System Architecture for VR Yoga Therapy Platform with 6-DoF Whole-Body Avatar Tracking.

Chronic pain is a leading cause of morbidity among children and adolescents affecting 35% of the global population. Pediatric chronic pain management requires integrative health methods spanning physical and psychological subsystems through various mind-body interventions. Yoga therapy is one such method, known for its ability to improve the quality of life both physically and psychologically in chronic pain conditions.

Classification performance of sEMG and kinematic parameters for distinguishing between non-lame and induced lameness conditions in horses.

Despite its proven research applications, it remains unknown whether surface electromyography (sEMG) can be used clinically to discriminate non-lame from lame conditions in horses. This study compared the classification performance of sEMG absolute value (sEMGabs) and asymmetry (sEMGasym) parameters, alongside validated kinematic upper-body asymmetry parameters, for distinguishing non-lame from induced fore- (iFL) and hindlimb (iHL) lameness. Bilateral sEMG and 3D-kinematic data were collected from clinically non-lame horses ( = 8) during in-hand trot.

A Virtual Reality Exergame: Clinician-Guided Breathing and Relaxation for Children with Muscular Dystrophy.

This study introduces a VR-based breathing and relaxation exergame tailored for individuals with Duchenne muscular dystrophy (DMD). DMD is a rare neuromuscular disease that leads to respiratory muscle dysfunction with anxiety being a common comorbidity. Clinical management requires frequent visits to rare disease specialists to manage symptom progression.

Electromyographic and Kinematic Comparison of the Leading and Trailing Fore- and Hindlimbs of Horses during Canter.

This study compared muscle activity and movement between the leading (Ld) and trailing (Tr) fore- (F) and hindlimbs (H) of horses cantering overground. Three-dimensional kinematic and surface electromyography (sEMG) data were collected from right triceps brachii, biceps femoris, middle gluteal, and splenius from 10 ridden horses during straight left- and right-lead canter. Statistical parametric mapping evaluated between-limb (LdF vs.

Adaptations in equine axial movement and muscle activity occur during induced fore- and hindlimb lameness: A kinematic and electromyographic evaluation during in-hand trot.

Background: The inter-relationship between equine thoracolumbar motion and muscle activation during normal locomotion and lameness is poorly understood.

Objective: To compare thoracolumbar and pelvic kinematics and longissimus dorsi (longissimus) activity of trotting horses between baseline and induced forelimb (iFL) and hindlimb (iHL) lameness.

Study Design: Controlled experimental cross-over study.

Adaptations in equine appendicular muscle activity and movement occur during induced fore- and hindlimb lameness: An electromyographic and kinematic evaluation.

The relationship between lameness-related adaptations in equine appendicular motion and muscle activation is poorly understood and has not been studied objectively. The aim of this study was to compare muscle activity of selected fore- and hindlimb muscles, and movement of the joints they act on, between baseline and induced forelimb (iFL) and hindlimb (iHL) lameness. Three-dimensional kinematic data and surface electromyography (sEMG) data from the fore- (triceps brachii, latissimus dorsi) and hindlimbs (superficial gluteal, biceps femoris, semitendinosus) were bilaterally and synchronously collected from clinically non-lame horses ( = 8) trotting over-ground (baseline).

Ability-Based Methods for Personalized Keyboard Generation.

This study introduces an ability-based method for personalized keyboard generation, wherein an individual's own movement and human-computer interaction data are used to automatically compute a personalized virtual keyboard layout. Our approach integrates a multidirectional point-select task to characterize cursor control over time, distance, and direction. The characterization is automatically employed to develop a computationally efficient keyboard layout that prioritizes each user's movement abilities through capturing directional constraints and preferences.

Ability-based Keyboards for Augmentative and Alternative Communication: Understanding How Individuals' Movement Patterns Translate to More Efficient Keyboards: Methods to Generate Keyboards Tailored to User-specific Motor Abilities.

