Enhancing Tai Chi Training System: Towards Group-Based and Hyper-Realistic Training Experiences.

In this article, we propose a lightweight and flexible enhanced Tai Chi training system composed of multiple standalone virtual reality (VR) devices. The system aims to enable a hyper-realistic multi-user action training platform at low cost by displaying real-time action guidance trajectories, providing real-world impossible visual effects and functions, and rapidly enhancing movement precision and communication interest for learners. We objectively evaluate participants' action quality at different levels of immersion, including traditional coach guidance (TCG), VR, and mixed reality (MR), along with subjective measures like motion sickness, quality of interaction, social meaning, presence/immersion to comprehensively explore the system's feasibility. The results indicate VR performs the best in training accuracy, but MR provides superior social experience and relatively high accuracy. Unlike TCG, MR offers hyper-realistic hand movement trajectories and Tai Chi social references. Compared with VR, MR provides more realistic avatar companions and a safer environment. In summary, MR balances accuracy and social experience.

Self-Guided DMT: Exploring a Novel Paradigm of Dance Movement Therapy in Mixed Reality for Children with ASD.

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

A qualitative study exploring the ritual-like activity and therapeutic relationship between Pilates teachers and clients with persistent low back pain.

BACKGROUND AND PURPOSE: Pilates is a commonly recommended exercise modality for the management of persistent low back pain. Whilst guidelines recommend the use of exercise for low back pain, research suggests that no one exercise is superior, creating a question over the mechanism of effect. The patient-practitioner relationship may be important in managing low back pain; however, the relationship between Pilates teachers and clients is not well understood. The purpose of this study was to identify the components of the relationship between Pilates teachers and clients with persistent low back pain, explore key influences on the relationship, and ascertain the nature of the relationship. METHOD: We conducted a qualitative, ethnographically-informed study at eight sites in the South of England, observing 24 Pilates sessions and interviewing 9 Pilates teachers and 10 clients with persistent low back pain. Fieldnotes and interview transcripts were analysed thematically. RESULTS: The findings demonstrate a complex, multi-faceted interaction that occurs during Pilates sessions, grounded within certain health perceptions, and predicated on expectations of individuality, choice and expertise. A key finding reveals the perceived importance of mastery of prescribed movements with control and precision, in which clients particularly value the authority of the teacher in a directive learning environment. CONCLUSION: We contend that the role of the Pilates teacher in this study facilitated the alleviation of clients' distress through the application of ritual-like Pilates activity. We conclude that the relationship between Pilates teachers and clients with persistent low back pain may be considered a therapeutic relationship.

Joint knee loads during squat with constant or variable resistance in males. A clinical trial.

BACKGROUND: In the squat movement, the use of constant resistance (CR) generates greater compression and shear forces close to 90° of knee flexion, increasing joint overload. However, when used variable resistance (VR) there is no consensus about the effect of knee joint load. The aim of this study was to compare knee torques using constant or variable resistance during the squat exercise. METHODS: Twenty-one healthy male subjects (mean age, 24 [SD, 3] years; height, 1.76 [SD, 0.04] m), who practice squats during strength training routine. Were simultaneously record data from the platform force and tridimensional kinematic to obtain torques around knee. 15 repetitions were performed up to maximum knee flexion with the use of variable (RV) or constant (CR) resistance in a single session. RESULTS: Significant differences regarding the angles only in the sagittal plane at the end of the ascending phase of the squat, with less knee extension in the VR condition. In the sagittal and frontal planes, lower values of extensor and abductor torque were found in the VR condition at the angles of greater knee flexion. CONCLUSION: The use of variable resistance compared to constant resistance seems to be an alternative to be considered when the objective is to minimize the internal loads on the knee joint in exercises such as the squat in amplitudes of greater flexion. This study indicated that VR helps subjects who are learning the squat movement, enabling the application of this knowledge in physical therapy or physical training clinics.

Development and pilot of equine facilitated physical therapy outcome measure tool for chronic low back pain patients.

