This special issue highlights some of the best work presented at the 4th International Symposium on Wearable Robotics (WeRob2018), held October 16–20, 2018, in Pisa, Italy. The papers focus on new technologies in the areas of neural interfaces, soft wearable robots, sensor and actuator technologies, and robotic exoskeletons. Based on the quality and significance of presentations at the conference,

Loaded walking with a rucksack results in both gravitational and inertial forces of the load that must be borne by human carriers. The inertial force may be the source of metabolic burden and musculoskeletal injuries. This paper presents a lightweight backpack with a disturbance observer-based acceleration control to minimize the inertial force. The backpack was evaluated by seven participants walking

Human operator control of brain-actuated robot steering based on electroencephalograph (EEG)-signals is a complex behavior consisting of surroundings perceiving, decision making, and commands issuing and differs among individual operators. However, no existing models allow decoupling the user from the loop to improve the system design and testing process, which can capture such behavior of a brain-actuated

Objective : There has been a constant increase in life expectancy with the advancement of modern medicine. Likewise, dementia has also increased and projected to elevate in the coming decades with the higher expenditure on healthcare. Consequently, it is essential to identify early dementia, e.g., a patient suffering from mild cognitive impairment who is highly vulnerable to developing dementia soon

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems to detect sensitive targets. However, asynchronous BCI systems based on video-target-evoked ERPs can pose a challenge in real-world applications due to the absence of an explicit target onset time and the time jitter of the detection latency. To address this challenge, we developed an asynchronous detection framework

Humanoid robots are widely used in brain computer interface (BCI). Using a humanoid robot stimulus could increase the amplitude of event-related potentials (ERPs), which improves BCI performance. Since a humanoid robot contains many human elements, the element that increases the ERPs amplitude is unclear, and how to test the effect of it on the brain is a problem. This study used different graphic

Sleep stage classification constitutes an important element of sleep disorder diagnosis. It relies on the visual inspection of polysomnography records by trained sleep technologists. Automated approaches have been designed to alleviate this resource-intensive task. However, such approaches are usually compared to a single human scorer annotation despite an inter-rater agreement of about 85% only. The

Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier’s disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This

Objective: When evaluating methods for machine-learning controlled prosthetic hands, able-bodied participants are often recruited, for practical reasons, instead of participants with upper limb absence (ULA). However, able-bodied participants have been shown to often perform myoelectric control tasks better than participants with ULA. It has been suggested that this performance difference can be reduced

Individuals with chronic hemiparesis post-stroke exhibit gait impairments that require functional rehabilitation through training. Exoskeletal robotic assistive devices can provide a user with continuous assistance but impose movement restrictions. There are currently devices that allow unrestricted movement but provide assistance only intermittently at specific points of the gait cycle. Our design

Using a shoulder harness and control cable, a person can control the opening and closing of a body-powered prosthesis prehensor. In many setups the cable does not pass adjacent to the shoulder joint center allowing shoulder flexion on the prosthetic side to be used for prehensor control. However, this makes cable setup a difficult compromise as prosthesis control is dependent on arm posture; too short

Muscle synergy analysis is commonly used to study how the nervous system coordinates the activation of a large number of muscles during human reaching. In synergy analysis, muscle activation data collected from various reaching directions are subjected to dimensionality reduction techniques to extract muscle synergies. Typically, muscle activation data are obtained only from a limited set of reaches

Robot-measured kinematic variables are increasingly used in neurorehabilitation to characterize motor recovery following stroke. However, few studies have evaluated the reliability of these kinematic variables. This study aimed at evaluating the test-retest reliability of typically-used robot-measured kinematic variables in healthy subjects (HS) and patients with stroke (SP). Sixty-one participants

Selecting actuators for assistive exoskeletons involves decisions in which designers usually face contrasting requirements. While certain choices may depend on the application context or design philosophy, it is generally desirable to avoid oversizing actuators in order to obtain more lightweight and transparent systems, ultimately promoting the adoption of a given device. In many cases, the torque

Mental fatigue deteriorates ability to perform daily activities − known as time-on-task (TOT) effect and becomes a common complaint in contemporary society. However, an applicable technique for fatigue detection/prediction is hindered due to substantial inter-subject differences in behavioural impairment and brain activity. Here, we developed a fully cross-validated, data-driven analysis framework

Artificial intelligence provides new feasibilities to the control of dexterous prostheses. To achieve suitable grasps over various objects, a novel computer vision-based classification method assorting objects into different grasp patterns is proposed in this paper. This method can be applied in the autonomous control of the multi-fingered prosthetic hand, as it can help users rapidly complete “reach-and-pick

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While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain

The convolutional neural network (CNN) model is an active research topic in the field of EEG signals analysis. However, the classification effect of CNN on EEG signals of amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) is not ideal. Even if EEG signals are transformed into multispectral images that are more closely matched with the model, the best classification performance

