed of a high-level closed-loop control with the user in the closed loop and a low-level closed-loop control for the direct robot motion control. The authors construct the high-level control loop by using electromyography (EMG)-based human motion intent decoding and electrical stimulation (ES)-based sensory feedback. The human motion intent is decoded by a finite state machine, which can achieve both the patterned motion control and the proportional force control. The sensory feedback is in the form of transcutaneous electrical nerve stimulation (TENS) with spatial-frequency modulation. To suppress the TENS interfering noise, the authors propose biphasic TENS to concentrate the stimulation current and the variable step-size least mean square adaptive filter to cancel the noise. Eight subjects participated in the validation experiments, including pattern selection and egg grasping tasks, to investigate the feasibility of the hybrid closed-loop control in clinical use.

The proposed noise cancellation method largely reduces the ES noise artifacts in the EMG electrodes by 18.5 dB on average. Compared with the open-loop control, the proposed hybrid closed-loop control method significantly improves both the pattern selection efficiency and the egg grasping success rate, both in blind operating scenarios (improved by 1.86 s, p < 0.001, and 63.7 per cent, p < 0.001) or in common operating scenarios (improved by 0.49 s, p = 0.008, and 41.3 per cent, p < 0.001).

The proposed hybrid closed-loop control method can be implemented on a prosthetic hand to improve the operation efficiency and accuracy for fragile objects such as eggs.

The primary contribution is the proposal of the hybrid closed-loop control, the spatial-frequency modulation method for the sensory feedback and the noise cancellation method for the integrating of the myoelectric control and the ES-based sensory feedback.

The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research.

Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143.

Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially.

Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject.

The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

The purpose of this paper is to present a five-fingered, multisensory prosthetic hand integrating both intuitive myoelectric control and sensory feedback.

The artificial hand’s palm has a three-arcuate configuration and the thumb can move along a cone surface, improving the resemblance with the biological hand. By using a coupling linkage mechanism, each finger is independently actuated by a direct current motor. Both torque and position sensors are embedded in the finger to sense the hand’s status and its interaction with the outer environment. The proposed human-in-the-loop control system consists of an internal motion control scheme and an external human–machine interface. The pattern recognition-based electromyography (EMG) control scheme is adopted to control the motion of the hand, and the transcutaneous electrical nerve stimulation (TENS) is utilized to feedback the hand’s sensory information to its user.

The hand prototype shows that it has an anthropomorphic appearance (85 per cent to an average human hand), low weight (420 g), great power (10 N on the fingertip) and eligible dexterity. Clinical evaluation of the prosthetic hand on transradial amputees also approves the hand design.

From a systematic view, the paper details the design concepts of the HIT–DLR prosthetic hand IV, especially on its appearance, mechanism, myoelectric control and sensory feedback.

The main aim of this paper is to design a technique for improving the quality of EEG signal by removing artefacts which is obtained during acquisition. Initially, pre-processing is done on EEG signal for quality improvement. Then, by using wavelet transform (WT) feature extraction is done. The artefacts present in the EEG are removed using deep convLSTM. This deep convLSTM is trained by proposed fractional calculus based flower pollination optimisation algorithm.

Nowadays' EEG signals play vital role in the field of neurophysiologic research. Brain activities of human can be analysed by using EEG signals. These signals are frequently affected by noise during acquisition and other external disturbances, which lead to degrade the signal quality. Denoising of EEG signals is necessary for the effective usage of signals in any application. This paper proposes a new technique named as flower pollination fractional calculus optimisation (FPFCO) algorithm for the removal of artefacts from EEG signal through deep learning scheme. FPFCO algorithm is the integration of flower pollination optimisation and fractional calculus which takes the advantages of both the flower pollination optimisation and fractional calculus which is used to train the deep convLSTM. The existed FPO algorithm is used for solution update through global and local pollinations. In this case, the fractional calculus (FC) method attempts to include the past solution by including the second order derivative. As a result, the suggested FPFCO algorithm approaches the best solution faster than the existing flower pollination optimization (FPO) method. Initially, 5 EEG signals are contaminated by artefacts such as EMG, EOG, EEG and random noise. These contaminated EEG signals are pre-processed to remove baseline and power line noises. Further, feature extraction is done by using WT and extracted features are applied to deep convLSTM, which is trained by proposed fractional calculus based flower pollination optimisation algorithm. FPFCO is used for the effective removal of artefacts from EEG signal. The proposed technique is compared with existing techniques in terms of SNR and MSE.

The proposed technique is compared with existing techniques in terms of SNR, RMSE and MSE.

100%.

The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.

Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.

This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.

The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

The purpose of this paper is to eliminate instability which may occur when a human stiffens his arms in physical human–robot interaction by estimating the human hand stiffness and presenting a modified vibration index.

Human hand stiffness is first estimated in real time as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A time-domain vibration index based on the interaction force is then modified to reduce the delay in instability detection. The instability is confirmed when the vibration index exceeds a given threshold. The virtual damping coefficient in admittance controller is adjusted accordingly to ensure stability in physical human–robot interaction.

By estimating the human hand stiffness and modifying the vibration index, the instability which may occur in stiff environment in physical human–robot interaction is detected and eliminated, and the time delay is reduced. The experimental results demonstrate significant improvement in stabilizing the system when the human operator stiffens his arms.

The originality is in estimating the human hand stiffness online as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A modification of the vibration index is also an originality to reduce the time delay of instability detection.

Sleep arousal detection is an important factor to monitor the sleep disorder.

Thus, a unique nth layer one-dimensional (1D) convolutional neural network-based U-Net model for automatic sleep arousal identification has been proposed.

The proposed method has achieved area under the precision–recall curve performance score of 0.498 and area under the receiver operating characteristics performance score of 0.946.

No other researchers have suggested U-Net-based detection of sleep arousal.

From the experimental results, it has been found that U-Net performs better accuracy as compared to the state-of-the-art methods.

Sleep arousal detection is an important factor to monitor the sleep disorder. Objective of the work is to detect the sleep arousal using different physiological channels of human body.

It will help in improving mental health by monitoring a person's sleep.

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.