Equine Facilitated Physical Therapy (EFPT) lacks consistent documentation due to being an unconventional physical therapy treatment to chronic low back pain patients (LBP) and lacking rehabilitation outcome measure tools for a stable (equestrian) environment. The objectives were to develop an online evaluation tool as well as to define inter- and intra-rater reliability to validate the outcome measurement tool "Evaluation of maintaining sitting position (on a horse) and walking (short distances)" designed for LBP patients in EFPT". A total of 48 movement related functions (n = 48), were derived from the International Classification of Functioning (ICF) and organized to an online evaluation tool. Depending on the state of validation two to six (2-6) raters scored randomized patient (n = 22) video material, recorded during a 12-week EFPT intervention, with the designed tool. Inter-rater agreement level between the experts reached good (α = 87) reliability for the scoring of the items and calculated per patient excellent (α = 100). Intra-rater reliability reached good (α = 87) and per patient good (α = 80) repeatability. For the healthy adults the reliability between raters reached acceptable (α = 72) levels and per rated excellent (α = 100). The developed assessment tool was found satisfactory to fulfil the requirement for the therapeutic practice. With the use of the tool physical therapist may detect postural changes for LBP patients as outcome report in EFPT. The tool may be used to identify treatment progress and to help design home exercises. The created tool will help to collect similar outcome measures from LBP patients in EFPT and to validate the treatment within industry.

Action observation with motor simulation improves reactive stepping responses following strong backward balance perturbations in healthy young individuals.

BACKGROUND AND OBJECTIVE: Adequate reactive steps are critical for preventing falls following balance perturbations. Perturbation-based balance training was shown to improve reactive stepping in various clinical populations, but its delivery is labor-intensive and generally uses expensive equipment. Action observation of reactive steps with either motor imagery (AOMI) or motor simulation (AOMS) are potential alternative training modalities. We here aimed to study their effects on reactive stepping performance. METHODS: Sixty healthy young subjects were subjected to forward platform translations that elicited backward reactive steps. The AOMI group (n = 20) was tested after AOMI of an actor's reactive steps, while the AOMS group (n = 20) additionally stepped along with the actor. The control group (n = 20) was tested without any prior observation. Our primary outcome was the step quality of the first trial response, as this best represents a real-life loss-of-balance. Step quality was quantified as the leg angle with respect to the vertical at stepping-foot contact. We also studied single step success rates and reactive step quality across repeated trials. RESULTS: Reactive step quality was significantly better in the AOMI and AOMS groups than in the control group, which differences coincided with a twofold higher single step success rate. Reactive step quality improved upon repeated trials in all groups, yet the AOMS group needed the fewest repetitions to reach plateau performance. SIGNIFICANCE: The present results demonstrate that both AOMI and AOMS improved first and repeated trial reactive stepping performance. These findings point at the potential applicability of these concepts for home-based reactive balance training, for instance in serious games, with overt movements (AOMS) possibly having some benefits over mental imaginations (AOMI). Whether similar beneficial effects also emerge in the target populations of balance-impaired individuals remains to be investigated.

Brain-wide neural activity underlying memory-guided movement.

Behavior relies on activity in structured neural circuits that are distributed across the brain, but most experiments probe neurons in a single area at a time. Using multiple Neuropixels probes, we recorded from multi-regional loops connected to the anterior lateral motor cortex (ALM), a circuit node mediating memory-guided directional licking. Neurons encoding sensory stimuli, choices, and actions were distributed across the brain. However, choice coding was concentrated in the ALM and subcortical areas receiving input from the ALM in an ALM-dependent manner. Diverse orofacial movements were encoded in the hindbrain; midbrain; and, to a lesser extent, forebrain. Choice signals were first detected in the ALM and the midbrain, followed by the thalamus and other brain areas. At movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

How Integration of a Brain-Machine Interface and Obstacle Detection System Can Improve Wheelchair Control via Movement Imagery.

This study presents a human-computer interaction combined with a brain-machine interface (BMI) and obstacle detection system for remote control of a wheeled robot through movement imagery, providing a potential solution for individuals facing challenges with conventional vehicle operation. The primary focus of this work is the classification of surface EEG signals related to mental activity when envisioning movement and deep relaxation states. Additionally, this work presents a system for obstacle detection based on image processing. The implemented system constitutes a complementary part of the interface. The main contributions of this work include the proposal of a modified 10-20-electrode setup suitable for motor imagery classification, the design of two convolutional neural network (CNNs) models employed to classify signals acquired from sixteen EEG channels, and the implementation of an obstacle detection system based on computer vision integrated with a brain-machine interface. The models developed in this study achieved an accuracy of 83% in classifying EEG signals. The resulting classification outcomes were subsequently utilized to control the movement of a mobile robot. Experimental trials conducted on a designated test track demonstrated real-time control of the robot. The findings indicate the feasibility of integration of the obstacle detection system for collision avoidance with the classification of motor imagery for the purpose of brain-machine interface control of vehicles. The elaborated solution could help paralyzed patients to safely control a wheelchair through EEG and effectively prevent unintended vehicle movements.

