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To provide guidelines for the development of textile electrode compression pants that collect reliable signals during surface electromyography (sEMG) measurements and maintain a comfortable level of pressure.

To increase skin adhesion, 12 textile electrode bands for biceps brachii were prepared according to a combination of variables, namely, the type of the textile electrode, the pressure level and the presence or absence of an electrolyte. The dry textile electrode adopted herein was developed in terms of the size and material of the contact area, and a new electrode design was proposed. After examining the optimal design conditions by measuring the sEMGs during isometric exercise of the biceps brachii, prototype pants were designed based on the design variables that gave the most promising evaluation results. The completed prototype pants were verified through isometric thigh muscle exercises.

It was confirmed that the textile electrode was capable of EMG measurement with an excellent signal quality. Upon considering the comfort of wearing the device and the cost efficiency of dry electrodes, prototype pants that adopted a fit relative to a light clothing pressure (i.e. thigh: 1.3–1.9 kPa), and combined both silicon and silver thread embroidery with a wide contact area for stability, were designed and their sEMG measurements were confirmed.

In this study, wearable clothing based on textile electrodes was developed to ensure a comfortable fit from the wearer's perspective, and a design method was proposed for the development of low-cost SmartWear electrodes and circuits.

To compare the electromyography (EMG) features during physical and imagined standing up in healthy young adults.

Twenty-two participants (ages ranged from 20–29 years old) were recruited to participate in this study. Electrodes were attached to the rectus femoris, biceps femoris, tibialis anterior and the medial gastrocnemius muscles of both sides to monitor the EMG features during physical and imagined standing up. The %maximal voluntary contraction (%MVC), onset and duration were calculated.

The onset and duration of each muscle of both sides had no statistically significant differences between physical and imagined standing up (p > 0.05). The %MVC of all four muscles during physical standing up was statistically significantly higher than during imagined standing up (p < 0.05) on both sides. Moreover, the tibialis anterior muscle of both sides showed a statistically significant contraction before the other muscles (p < 0.05) during physical and imagined standing up.

Muscles can be activated during imagined movement, and the patterns of muscle activity during physical and imagined standing up were similar. Imagined movement may be used in rehabilitation as an alternative or additional technique combined with other techniques to enhance the STS skill.

Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates (SWRs). As a first step toward solving these problems, this study aims, firstly, to design a surface electromyography (sEMG) controlled steering assistance interface that enables hands-free steering wheel rotation and, secondly, to validate the effect of this rotation on path-following accuracy.

A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios: U-turn, 90º turn and 45º turn. For comparison, the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface. The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel.

Overall, the steering assistance interface with a maximized SWR was comparable to a game steering wheel. For the U-turn, 90º turn and 45º turn, the sEMG-based human–machine interface (HMI) had median lateral errors of 0.55, 0.3 and 0.2 m, respectively, whereas the game steering wheel, respectively, had median lateral errors of 0.7, 0.4 and 0.3 m. The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn.

Although production automobiles do not use sEMG-based HMIs, and few studies have proposed sEMG controlled steering, the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.

The most commonly described components of customer experience include cognitive and affective aspects. However, the subjective self-reported methods traditionally applied in tourism research cannot fully represent the instant, dynamic, and affective nature of customer experience. Therefore, there is a need for moment-based approaches and longitudinal methods in tourism research. The chapter provides a selective review of measures that can be used to assess the affective aspects of customer experience. Taking into account the advantages and limitations of each method, the integration of self-reported scales, moment-based psychophysiological techniques, and longitudinal methods should be considered as the best approach to measuring affective components of customer experience in tourism. This holistic interdisciplinary approach will help researchers and tourism practitioners understand the relationship between affective and cognitive components of tourists' pre-visit, onsite, and post-visit experience, as well as evaluate the effectiveness of marketing campaigns, identify weak points of tourists' customer journey, and maximize total travel experience.

The aim of this study is to investigate subject independent emotion recognition capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG), electromyography (EMG), electrodermal activity (EDA), temperature, plethysmograph and respiration. The experiments are conducted on both modalities independently and in combination. This study arranges the physiological signals in order based on the prediction accuracy obtained on test data using time and frequency domain features.

