Exoskeletons for gait training commonly use a rigid-linked “skeleton” which makes them heavy and bulky. Cable-driven exoskeletons eliminate the rigid-linked skeleton, providing a lighter and transparent design. Current cable-driven exoskeletons are aimed only at gait assistance by provide short bursts of forces to the leg during walking. It has not yet been shown if these designs are suitable for gait retraining where rehabilitative forces need to be continuously applied to the leg in response to errors from a desired movement. The goal of this study is to investigate if a cable-driven leg exoskeleton can retrain the gait of human users. Nine healthy subjects were trained by a cable-driven leg exoskeleton to walk in a new gait pattern with 30% increase in step height from their natural gait. After 40 minutes of training, the gait of the subjects became significantly closer to the target gait than before the training. In three different post-training sessions, the step height of the subjects increased by 22%, 29% and 31% on an average. In a fourth post-training session when the subjects were instructed to ignore the training and walk naturally, the step height remained increased by 11%. These results confirm the potential of cable-driven designs in gait training applications.

Virtual reality (VR) is a useful tool to assess and improve spatial navigation, a complex skill and relevant marker for progression of dementia. A fully-immersive VR system that allows the user to physically navigate in the space can provide an ecologically valid environment for early detection and remediation of cognitive and navigational deficits. The aim of this study was to develop a virtual version of the floor maze test (VR-FMT), a navigational test that requires navigating through an unfamiliar two-dimensional floor maze. With the VR-FMT, mazes of desired complexity and walls of preferred height can be built to challenge navigational ability and mask visual clues. Fifty-five healthy adults completed the FMT in three different conditions: real environment (RE), virtual environment with no walls (VE-NW), and virtual environment with walls (VE-W). In addition, they completed two neuropsychological tests, the Trail Making Test and Digit Symbol Substitution Test. Results showed that the time to complete the maze in the VE was significantly higher than in the RE. The introduction of walls increased the number of errors, the completion time, and the length of the path. Only time to exit in the VE-W correlated with results of the cognitive tests. Participants were further subdivided on the basis of their time to exit the maze in the RE, VE-NW, and VE-W (low navigational time -LNT, and high navigational time -HNT). Only when analyzing the time to exit the maze in the VE-W, the LNT group outperformed the HNT group in all cognitive tests.

Freezing of gait is an episodic phenomena faced by many patients with Parkinson's disease. It is characterized by episodes during which patients are unable to generate effective forward stepping movements, despite absence of motor deficits. During the onset of the event, the patients are less stable with statistically different stride width, toe in/out angle and center of pressure distance. It has been postulated that the degree of freezing can be reduced by providing external sensory feedback to the patients during the event. However, this intervention could be facilitated by accurate identification of freezing events in real-time. This manuscript presents an Artificial Neural Network model which uses signals recorded by an instrumented footwear to predict if a walking subject is having a freezing episode. Our model presented in this paper is capable of continuously predicting freezing of gait events at a high temporal resolution of 50 Hz, using a 0.5 second window of data recorded by the instrumented shoes, with a sensitivity of 96.0±2.5%, a specificity of 99.6±0.3%, a precision of 89.5±5.9%, and an accuracy of 99.5±0.4%. This algorithm was tested with data collected from 10 patients with Parkinson's disease with frequent freezing of gait episodes.

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.

Virtual Reality is a versatile platform to study human behavior in simulated environments and to develop interventions for functional rehabilitation. In this work, we designed a dual-task paradigm in a virtual environment where both tasks demand motor skills. Twenty-one healthy adults (mean age: 24.1 years) participated in this study. The experiment involved three conditions - normal overground walking, catch and throw a ball while standing, and catch and throw a ball while walking overground -all in the virtual environment. We investigated the dual-task gait characteristics and their correlations with outcomes from cognitive assessments. Results show that subjects walk conservatively with smaller stride lengths, larger stride widths and stride time while catching and throwing. However, they are able to throw the balls more accurately at the target and achieve higher scores. During the dual-task throw, we observed that the participants threw more balls during the stance phase of the gait when the foot was in the terminal stance and pre-swing region. During this region, the body has forward momentum. In addition, the changes in gait characteristics during dual-task throw correlate well with outcome measures in standardized cognitive tests. This study provides a new and engaging paradigm to analyze dual-motor-task cost in a virtual reality environment and it can be used as a basis to compare strategies adopted by different population groups with healthy young adults to execute coordinated motor tasks.