Effects of Manual Therapy on Parkinson's Gait: A Systematic Review.

Manual therapy (MT) is commonly used in rehabilitation to deal with motor impairments in Parkinson's disease (PD). However, is MT an efficient method to improve gait in PD? To answer the question, a systematic review of clinical controlled trials was conducted. Estimates of effect sizes (reported as standard mean difference (SMD)) with their respective 95% confidence interval (95% CI) were reported for each outcome when sufficient data were available. If data were lacking, p values were reported. The PEDro scale was used for the quality assessment. Three studies were included in the review. MT improved Dynamic Gait Index (SMD = 1.47; 95% CI: 0.62, 2.32; PEDro score: 5/10, moderate level of evidence). MT also improved gait performances in terms of stride length, velocity of arm movements, linear velocities of the shoulder and the hip (p < 0.05; PEDro score: 2/10, limited level of evidence). There was no significant difference between groups after MT for any joint's range of motion during gait (p > 0.05; PEDro score: 6/10, moderate level of evidence). There is no strong level of evidence supporting the beneficial effect of MT to improve gait in PD. Further randomized controlled trials are needed to understand the impact of MT on gait in PD.

Beta bursts question the ruling power for brain-computer interfaces.

Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.

Towards therapeutic electrophysiological neurofeedback in Parkinson's disease.

Neurofeedback (NF) techniques support individuals to self-regulate specific features of brain activity, which has been shown to impact behavior and potentially ameliorate clinical symptoms. Electrophysiological NF (epNF) may be particularly impactful for patients with Parkinson's disease (PD), as evidence mounts to suggest a central role of pathological neural oscillations underlying symptoms in PD. Exaggerated beta oscillations (12-30 Hz) in the basal ganglia-cortical network are linked to motor symptoms (e.g., bradykinesia, rigidity), and beta is reduced by successful therapy with dopaminergic medication and Deep Brain Stimulation (DBS). PD patients also experience non-motor symptoms related to sleep, mood, motivation, and cognitive control. Although less is known about the mechanisms of non-motor symptoms in PD and how to successfully treat them, low frequency neural oscillations (1-12 Hz) in the basal ganglia-cortical network are particularly implicated in non-motor symptoms. Here, we review how cortical and subcortical epNF could be used to target motor and non-motor specific oscillations, and potentially serve as an adjunct therapy that enables PD patients to endogenously control their own pathological neural activities. Recent studies have demonstrated that epNF protocols can successfully support volitional control of cortical and subcortical beta rhythms. Importantly, this endogenous control of beta has been linked to changes in motor behavior. epNF for PD, as a casual intervention on neural signals, has the potential to increase understanding of the neurophysiology of movement, mood, and cognition and to identify new therapeutic approaches for motor and non-motor symptoms.

Differences in motor imagery abilities in active and sedentary individuals: new insights from backward-walking imagination.

Evidence has shown that imagining a complex action, like backward-walking, helps improve the execution of the gesture. Despite this, studies in sport psychology have provided heterogeneous results on the use of motor imagery (MI) to improve performance. We aimed to fill this gap by analyzing how sport experience influences backward-walking MI processes in a sample of young women (n = 41, mean age = 21 ± 2.2) divided into Active and Sedentary. All participants were allocated to two randomized mental chronometric tasks, in which they had first to imagine and then execute forward-walking (FW) and backward-walking (BW). The Isochrony Efficiency measured the difference between imagination and execution times in both conditions (FW and BW). Moreover, we analyzed the ability to vividly imagine FW and BW within various perspectives in both groups through the Vividness of Movement Imagery Questionnaire (VMIQ-2). Findings showed that active individuals performed better in the BW imagery task when compared to sedentary ones (F1,39 = 4.98; p = 0.03*), while there were no differences between groups in the FW imagery task (F1,39 = .10; p = 0.75). Further, VMIQ-2 had evidenced that the ability to imagine backward is influenced by perspective used. Specifically, the use of internal visual imagery (IVI) led to worse Isochrony Efficiency (t32,25 = 2.16; p = 0.04*), while the use of kinesthetic imagery (KIN) led to better Isochrony Efficiency (t32,25 = - 2.34; p = 0.03*). These results suggest a close relation between motor experience and complex motor imagery processes and open new insights for studying these mental processes.