About 1% of the population around the world suffers from epilepsy. The success of epilepsy surgery depends critically on pre-operative localization of epileptogenic zones. High frequency oscillations including ripples (80-250 Hz) and fast ripples (250-500 Hz) are commonly used as biomarkers to localize epileptogenic zones. Recent literature demonstrated that fast ripples indicate epileptogenic zones

Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize

Next generation prosthetics will rely massively on myoelectric ”Pattern Recognition” (PR) based control approaches, to improve their users’ dexterity. One major identified factor of successful functioning of these approaches lies in the training of amputees and in their understanding of how those prosthetics works. We thus propose here an intuitive pattern similarity biofeedback which can be easily

Objective: Classification of the neural activity of the brain is a well known problem in the field of brain computer interface. Machine learning based approaches for classification of brain activities do not reveal the underlying dynamics of the human brain. Methods: Since eigen decomposition has been found useful in a variety of applications, we conjecture that change of brain states would manifest

The viability of electroencephalogram (EEG) based vocal imagery (VIm) and vocal intention (VInt) Brain-Computer Interface (BCI) systems has been investigated in this study. Four different types of experimental tasks related to humming has been designed and exploited here. They are: (i) non-task specific (NTS), (ii) motor task (MT), (iii) VIm task, and (iv) VInt task. EEG signals from seventeen participants

Objective: Custom foot orthoses (CFOs) are typically used for the prevention and cure of lower extremity injuries (LEIs). Typically, CFOs are designed and prescribed based on iterative loops including: (1) follow-up loops between the patient and the physician, and (2) design feedback loops between the physician and the fabricator. The current prescription methodology has some deficiencies, i.e. excessive

Most research in Brain-Computer-Interfaces (BCI) focuses on technologies to improve accuracy and speed. Little has been done on the effects of subject variability, both across individuals and within the same individual, on BCI performance. For example, stress, arousal, motivation, and fatigue can all affect the electroencephalogram (EEG) signals used by a BCI, which in turn impacts performance. Overcoming

For active AFO applications, pneumatic remote transmission has advantages in minimizing the mass and complexity of the system due to the flexibility in placing pneumatic components and providing high back-drivability via simple valve control. However, pneumatic systems are generally tethered to large stationary air compressors, which greatly limit the practical daily usage. In this study, we implemented

Although driving fatigue has long been recognized as one of the leading causes of fatal accidents worldwide, the underlying neural mechanisms remain largely unknown that impedes the developments of automatic detection techniques. This study investigated the effects of driving fatigue on the reorganization of dynamic functional connectivity (FC) through our newly developed temporal brain network analysis

Mechanical impedance, which changes with posture and muscle activations, characterizes how the central nervous system regulates the interaction with the environment. Traditional approaches to impedance estimation, based on averaging of movement kinetics, requires a large number of trials and may introduce bias to the estimation due to the high variability in a repeated or periodic movement. Here, we

Accurate predictions of joint contact forces through computer simulation of musculoskeletal dynamics can provide insight, in a non-invasive manner, into the joint loads of patients with osteoarthritis and healthy controls. The current approach to assume optimal control, in terms of metabolic energy expenditure, remains a major limitation of the prediction of muscle activation patterns that determine

The reliability (precision) and agreement (accuracy) of anthropometric measurements based on manually placed 3D landmarks using the RealSense D415 were investigated in this paper. Thirty facial palsy patients, with their face in neutral (resting) position, were recorded simultaneously with the RealSense and a professional 3dMD imaging system. First the RealSense depth accuracy was determined. Subsequently

Motion and muscle artifacts can undermine signal quality in electroencephalography (EEG) recordings during locomotion. We evaluated approaches for recovering ground-truth artificial brain signals from noisy EEG recordings. We built an electrical head phantom that broadcast four brain and four muscle sources. Head movements were generated by a robotic motion platform. We recorded 128-channel dual layer

The objective of this study is to explore the diagnostic decision and sensitivity of the surface electromyogram (EMG) clustering index (CI) with respect to post-stroke motor unit (MU) alterations through a simulation approach by the existing motor neuron pool model and surface EMG model. In the simulation analysis, three patterns of diagnostic decisions were presented in 24 groups representing eight

Motor imagery based brain-computer interface (MI-BCI) has been studied for improvement of patients’ motor function in neurorehabilitation and motor assistance. However, the difficulties in performing imagery tasks limit its application. To overcome the limitation, an enhanced MI-BCI based on functional electrical stimulation (FES) and virtual reality (VR) is proposed in this study. On one hand, the

Much attention has been dedicated to clinical research of focal epilepsy, but the ability to derive a successful seizure control strategy based on unique dynamical features of the electroencephalogram is an unsolved problem. In this work, we introduce a basic model of spontaneous seizure dynamics and construct from it to a network model of focal-onset seizure dynamics. The full model is composed of

Objective: Proprioceptive senses play an important role in human body reflex and movement, so far implementing physiological mathematical models of proprioceptors in the musculoskeletal model and investigating their effects have not been sufficiently investigated. The purpose of the present study was to establish a compact framework for a lower limb musculoskeletal model by considering both ascending

The title of [1] contains a typo. “Desire of Use: A Hierarchical Decomposition of Activities and Its Application on Mobility of Blind and Low-Vision Individuals” is the correct title.