This study presents the evaluation of ability-based methods extended to keyboard generation for alternative communication in people with dexterity impairments due to motor disabilities. Our approach characterizes user-specific cursor control abilities from a multidirectional point-select task to configure letters on a virtual keyboard based on estimated time, distance, and direction of movement. These methods were evaluated in three individuals with motor disabilities against a generically optimized keyboard and the ubiquitous QWERTY keyboard.

Effects of Timed Frontal Plane Pelvic Moments During Overground Walking With a Mobile TPAD System.

Robotic gait training may improve overground ambulation for individuals with poor control over pelvic motion. However, there is a need for an overground gait training robotic device that allows full control of pelvic movement and synchronizes applied forces to the user's gait. This work evaluates an overground robotic gait trainer that applies synchronized forces on the user's pelvis, the mobile Tethered Pelvic Assist Device.

Surface Electromyography-Based Recognition, Synthesis, and Perception of Prosodic Subvocal Speech.

Purpose This study aimed to evaluate a novel communication system designed to translate surface electromyographic (sEMG) signals from articulatory muscles into speech using a personalized, digital voice. The system was evaluated for word recognition, prosodic classification, and listener perception of synthesized speech. Method sEMG signals were recorded from the face and neck as speakers with ( = 4) and without ( = 4) laryngectomy subvocally recited (silently mouthed) a speech corpus comprising 750 phrases (150 phrases with variable phrase-level stress).

Muscle Function and Kinematics during Submaximal Equine Jumping: What Can Objective Outcomes Tell Us about Athletic Performance Indicators?

Selection and training practices for jumping horses have not yet been validated using objective performance analyses. This study aimed to quantify the differences and relationships between movement and muscle activation strategies in horses with varying jump technique to identify objective jumping performance indicators. Surface electromyography (sEMG) and three-dimensional kinematic data were collected from horses executing a submaximal jump.

Motor unit drive: a neural interface for real-time upper limb prosthetic control.

Objective: Modern prosthetic limbs have made strident gains in recent years, incorporating terminal electromechanical devices that are capable of mimicking the human hand. However, access to these advanced control capabilities has been prevented by fundamental limitations of amplitude-based myoelectric neural interfaces, which have remained virtually unchanged for over four decades. Consequently, nearly 23% of adults and 32% of children with major traumatic or congenital upper-limb loss abandon regular use of their myoelectric prosthesis.

Wearable Movement Sensors for Rehabilitation: A Focused Review of Technological and Clinical Advances.

Recent technologic advancements have enabled the creation of portable, low-cost, and unobtrusive sensors with tremendous potential to alter the clinical practice of rehabilitation. The application of wearable sensors to track movement has emerged as a promising paradigm to enhance the care provided to patients with neurologic or musculoskeletal conditions. These sensors enable quantification of motor behavior across disparate patient populations and emerging research shows their potential for identifying motor biomarkers, differentiating between restitution and compensation motor recovery mechanisms, remote monitoring, telerehabilitation, and robotics.

Development of sEMG sensors and algorithms for silent speech recognition.

Objective: Speech is among the most natural forms of human communication, thereby offering an attractive modality for human-machine interaction through automatic speech recognition (ASR). However, the limitations of ASR-including degradation in the presence of ambient noise, limited privacy and poor accessibility for those with significant speech disorders-have motivated the need for alternative non-acoustic modalities of subvocal or silent speech recognition (SSR).

Approach: We have developed a new system of face- and neck-worn sensors and signal processing algorithms that are capable of recognizing silently mouthed words and phrases entirely from the surface electromyographic (sEMG) signals recorded from muscles of the face and neck that are involved in the production of speech.

Silent Speech Recognition as an Alternative Communication Device for Persons with Laryngectomy.

Each year thousands of individuals require surgical removal of their larynx (voice box) due to trauma or disease, and thereby require an alternative voice source or assistive device to verbally communicate. Although natural voice is lost after laryngectomy, most muscles controlling speech articulation remain intact. Surface electromyographic (sEMG) activity of speech musculature can be recorded from the neck and face, and used for automatic speech recognition to provide speech-to-text or synthesized speech as an alternative means of communication.

Impaired perception of biological motion in Parkinson's disease.