A design and implementation of multi-character classification scheme based on motor imagery EEG signals.

In the field of brain-to-text communication, it is difficult to finish highly dexterous behaviors of writing multi-character by motor-imagery-based brain-computer interface (MI-BCI), setting a barrier to restore communication in people who have lost the ability to move and speak. In this paper, we design and implement a multi-character classification scheme based on 29 characters of motor imagery (MI) electroencephalogram (EEG) signals, which contains 26 English letters and 3 punctuation marks. Firstly, we design a novel experimental paradigm to increase the variety of BCI inputs by asking subjects to imagine the movement of writing 29 characters instead of gross motor skills such as reaching or grasping. Secondly, because of the high dimension of EEG signals, we adopt power spectral density (PSD), principal components analysis (PCA), kernel principal components analysis (KPCA) respectively to decompose EEG signals and extract feature, and then test the results with pearson product-moment correlation coefficient (PCCs). Thirdly, we respectively employ k-nearest neighbor (kNN), support vector machine (SVM), extreme learning machine (ELM) and light gradient boosting machine (LightGBM) to classify 29 characters and compare the results. We have implemented a complete scheme, including paradigm design, signal acquisition, feature extraction and classification, which can effectively classify 29 characters. The experimental results show that the KPCA has the best feature extraction effect and the kNN has the highest classification accuracy, with the final classification accuracy reaching 96.2%, which is better than other studies.

Inducing visual attention through audiovisual stimuli: Can synchronous sound be a salient event?

We present an experimental research aiming to explore how spatial attention may be biased through auditory stimuli. In particular, we investigate how synchronous sound and image may affect attention and increase the saliency of the audiovisual event. We have designed and implemented an experimental study where subjects, wearing an eye-tracking system, were examined regarding their gaze toward the audiovisual stimuli being displayed. The audiovisual stimuli were specifically tailored for this experiment, consisting of videos contrasting in terms of Synch Points (i.e., moments where a visual event is associated with a visible trigger movement, synchronous with its correspondent sound). While consistency across audiovisual sensory modalities revealed to be an attention-drawing feature, when combined with synchrony, it clearly emphasized the biasing, triggering orienting, that is, focal attention towards the particular scene that contains the Synch Point. Consequently, results revealed synchrony to be a saliency factor, contributing to the strengthening of the focal attention.

Motor cortex activation during visuomotor transformations: evoked potentials during overt and imagined movements.

Despite the prevalence of visuomotor transformations in our motor skills, their mechanisms remain incompletely understood, especially when imagery actions are considered such as mentally picking up a cup or pressing a button. Here, we used a stimulus-response task to directly compare the visuomotor transformation underlying overt and imagined button presses. Electroencephalographic activity was recorded while participants responded to highlights of the target button while ignoring the second, non-target button. Movement-related potentials (MRPs) and event-related desynchronization occurred for both overt movements and motor imagery (MI), with responses present even for non-target stimuli. Consistent with the activity accumulation model where visual stimuli are evaluated and transformed into the eventual motor response, the timing of MRPs matched the response time on individual trials. Activity-accumulation patterns were observed for MI, as well. Yet, unlike overt movements, MI-related MRPs were not lateralized, which appears to be a neural marker for the distinction between generating a mental image and transforming it into an overt action. Top-down response strategies governing this hemispheric specificity should be accounted for in future research on MI, including basic studies and medical practice.

Signature methods for brain-computer interfaces.