Knee injuries at risk of post-traumatic knee osteoarthritis (PTOA) and knee osteoarthritis (OA) are closely associated with knee transverse plane and/or frontal plane instability and excessive loading. However, most existing training and rehabilitation devices involve mainly movements in the sagittal plane. An offaxis elliptical training system was developed to train and evaluate neuromuscular control

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On July 1, 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE) will be entirely Open Access.We are making this change in response to a growing consensus among our authors and readers—as well as research funders worldwide—that research results should be freely available to the entire scientific community, regardless of the ability to pay to read.

Electromyography (EMG) is a source of neural information for controlling neuroprosthetic devices. To enhance the information content of conventional bipolar EMG, high-density EMG systems include tens to hundreds of closely spaced electrodes that non-invasively record the electrical activity of muscles with high spatial resolution. Despite the advantages of relying on multiple signal sources, however

Working memory (WM) – one of the brain ability that maintains information – can evaluate the function of brain. Activities related to memory sustention, inhibition and disinhibition have gathered significant attention for the basic neurocognitive architecture. Although researchers have proposed some brain models that attempt to explain the entire procedure of WM, little evidence can proof and describe

In sensorimotor control, sensory feedback integrates with forward models to alleviate the impacts of sensory noise and delay on state estimation. The sensorimotor integration is subject to Bayesian inference and has been formulated by the Kalman filter in computational neuroscience. However, the Kalman filter, as an artificial optimal estimator to address the abstract characteristics of spatial perception

Motor learning-based methods offer an alternative paradigm to machine learning-based methods for controlling upper-limb prosthetics. Within this paradigm, the patterns of muscular activity used for control can differ from those which control biological limbs. Practice expedites the learning of these new, functional patterns of muscular activity. We envisage that these methods can result in enhanced

The coherence of neural activities among different areas in the brain has received great attention because it is valuable in understanding the functional mechanism of brain structures. While many methodologies, such as time-frequency and entropy analysis, have been applied to evaluate relations between neural signals, these techniques haven’t been effective in assessing neural communication in order

Improving the understanding of hand kinematics during the performance of activities of daily living may help improve the control of hand prostheses and hand function assessment. This work identifies sparse synergies (each degree of freedom is present mainly in only one synergy), representative of the global population, with emphasis in unveiling the coordination of joints with small range of motion

The purpose of this study is to investigate the effects of different cursor click modalities in an alternative computer access device using accelerometry from head tilt to control cursor movement. Eighteen healthy adults performed a target acquisition task using the device with five different cursor click modalities, while maintaining cursor movement control via accelerometry. Three dwell-based click

Benchmarks have long been used to verify and compare the readiness level of different technologies in many application domains. In the field of wearable robots, the lack of a recognized benchmarking methodology is one important impediment that may hamper the efficient translation of research prototypes into actual products. At the same time, an exponentially growing number of research studies are addressing

Present robots for investigating lower-limb motor control and rehabilitation focus on gait training. An alternative approach is to focus on restoring precursor abilities such as motor adaptation and volitional movement, as is common in upper-limb robotic therapy. Here we describe NOTTABIKE, a one degree-of-freedom rehabilitation robot designed to probe and promote these underlying capabilities. A recumbent

Limb position is a factor that negatively affects myoelectric pattern recognition classification accuracy. However, prior studies evaluating impact on real-time control for upper-limb amputees have done so without a physical prosthesis on the residual limb. It remains unclear how limb position affects real-time pattern recognition control in amputees when their residual limb is supporting various weights

Visual information plays an essential role in enhancing neural activity during mental practices. Previous research has shown that using different visual scenarios during mental practices that involve imagining the movement of a specific body part may result in differences in performance. Many of these scenarios utilize the concept of embodiment, or one’s observation of another entity to be a part of

Prediction of human voluntary gait intention is a very significant task to ensure direct cortical control of real-life assistive technologies for locomotion rehabilitation. Neurophysiological studies provide that human voluntary gait intention is represented by slow DC potentials and power shifts in specific frequency ranges of brain wave, which can be detected 1.5- 2 seconds before the actual onset

Gait disturbances in people with Parkinson’s disease (PD) are a major cause for functional dependence and have recently been shown to be the largest risk factor for falls, institutionalization and death in PD. The use of external cues has been successful at improving gait in people with PD, but the effect of external cues on gait stability is unclear. We examined whether different forms of cueing,

Spasticity is a major impairment that can occur following a hemispheric stroke and is often treated with injections of botulinum toxin, a neurotoxin that impairs transmission at the neuromuscular junction. Hyperreflexia is a defining feature of spasticity. Our main objective here was to quantify the time course of changes in the deep tendon reflex (DTR) responses and voluntary activation capacity following