Objective: We examined biological motion perception in Parkinson's disease (PD). Biological motion perception is related to one's own motor function and depends on the integrity of brain areas affected in PD, including posterior superior temporal sulcus. If deficits in biological motion perception exist, they may be specific to perceiving natural/fast walking patterns that individuals with PD can no longer perform, and may correlate with disease-related motor dysfunction.

Randomized Controlled Trial of a Home-Based Action Observation Intervention to Improve Walking in Parkinson Disease.

Objective: To examine the feasibility and efficacy of a home-based gait observation intervention for improving walking in Parkinson disease (PD).

Design: Participants were randomly assigned to an intervention or control condition. A baseline walking assessment, a training period at home, and a posttraining assessment were conducted.

Veering in hemi-Parkinson's disease: Primacy of visual over motor contributions.

Veering while walking is often reported in individuals with Parkinson's disease (PD), with potential mechanisms being vision-based (asymmetrical perception of the visual environment) or motoric (asymmetry in stride length between relatively affected and non-affected body side). We examined these competing hypotheses by assessing veering in 13 normal control participants (NC) and 20 non-demented individuals with PD: 9 with left-side onset of motor symptoms (LPD) and 11 with right-side onset (RPD). Participants walked in a corridor under three conditions: eyes-open, egocentric reference point (ECRP; walk toward a subjectively perceived center of a target at the end of the corridor), and vision-occluded.

Decomposition of surface EMG signals from cyclic dynamic contractions.

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait.

Dynamical learning and tracking of tremor and dyskinesia from wearable sensors.

We have developed and evaluated several dynamical machine-learning algorithms that were designed to track the presence and severity of tremor and dyskinesia with 1-s resolution by analyzing signals collected from Parkinson's disease (PD) patients wearing small numbers of hybrid sensors with both 3-D accelerometeric and surface-electromyographic modalities. We tested the algorithms on a 44-h signal database built from hybrid sensors worn by eight PD patients and four healthy subjects who carried out unscripted and unconstrained activities of daily living in an apartment-like environment. Comparison of the performance of our machine-learning algorithms against independent clinical annotations of disorder presence and severity demonstrates that, despite their differing approaches to dynamic pattern classification, dynamic neural networks, dynamic support vector machines, and hidden Markov models were equally effective in keeping error rates of the dynamic tracking well below 10%.

High-resolution tracking of motor disorders in Parkinson's disease during unconstrained activity.

Parkinson's disease (PD) can present with a variety of motor disorders that fluctuate throughout the day, making assessment a challenging task. Paper-based measurement tools can be burdensome to the patient and clinician and lack the temporal resolution needed to accurately and objectively track changes in motor symptom severity throughout the day. Wearable sensor-based systems that continuously monitor PD motor disorders may help to solve this problem, although critical shortcomings persist in identifying multiple disorders at high temporal resolution during unconstrained activity.

Preferred sensor sites for surface EMG signal decomposition.

Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646-57 and Nawab et al 2010 Clin.

Detecting freezing-of-gait during unscripted and unconstrained activity.

We present a dynamic neural network (DNN) solution for detecting instances of freezing-of-gait (FoG) in Parkinson's disease (PD) patients while they perform unconstrained and unscripted activities. The input features to the DNN are derived from the outputs of three triaxial accelerometer (ACC) sensors and one surface electromyographic (EMG) sensor worn by the PD patient. The ACC sensors are placed on the shin and thigh of one leg and on one of the forearms while the EMG sensor is placed on the shin.

Resolving signal complexities for ambulatory monitoring of motor function in Parkinson's disease.

Automatic tracking of movement disorders in patients with Parkinson's disease (PD) is dependent on the ability of machine learning algorithms to resolve the complex and unpredictable characteristics of wearable sensor data. The challenge reflects the variety of movement disorders that fluctuate throughout the day which can be confounded by voluntary activities of daily life. Our approach is the development of multiple dynamic neural network (DNN) classifiers whose application are governed by a rule-based controller within the Integrated Processing and Understanding of Signals (IPUS) framework.