Brain-computer interfaces (BCIs) allow direct communication between one's central nervous system and a computer without any muscle movement hence by-passing the peripheral nervous system. They can restore disabled people's ability to interact with their environment, e.g. communication and wheelchair control. However, to this day their performance is still hindered by the non-stationarity of electroencephalography (EEG) signals, as well as their susceptibility to noise from the users' environment and from their own physiological activity. Moreover, a non-negligible amount of users struggle to use BCI systems based on motor imagery. In this paper, a new method based on the path signature is introduced to tackle this problem by using features which are different from the usual power-based ones. The path signature is a series of iterated integrals computed from a multidimensional path. It is invariant under translation and time reparametrization, which makes it a robust feature for multichannel EEG time series. The performance can be further boosted by combining the path signature with the gold standard Riemannian classifier in the BCI field exploiting the geometric structure of symmetric positive definite (SPD) matrices. The results obtained on publicly available datasets show that the signature method is more robust to inter-user variability than classical ones, especially on noisy and low-quality data. Hence, this study paves the way towards the use of mathematical tools that until now have been neglected, in order to tackle the EEG-based BCI variability issue. It also sheds light on the lead-lag relationship captured by path signature which seems relevant to assess the underlying neural mechanisms.

The effect of 4-weeks exposure to music on social bonding between rats.

Interpersonal synchronization of movement induced by music is believed to facilitate social bonding between human beings, but it is unknown whether it also works in animals. We allowed rats to interact and develop social bonding with a specific subject for four weeks under one of the three acoustic conditions: playback of K.448 at its original tempo, playback at its double-tempo, and silence. The strength of social bonding between each pair of rats was then measured. The results showed an increase in preference for rats that had interacted under the original tempo playback compared to the other acoustic conditions. Considering that rats move in synchrony with the beat more robustly and consistently between subjects under the original tempo playback than under the double-tempo playback, this result suggests that motor synchronization between subjects through music may facilitate social bonding between rats.

Comparing the seoi-nage skill of elite and non-elite judo athletes.

Seoi-nage performance requires a high level of skill and proficiency. The aim of this study was to compare the motor planning, regulation, and control skills of elite versus non-elite seoi-nage judo athletes. Twenty subjects (10 elites and 10 non-elite) performed the three-phase seoi-nage skills of unbalancing, positioning, and throwing while an optical motion capture 3D camera monitored their shoulder, pelvis, hip, and knee joint movements to calculate their force magnitude and direction. Elite athletes performed better than non-elite athletes in terms of the shoulder (247.4° vs. 208.3° in Event 4) and pelvic (235.4° vs. 194.4° in Event 4) rotation, tilt angle (15.13° vs. - 0.74° in Event 4) characteristics, as well as hip (136.1° vs. 125.0° in Event 4) and knee joint (124.0° vs. 120.8° in Event 3) flexion-extension angle. Compared to non-elite athletes, elite athletes also showed more controlled force and movement in all bodily areas. These results can help to guide the development of seoi-nage skills as judo athletes advance from the non-elite to the elite level.

Assessment of a karate performer's position estimation system without any markers.

Karate has become more popular, and researches have been conducted on training methods and motion analysis related to karate. The AR system for karate is an effective system for training and for the audience to understand the performance. To develop the AR system, it is necessary to acquire the movement of the performer. The purpose of the research is to assess a motion capture system to estimate a karate performer's full-body position and posture from HoloLens 2, without wearing any attachments. We used ThreeDPoseUnityBarracuda to estimate the position and posture of the performer's joints. In the experiment, the estimated joint's positions were compared with actual positions using the inertial three-dimensional motion capture. As results, although the error increased when the performer's movement was large, the median error between the estimated and actual positions was a maximum of 0.24 m at the x-coordinate of the right hand, and high accuracy was obtained when the performer's movement was small.

Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance.

The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input consists of spatial covariance matrices derived from EEGs. In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models. The quality of generated samples is evaluated through visual and quantitative assessments using a left/right-hand-movement motor imagery dataset. The exceptional pixel-level resolution of these generative samples highlights the formidable capacity of score-based generative modeling. Additionally, the center (Fréchet mean) of the generated samples aligns with neurophysiological evidence that event-related desynchronization and synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency bands during motor imagery processing. The quantitative evaluation revealed that 84.3% of the generated samples could be accurately predicted by a pre-trained classifier and an improvement of up to 8.7% in the average accuracy over ten runs for a specific test subject in a holdout experiment.