IEEE Transactions on Neural Systems and Rehabilitation Engineering

Physical rehabilitation techniques during the treatment of clinical pathology are one of the most challenging areas for the medical structure, patients, and families. In large and continental countries, remote monitoring of this treatment is essential. However, equipment and medical follow-up during exercises still have high costs. With the improvement of computer vision and machine learning techniques, some computational, less expensive alternatives have been proposed in the literature. However, monitoring patients during physical rehabilitation exercises with the help of artificial intelligence by a health professional, especially from the capture of visual signals, is still a challenge and poorly explored in the scientific-technological literature...

Telerehabilitation technology often helps individuals with Parkinson's disease (PD) to control their balance and improve postural stability. This proof-of-concept study describes the redesign of a smartphone-based wearable balance rehabilitation system, or Smarter Balance System (SBS) intended for in-home use, and determines the number of exercise sessions required to achieve steady-state balance exercise performance by people with PD who performed 6 weeks of in-home dynamic weight-shifting balance exercises...

Steady-state visual evoked potential (SSVEP) is one of the most popular brain-computer interface (BCI) paradigms, with high information transmission rate and signal-to-noise ratio. Many calibration-free and calibration-based approaches have been proposed to improve the performance of SSVEP-based BCIs. This paper considers a quick calibration scenario, where there are plenty of data from multiple source subjects, but only a small number of calibration trials from a subset of stimulus frequencies for the new subject...

Many persons with stroke exhibit upper extremity motor impairments. These impairments often lead to dysfunction and affect performance in activities of daily living, where successful manipulation of objects is essential. Hence, understanding how upper extremity motor deficits manifest in functional interactions with objects is critical for rehabilitation. However, quantifying skill in these tasks has been a challenge. Traditional rehabilitation assessments require highly trained clinicians, are time-consuming, and yield subjective scores...

Quantitative evaluation of human stability using foot pressure/force measurement hardware and motion capture (mocap) technology is expensive, time consuming, and restricted to the laboratory. We propose a novel image-based method to estimate three key components for stability computation: Center of Mass (CoM), Base of Support (BoS), and Center of Pressure (CoP). Furthermore, we quantitatively validate our image-based methods for computing two classic stability measures, CoMtoCoP and CoMtoBoS distances, against values generated directly from laboratory-based sensor output (ground truth) using a publicly available, multi-modality (mocap, foot pressure, two-view videos), ten-subject human motion dataset...

Emotion analysis has been employed in many fields such as human-computer interaction, rehabilitation, and neuroscience. But most emotion analysis methods mainly focus on healthy controls or depression patients. This paper aims to classify the emotional expressions in individuals with hearing impairment based on EEG signals and facial expressions. Two kinds of signals were collected simultaneously when the subjects watched affective video clips, and we labeled the video clips with discrete emotional states (fear, happiness, calmness, and sadness)...

Optimizing skill acquisition during novel motor tasks and regaining lost motor functions have been the interest of many researchers over the past few decades. One approach shown to accelerate motor learning involves haptically coupling two individuals through robotic interfaces. Studies have shown that an individual's solo performance during upper-limb tracking tasks may improve after haptically-coupled training with a partner. In this study, our goal was to investigate whether these findings can be translated to lower-limb motor tasks, more specifically, during an ankle position tracking task...

Fear of Fall (FoF) is often associated with postural and gait abnormalities leading to decreased mobility in individuals with Parkinson's Disease (PD). The variability in knee flexion (postural index) during heel-strike and toe-off events while walking can be related to one's FoF. Depending on the progression of the disease, gait abnormality can be manifested as start/turn/stop hesitation, etc. adversely affecting one's cadence along with an inability to transfer weight from one leg to the other. Also, task demands can have implications on one's gait and posture...

Neuromodulation is used to treat neurological disorders. Focused ultrasound can deliver acoustic energy to local regions of the brain, including deep brain structures. In addition, it is possible to induce the activation or inhibition of nerves through parameter adjustments of focused ultrasound. Laser-generated focused ultrasound (LGFUS) has demonstrated a potential use in precise therapeutic ultrasound applications owing to the ability to produce high-pressure, broadband frequency of shock waves with a tight focal spot, resulting in confined acoustic exposure of a small area...

The traditional polysomnography (PSG) examination for Obstructive Sleep Apnea (OSA) diagnosis needs to measure several signals, such as EEG, ECG, EMG, EOG and the oxygen level in blood, of a patient who may have to wear many sensors during sleep. After the PSG examination, the Apnea-Hypopnea Index (AHI) is calculated based on the measured data to evaluate the severity of apnea and hypopnea for the patient. This process is obviously complicated and inconvenient. In this paper, we propose an AI-based framework, called RAre Pattern Identification and DEtection for Sleep-stage Transitions (RAPIDEST), to detect OSA based on the sequence of sleep stages from which a novel rarity score is defined to capture the unusualness of the sequence of sleep stages...