DEAP dataset is used in this experiment. Time and frequency domain features of EEG and physiological signals are extracted, followed by correlation-based feature selection. Classifiers namely – Naïve Bayes, logistic regression, linear discriminant analysis, quadratic discriminant analysis, logit boost and stacking are trained on the selected features. Based on the performance of the classifiers on the test set, the best modality for each dimension of emotion is identified.

 The experimental results with EEG as one modality and all physiological signals as another modality indicate that EEG signals are better at arousal prediction compared to physiological signals by 7.18%, while physiological signals are better at valence prediction compared to EEG signals by 3.51%. The valence prediction accuracy of EOG is superior to zygomaticus electromyography (zEMG) and EDA by 1.75% at the cost of higher number of electrodes. This paper concludes that valence can be measured from the eyes (EOG) while arousal can be measured from the changes in blood volume (plethysmograph). The sorted order of physiological signals based on arousal prediction accuracy is plethysmograph, EOG (hEOG + vEOG), vEOG, hEOG, zEMG, tEMG, temperature, EMG (tEMG + zEMG), respiration, EDA, while based on valence prediction accuracy the sorted order is EOG (hEOG + vEOG), EDA, zEMG, hEOG, respiration, tEMG, vEOG, EMG (tEMG + zEMG), temperature and plethysmograph.

Many of the emotion recognition studies in literature are subject dependent and the limited subject independent emotion recognition studies in the literature report an average of leave one subject out (LOSO) validation result as accuracy. The work reported in this paper sets the baseline for subject independent emotion recognition using DEAP dataset by clearly specifying the subjects used in training and test set. In addition, this work specifies the cut-off score used to classify the scale as low or high in arousal and valence dimensions. Generally, statistical features are used for emotion recognition using physiological signals as a modality, whereas in this work, time and frequency domain features of physiological signals and EEG are used. This paper concludes that valence can be identified from EOG while arousal can be predicted from plethysmograph.

One of the main causes of long-term prosthetic abandonment is the lack of ownership over the prosthesis, which was caused mainly by the absence of sensory information regarding the lost limb. The period where the patient learns how to interact with a prosthetic device is critical in rehabilitation. This ideally happens within the first months after amputation, which is also a period associated with the consolidation of brain changes. Different studies have shown that the introduction of feedback mechanisms can be crucial to bypass the lack of sensorial information. To develop a biofeedback system for the rehabilitation of transfemoral amputees – controlled via electromyographic (EMG) activity from the leg muscles – that can provide real-time visual and/or vibratory feedback for the user.

The system uses surface EMG to control two feedback mechanisms, which are the knee joint of a prosthetic leg of a humanoid avatar in a virtual reality (VR) environment (visual feedback) and a matrix of 16 vibrotactile actuators placed in the back of the user (vibratory feedback). Data acquisition was inside a Faraday Cage using an OpenEphys® acquisition board for the surface EMG recordings. The tasks were performed on able-bodied participants, with no amputation, and for this, the dominant leg of the user was immobilized using an orthopedic boot fixed on the chair, allowing only isometric contractions of target muscles, according to the Surface EMG for Non-Invasive Assessment of Muscles (SENIAM) standard. The authors test the effectiveness of combining vibratory and visual feedback and how task difficulty affects overall performance.

The authors' results show no negative interference combining both feedback modalities and that performance peaked at the intermediate difficulty. These results provide powerful insights of what can be accomplished with the population of amputee people. By using this biofeedback system, the authors expect to engage another sensory modality in the process of spatial representation of a virtual leg, bypassing the lack of information associated with the disruption of afferent pathways following amputation.

The authors developed a showcase with a new protocol and feedback mechanisms showing the protocol's safety, efficiency and reliability. However, since this system is designed for patients with leg amputation, the full extent of the effects of the biofeedback training can only be assessed after the evaluation with the amputees, and the results obtained so far establish a safe and operational protocol to accomplish this.

In this study, the authors proposed a new biofeedback device intended to be used in the preprosthetic rehabilitation phase for people with transfemoral amputation. With this new system, the authors propose a mechanism to bypass the lack of sensory information from a virtual prosthesis and help to assimilate visual and vibrotactile stimuli as a cue for movement representation.

