Motion capture sensing techniques used in human upper limb motion: a review

Purpose Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion. Design/methodology/approach This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion. Findings The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems. Originality/value This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges

An assistive tabletop keyboard for stroke rehabilitation

We propose a tabletop keyboard that assists stroke patients in using computers. Using computers for purposes such as paying bills, managing bank accounts, sending emails, etc., which all include typing, is part of Activities of Daily Living (ADL) that stroke patients wish to recover. To date, stroke rehabilitation research has greatly focused on using computer-assisted technology for rehabilitation. However, working with computers as a skill that patients need to recover has been neglected. The conventional human computer interfaces are mouse and keyboard. Using keyboard stays the main challenge for hemiplegic stroke patients because typing is usually a bimanual task. Therefore, we propose an assistive tabletop keyboard which is not only a novel computer interface that is specially designed to facilitate patient-computer interaction but also a rehab medium through which patients practice the desired arm/hand functions. © 2013 Authors

Comparing direct and indirect interaction in stroke rehabilitation

We explore the differences of direct (DI) vs. indirect (IDI) interaction in stroke rehabilitation. Direct interaction is when the patients move their arms in reaction to changes in the augmented physical environment; indirect interaction is when the patients move their arms in reaction to changes on a computer screen. We developed a rehabilitation game in both settings evaluated by a within-subject study with 10 patients with chronic stroke, aiming to answer 2 major questions: (i) do the game scores in either of the two interaction modes correlate with clinical assessment scores? and (ii) whether performance is different using direct versus indirect interaction in patients with stroke. Our experimental results confirm higher performance in use of DI over IDI. They also suggest better correlation of DI and clinical scores. Our study provides evidence for the benefits of direct interaction therapies vs. indirect computer-assisted therapies in stroke rehabilitation

Comparing "pick and place" task in spatial Augmented Reality versus non-immersive Virtual Reality for rehabilitation setting.

Introducing computer games to the rehabilitation market led to development of numerous Virtual Reality (VR) training applications. Although VR has provided tremendous benefit to the patients and caregivers, it has inherent limitations, some of which might be solved by replacing it with Augmented Reality (AR). The task of pick-and-place, which is part of many activities of daily living (ADL's), is one of the major affected functions stroke patients mainly expect to recover. We developed an exercise consisting of moving an object between various points, following a flash light that indicates the next target. The results show superior performance of subjects in spatial AR versus non-immersive VR setting. This could be due to the extraneous hand-eye coordination which exists in VR whereas it is eliminated in spatial AR

Ego-perspective enhanced fitness training experience of AR Try to Move game

AR, a recent emerging technology, has been widely used in entertainment to provide users with immersive, interactive, and, sometimes, engaging experiences. The process of rehabilitation treatment and motor training process is often boring, and it is well known that users' exercise efficiency is often not as efficient as in a rehabilitation institution. Thus far, there is no effective upper limb sports rehabilitation training game based on the ego-perspective. Hence, with the objective of enhancing the enjoyment experience in rehabilitation and more effective remote rehabilitation training, this work aims to provide an AR Try to Move game and a convolutional neural network (CNN) for identifying and classifying user gestures from a self-collected AR multiple interactive gestures dataset. Utilizing an AR game scoring system, users are incentivized to enhance their upper limb muscle system through remote training with greater effectiveness and convenience.Comment: 6 pages, 2 figures, 2 tables, 2023 International Conference on Machine Learning and Automation (CONF-MLA 2023

Statistical validation for clinical measures: Repeatability and agreement of Kinect based software