In recent years, with the widespread popularity of the Internet, social media has become an indispensable part of people's lives. People regard online social media as an essential tool for interaction and communication. Due to the convenience of data acquisition from social media, mental health research on social media has received a lot of attention. The early detection of psychological disorder based on social media can help prevent further deterioration in at-risk people. In this paper, depression detection is performed based on non-verbal (acoustics and visual) behaviors of vlog...

Prosthetic discrete controller relies on finite state machines to switch between a set of predefined task-specific controllers. Therefore the prosthesis can only perform a limited number of discrete locomotion tasks and need hours to tune the parameters for each user. In contrast, the continuous controller treats a gait cycle in a unified way. Thus it is expected to better facilitate normative biomechanics by providing a gait predictive model to contribute a non-switching controller that supports a continuum of tasks...

Forearm amputees can use body-powered hooks and myoelectric hands for their daily activities. The body-powered hooks are suitable for delicate manipulation. However, their appearance is not always preferred by amputees, and a harness to pull a control cable is not easy to wear. Although the myoelectric hands have a natural appearance similar to the human hand and can be intuitively controlled by a myoelectric control system, they are not easy to try out and are heavy. This paper reports on the Finch, a prosthetic arm with three opposing fingers controlled by a muscle bulge...

Infantile spasms (IS) is a typical childhood epileptic disorder with generalized seizures. The sudden, frequent and complex characteristics of infantile spasms are the main causes of sudden death, severe comorbidities and other adverse consequences. Effective prediction is highly critical to infantile spasms subjects, but few related studies have been done in the past. To address this, this study proposes a seizure prediction framework for infantile spasms by combining the statistical analysis and deep learning model...

Transcranial direct current stimulation (tDCS) has become a new method of rehabilitation treatment for stroke and is gradually gaining acceptance. However, the neurophysiological mechanisms of tDCS in the treatment of stroke still need to be further explored. In our research, we aimed to investigate the effects of tDCS on neural oscillation power and brain functional network (BFN) connectivity in stroke patients based on electroencephalogram (EEG). Fifteen patients with ischemic stroke attended two experimental sessions in a randomized crossover trial and received real and sham tDCS...

We have previously shown that healthy subjects can transfer coordination skills to the unpracticed hand by performing a unimanual task with the other hand and visualizing a bimanual action using a game-like interactive system. However, whether this system could be used to transfer coordination skills to the paretic hand after stroke and its underlying neural mechanism remain unknown. Here, using a game-like interactive system for visualization during physical practice in an immersive virtual reality environment, we examined coordination skill improvement in the unpracticed/paretic hand after training in 10 healthy subjects and 13 chronic and sub-acute stroke patients...

Depression is a severe psychiatric illness that causes emotional and cognitive impairment and has a considerable impact on patients' thoughts, behaviors, feelings and well-being. Moreover, methods for recognizing and treating depression are lacking in clinical practice. Electroencephalogram (EEG) signals, which objectively reflect the internal workings of the brain, is a promising and objective tool for recognizing and diagnosing of depression and enhancing clinical effects. However, previous EEG feature extraction methods have not performed well when exploring the intrinsic characteristics of highly complex and non-stationary EEG signals...

The application of intracranial electroencephalogram (iEEG) to predict seizures remains challenging. Although channel selection has been utilized in seizure prediction and detection studies, most of them focus on the combination with conventional machine learning methods. Thus, channel selection combined with deep learning methods can be further analyzed in the field of seizure prediction. Given this, in this work, a novel iEEG-based deep learning method of One-Dimensional Convolutional Neural Networks (1D-CNN) combined with channel increment strategy was proposed for the effective seizure prediction...

Anesthetic-induced loss of consciousness (LOC) has been studied using functional connectivity (FC) and functional network analysis (FNA), manifested as fragmentation of the whole-brain functional network. However, how the fragmented brain networks reversibly recover during the recovery of consciousness (ROC) remains vague. This study aims to investigate the changes in brain network structure during ROC, to better understand the network fragmentation during anesthesia, thus providing insights into consciousness monitoring...

Understanding the distinct functions of human muscles could not only help professionals obtain insights into the underlying mechanisms that we accommodate compromised neuromuscular system, but also assist engineers in developing rehabilitation devices. This study aims to determine the contribution of major muscle and the energy flow in the human musculoskeletal system at four sub-phases (collision, rebound, preload, push-off) during the stance of walking at different speeds. Gait experiments were performed with three self-selected speeds: slow, normal, and fast...