With this new system, the authors propose a mechanism to bypass the lack of sensory information from a virtual prosthesis and help to assimilate visual and vibrotactile stimuli as a cue for movement representation.

The authors' results show that all users were capable of recognizing both feedback modalities, both separate and combined, being able to respond accordingly throughout the tasks. The authors also show that for a one-session protocol, the last difficulty level imposed a greater challenge for most users, explained by the significant drop in performance disregarding the feedback modality. Lastly, the authors believe this paradigm can provide a better process for the embodiment of prosthetic devices, fulfilling the lack of sensory information for the users.

Breathing resistance is the main factor that influences the wearing comfort of respirators. This paper aims to demonstrate the feasibility of using the gene expression programming (GEP) for the purpose of predicting subjective perceptions of breathing resistances of wearing respirators via surface electromyography (sEMG) and respiratory signals (RSP) sensors.

The authors developed a physiological signal monitoring system with a specific garment. The inputs included seven physical measures extracted from (RSP) and (sEMG) signals. The output was the subjective index of breathing resistances of wearing respirators derived from the category partitioning-100 scale with proven levels of reliability and validity. The prediction model was developed and validated using data collected from 30 subjects and 24 test combinations (12 respirator conditions × 2 motion conditions). The subjects evaluated 24 conditions of breathing resistances in repeated measures fashion.

The results show that the GEP model can provide good prediction performance (R² = 0.71, RMSE = 0.11). This study demonstrates that subjective perceptions of breathing resistance of wearing respirators on the human body can be predicted using the GEP via sEMG and RSP in real-time, at little cost, non-invasively and automatically.

This is the first paper suggesting that subjective perceptions of subjective breathing resistances can be predicted from sEMG and RSP sensors using a GEP model, which will remain helpful to the scientific community to start further human-centered research work and product development using wearable biosensors and evolutionary algorithms.

This paper aims to propose an innovative adaptive control method for lower-limb rehabilitation robots.

Despite carrying out various studies on the subject of rehabilitation robots, the flexibility and stability of the closed-loop control system is still a challenging problem. In the proposed method, surface electromyography (sEMG) and human force-based dual closed-loop control strategy is designed to adaptively control the rehabilitation robots. A motion analysis of human lower limbs is performed by using a wavelet neural network (WNN) to obtain the desired trajectory of patients. In the outer loop, the reference trajectory of the robot is modified by a variable impedance controller (VIC) on the basis of the sEMG and human force. Thenceforward, in the inner loop, a model reference adaptive controller with parameter updating laws based on the Lyapunov stability theory forces the rehabilitation robot to track the reference trajectory.

The experiment results confirm that the trajectory tracking error is efficiently decreased by the VIC and adaptively correct the reference trajectory synchronizing with the patients’ motion intention; the model reference controller is able to outstandingly force the rehabilitation robot to track the reference trajectory. The method proposed in this paper can better the functioning of the rehabilitation robot system and is expandable to other applications of the rehabilitation field.

The proposed approach is interesting for the design of an intelligent control of rehabilitation robots. The main contributions of this paper are: using a WNN to obtain the desired trajectory of patients based on sEMG signal, modifying the reference trajectory by the VIC and using model reference control to force rehabilitation robot to track the reference trajectory.

This chapter presents a novel human arm gesture tracking and recognition technique based on fuzzy logic and nonlinear Kalman filtering with applications in crane guidance. A Kinect visual sensor and a Myo armband sensor are jointly utilised to perform data fusion to provide more accurate and reliable information on Euler angles, angular velocity, linear acceleration and electromyography data in real time. Dynamic equations for arm gesture movement are formulated with Newton–Euler equations based on Denavit–Hartenberg parameters. Nonlinear Kalman filtering techniques, including the extended Kalman filter and the unscented Kalman filter, are applied in order to perform reliable sensor fusion, and their tracking accuracies are compared. A Sugeno-type fuzzy inference system is proposed for arm gesture recognition. Hardware experiments have shown the efficacy of the proposed method for crane guidance applications.