Background. The rehabilitation process is a fundamental stage for recovery of people's capabilities. However, the evaluation of the process is performed by physiatrists and medical doctors, mostly based on their observations, that is, a subjective appreciation of the patient's evolution. This paper proposes a tracking platform of the movement made by an individual's upper limb using Kinect sensor(s) to be applied for the patient during the rehabilitation process. The main contribution is the development of quantifying software and the statistical validation of its performance, repeatability, and clinical use in the rehabilitation process. Methods. The software determines joint angles and upper limb trajectories for the construction of a specific rehabilitation protocol and quantifies the treatment evolution. In turn, the information is presented via a graphical interface that allows the recording, storage, and report of the patient's data. For clinical purposes, the software information is statistically validated with three different methodologies, comparing the measures with a goniometer in terms of agreement and repeatability. Results. The agreement of joint angles measured with the proposed software and goniometer is evaluated with Bland-Altman plots; all measurements fell well within the limits of agreement, meaning interchangeability of both techniques. Additionally, the results of Bland-Altman analysis of repeatability show 95% confidence. Finally, the physiotherapists' qualitative assessment shows encouraging results for the clinical use. Conclusion. The main conclusion is that the software is capable of offering a clinical history of the patient and is useful for quantification of the rehabilitation success. The simplicity, low cost, and visualization possibilities enhance the use of the software Kinect for rehabilitation and other applications, and the expert's opinion endorses the choice of our approach for clinical practice. Comparison of the new measurement technique with established goniometric methods determines that the proposed software agrees sufficiently to be used interchangeably.Fil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Pérez Berenguer, María Elisa. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Tello, Emanuel Bienvenido. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Rodrigo, Alejandro. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Valentinuzzi, Maximo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet Noa Sur. Instituto Superior de Investigaciones Biológicas. Grupo de Investigación y Desarrollo del Noroeste Argentino | Universidad Nacional de Tucumán. Instituto Superior de Investigaciones Biológicas. Grupo de Investigación y Desarrollo del Noroeste Argentino; Argentin

Head mounted display effect on vestibular rehabilitation exercises performance

OBJECTIVES: Vestibular rehabilitation clinical guidelines document the additional benefit offered by the Mixed Reality environments in the reduction of symptoms and the improvement of balance in peripheral vestibular hypofunction. The HOLOBalance platform offers vestibular rehabilitation exercises, in an Augmented Reality (AR) environment, projecting them using a low- cost Head Mounted Display. The effect of the AR equipment on the performance in three of the commonest vestibular rehabilitation exercises is investigated in this pilot study. METHODS: Twenty-five healthy adults (12/25 women) participated, executing the predetermined exercises with or without the use of the AR equipment. RESULTS: Statistically significant difference was obtained only in the frequency of head movements in the yaw plane during the execution of a vestibular adaptation exercise by healthy adults (0.97 Hz; 95% CI=(0.56, 1.39), p<0.001). In terms of difficulty in exercise execution, the use of the equipment led to statistically significant differences at the vestibular-oculomotor adaptation exercise in the pitch plane (OR=3.64, 95% CI (-0.22, 7.50), p=0.049), and in the standing exercise (OR=28.28. 95% CI (23.6, 32.96), p=0.0001). CONCLUSION: Τhe use of AR equipment in vestibular rehabilitation protocols should be adapted to the clinicians' needs

Predicting Gains With Visuospatial Training After Stroke Using an EEG Measure of Frontoparietal Circuit Function

The heterogeneity of stroke prompts the need for predictors of individual treatment response to rehabilitation therapies. We previously studied healthy subjects with EEG and identified a frontoparietal circuit in which activity predicted training-related gains in visuomotor tracking. Here we asked whether activity in this same frontoparietal circuit also predicts training-related gains in visuomotor tracking in patients with chronic hemiparetic stroke. Subjects (n = 12) underwent dense-array EEG recording at rest, then received 8 sessions of visuomotor tracking training delivered via home-based telehealth methods. Subjects showed significant training-related gains in the primary behavioral endpoint, Success Rate score on a standardized test of visuomotor tracking, increasing an average of 24.2 ± 21.9% (p = 0.003). Activity in the circuit of interest, measured as coherence (20–30Hz) between leads overlying ipsilesional frontal (motor cortex) and parietal lobe, significantly predicted training-related gains in visuomotor tracking change, measured as change in Success Rate score (r = 0.61, p = 0.037), supporting the main study hypothesis. Results were specific to the hypothesized ipsilesional motor-parietal circuit, as coherence within other circuits did not predict training-related gains. Analyses were repeated after removing the four subjects with injury to motor or parietal areas; this increased the strength of the association between activity in the circuit of interest and training-related gains. The current study found that (1) Eight sessions of training can significantly improve performance on a visuomotor task in patients with chronic stroke, (2) this improvement can be realized using home-based telehealth methods, (3) an EEG-based measure of frontoparietal circuit function predicts training-related behavioral gains arising from that circuit, as hypothesized and with specificity, and (4) incorporating measures of both neural function and neural injury improves prediction of stroke rehabilitation therapy effects