Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence—High Arousal, High Valence—Low Arousal, Low Valence—High Arousal, and Low Valence—Low Arousal. Emotion elicitation average accuracy of $98.73\%$ is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.

Stroke has been one of the leading causes of disability worldwide and is still a social health issue. Keeping in view the importance of physical rehabilitation of stroke patients, an analytical review has been compiled in which different therapies have been reviewed for their effectiveness, such as functional electric stimulation (FES), noninvasive brain stimulation (NIBS) including transcranial direct current stimulation (t-DCS) and transcranial magnetic stimulation (t-MS), invasive epidural cortical stimulation, virtual reality (VR) rehabilitation, task-oriented therapy, robot-assisted training, tele rehabilitation, and cerebral plasticity for the rehabilitation of upper extremity motor impairment. New therapeutic rehabilitation techniques are also being investigated, such as VR. This literature review mainly focuses on the randomized controlled studies, reviews, and statistical meta-analyses associated with motor rehabilitation after stroke. Moreover, with the increasing prevalence rate and the adverse socio-economic consequences of stroke, a statistical analysis covering its economic factors such as treatment, medication and post-stroke care services, and risk factors (modifiable and non-modifiable) have also been discussed. This review suggests that if the prevalence rate of the disease remains persistent, a considerable increase in the stroke population is expected by 2025, causing a substantial economic burden on society, as the survival rate of stroke is high compared to other diseases. Compared to all the other therapies, VR has now emerged as the modern approach towards rehabilitation motor activity of impaired limbs. A range of randomized controlled studies and experimental trials were reviewed to analyse the effectiveness of VR as a rehabilitative treatment with considerable satisfactory results. However, more clinical controlled trials are required to establish a strong evidence base for VR to be widely accepted as a preferred rehabilitation therapy for stroke.

Purpose: There is growing evidence that vertebral column function and dysfunction play a vital role in neuromuscular control. This invited review summarises the evidence about how vertebral column dysfunction, known as a central segmental motor control (CSMC) problem, alters neuromuscular function and how spinal adjustments (high-velocity, low-amplitude or HVLA thrusts directed at a CSMC problem) and spinal manipulation (HVLA thrusts directed at segments of the vertebral column that may not have clinical indicators of a CSMC problem) alters neuromuscular function.Methods: The current review elucidates the peripheral mechanisms by which CSMC problems, the spinal adjustment or spinal manipulation alter the afferent input from the paravertebral tissues. It summarises the contemporary model that provides a biologically plausible explanation for CSMC problems, the manipulable spinal lesion. This review also summarises the contemporary, biologically plausible understanding about how spinal adjustments enable more efficient production of muscular force. The evidence showing how spinal dysfunction, spinal manipulation and spinal adjustments alter central multimodal integration and motor control centres will be covered in a second invited review.Results: Many studies have shown spinal adjustments increase voluntary force and prevent fatigue, which mainly occurs due to altered supraspinal excitability and multimodal integration. The literature suggests physical injury, pain, inflammation, and acute or chronic physiological or psychological stress can alter the vertebral column’s central neural motor control, leading to a CSMC problem. The many gaps in the literature have been identified, along with suggestions for future studies.Conclusion: Spinal adjustments of CSMC problems impact motor control in a variety of ways. These include increasing muscle force and preventing fatigue. These changes in neuromuscular function most likely occur due to changes in supraspinal excitability. The current contemporary model of the CSMC problem, and our understanding of the mechanisms of spinal adjustments, provide a biologically plausible explanation for how the vertebral column’s central neural motor control can dysfunction, can lead to a self-perpetuating central segmental motor control problem, and how HVLA spinal adjustments can improve neuromuscular function.

Background: Indirect vibration stimulation, i.e., whole body vibration or upper limb vibration, has been investigated increasingly as an exercise intervention for rehabilitation applications. However, there is a lack of evidence regarding the effects of graded isometric contractions superimposed on whole body vibration stimulation. Hence, the objective of this study was to quantify and analyse the effects of variations in the vibration parameters and contraction levels on the neuromuscular responses to isometric exercise superimposed on whole body vibration stimulation. Methods: In this study, we assessed the ‘neuromuscular effects’ of graded isometric contractions, of 20%, 40%, 60%, 80% and 100% of maximum voluntary contraction, superimposed on whole body vibration stimulation (V) and control (C), i.e., no-vibration in 12 healthy volunteers. Vibration stimuli tested were 30 Hz and 50 Hz frequencies and 0.5 mm and 1.5 mm amplitude. Surface electromyographic activity of the vastus lateralis, vastus medialis and biceps femoris were measured during V and C conditions with electromyographic root mean square and electromyographic mean frequency values used to quantify muscle activity and their fatigue levels, respectively. Results: Both the prime mover (vastus lateralis) and the antagonist (biceps femoris) displayed significantly higher (P < 0.05) electromyographic activity with the V than the C condition with varying percentage increases in EMG root-mean-square (EMGrms) values ranging from 20% to 200%. For both the vastus lateralis and biceps femoris, the increase in mean EMGrms values depended on the frequency, amplitude and muscle contraction level with 50 Hz–0.5 mm stimulation inducing the largest neuromuscular activity. Conclusions: These results show that the isometric contraction superimposed on vibration stimulation leads to higher neuromuscular activity compared to isometric contraction alone in the lower limbs. The combination of the vibration frequency with the amplitude and the muscle tension together grades the final neuromuscular output.

Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system’s performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones’ ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.