Dual-Motor-Task of Catching and Throwing a Ball During Overground Walking in Virtual Reality

A diagnostic room for lower limb amputee based on virtual reality and an intelligent space

This article proposes a virtual reality (VR) system for diagnosing and rehabilitating lower limb amputees. A virtual environment and an intelligent space are the basis of the proposed solution. The target audiences are physiotherapists and doctors, and the aim is to provide a VR-based system to allow visualization and analysis of gait parameters and conformity. The multi-camera system from the intelligent space acquires images from patients during gait. This way, it is possible to generate tridimensional information for the VR-based system. Among the provided functionalities, the user can explore the virtual environment and manage several features, such as gait reproduction and parameters displayed, using a head-mounted display and hand controllers. Besides, the system presents an automatic classifier that can assist physiotherapists and doctors in assessing abnormalities from conventional human gait. We evaluate the system through two quantitative experiments. The first one addresses the performance evaluation of the automatic classifier. The second analysis is through a Likert scale questionnaire submitted to a group of physiotherapists. In this case, the specialists evaluate the existing features of the proposed framework. The results from the questionnaire showed that the virtual environment is suitable for helping track patients' rehabilitation. Also, the neural network-based classifier results are promising, averaging higher than 91% for all evaluation metrics. Finally, a comparison with related works in the literature highlights the contributions of the proposed solution to the field.

Motor adaptation to cognitive challenges and walking perturbations in healthy young adults

BACKGROUND

Cognitive-walking interference is manifested when simultaneously performing a cognitive task while walking. However, majority of the dual-task walking paradigms incorporated relatively short testing trials and were focused on posing a cognitive challenge by adding a secondary cognitive task but not introducing walking perturbations.

RESEARCH QUESTION

How do healthy young adults adapt to concurrent cognitive challenges and walking perturbations in terms of task prioritization and adaptation strategies to control walking stability?

METHODS

Eighteen healthy young participants walked with and without (1) continuous treadmill platform sways (Perturbed and Unperturbed walking), and (2) performing one of the cognitive tasks: visual and auditory Stroop tasks, Clock task, Paced Auditory Serial Addition Test (PASAT), and walk only. Primary outcome measures included cognitive task performance, mediolateral dynamic margins of stability (MOS_ML), M-L local dynamic stability, stride time variability and the dual-task interference (DTI) on these measures.

RESULTS

Gait adjustments made during Perturbed walking did not improve walking stability but instead, showing more local instability and greater gait variability (all p < 0.001) than Unperturbed walking. Participants increased average MOS_ML during Clock and PASAT compared to Walk Only for both Perturbed and Unperturbed walking (THSD, p < 0.05). Participants had significantly less DTI on stride time variability during Unperturbed walking than during Perturbed walking (p < 0.001). Participants also had significantly greater DTI on PASAT performance during Perturbed than during Unperturbed walking (THSD, p < 0.05) SIGNIFICANCE: Participants prioritized the walking task under a more challenging walking condition although the adjustments made during Perturbed walking were not sufficient to maintain a similar level of walking stability as Unperturbed walking. Adjustments to the cognitive-walking challenges were differed by the type of cognitive tasks. The current findings suggest that cognitive tasks involving both working memory and information processing or visuospatial recognition or attention have greater impact on gait especially during the perturbed walking condition.

Influence of body visualization in VR during the execution of motoric tasks in different age groups

Virtual reality (VR) has become a common tool and is often considered for sport-specific purposes. Despite the increased usage, the transfer of VR-adapted skills into the real-world (RW) has not yet been sufficiently studied, and it is still unknown how much of the own body must be visible to complete motoric tasks within VR. In addition, it should be clarified whether older adults also need to perceive their body within VR scenarios to the same extent as younger people extending the usability. Therefore, younger (18-30 years old) and elderly adults (55 years and older) were tested (n = 42) performing a balance-, grasping- and throwing task in VR (HMD based) accompanied with different body visualization types in VR and in the RW having the regular visual input of body's components. Comparing the performances between the age groups, the time for completion, the number of steps (balance task), the subjective estimation of difficulty, the number of errors, and a rating system revealing movements' quality were considered as examined parameters. A one-way ANOVA/Friedmann with repeated measurements with factor [body visualization] was conducted to test the influence of varying body visualizations during task completion. Comparisons between the conditions [RW, VR] were performed using the t-Tests/Wilcoxon tests, and to compare both age groups [young, old], t-Tests for independent samples/Mann-Whitney-U-Test were used. The analyses of the effect of body visualization on performances showed a significant loss in movement's quality when no body part was visualized (p < .05). This did not occur for the elderly adults, for which no influence of the body visualization on their performance could be proven. Comparing both age groups, the elderly adults performed significantly worse than the young age group in both conditions (p < .05). In VR, both groups showed longer times for completion, a higher rating of tasks' difficulty in the balance and throwing task, and less performance quality in the grasping task. Overall, the results suggest using VR for the elderly with caution to the task demands, and the visualization of the body seemed less crucial for generating task completion. In summary, the actual task demands in VR could be successfully performed by elderly adults, even once one has to reckon with losses within movement's quality. Although more different movements should be tested, basic elements are also realizable for elderly adults expanding possible areas of VR applications.

Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters

Ageing, disease, and injuries result in movement defects that affect daily life. Gait analysis is a vital tool for understanding and evaluating these movement dysfunctions. In recent years, the use of virtual reality (VR) to observe motion and offer augmented clinical care has increased. Although VR-based methodologies have shown benefits in improving gait functions, their validity against more traditional methods (e.g., cameras or instrumented walkways) is yet to be established. In this work, we propose a procedure aimed at testing the accuracy and viability of a VIVE Virtual Reality system for gait analysis. Seven young healthy subjects were asked to walk along an instrumented walkway while wearing VR trackers. Heel strike (HS) and toe off (TO) events were assessed using the VIVE system and the instrumented walkway, along with stride length (SL), stride time (ST), stride width (SW), stride velocity (SV), and stance/swing percentage (STC, SWC%). Results from the VR were compared with the instrumented walkway in terms of detection offset for time events and root mean square error (RMSE) for gait features. An absolute offset between VR- and walkway-based data of (15.3 ± 12.8) ms for HS, (17.6 ± 14.8) ms for TOs and an RMSE of 2.6 cm for SW, 2.0 cm for SL, 17.4 ms for ST, 2.2 m/s for SV, and 2.1% for stance and swing percentage were obtained. Our findings show VR-based systems can accurately monitor gait while also offering new perspectives for VR augmented analysis.