Can google glass™ technology improve freezing of gait in parkinsonism? A pilot study

Gait-modifying effects of augmented-reality cueing in people with Parkinson's disease

Introduction

External cueing can improve gait in people with Parkinson's disease (PD), but there is a need for wearable, personalized and flexible cueing techniques that can exploit the power of action-relevant visual cues. Augmented Reality (AR) involving headsets or glasses represents a promising technology in those regards. This study examines the gait-modifying effects of real-world and AR cueing in people with PD.

Methods

21 people with PD performed walking tasks augmented with either real-world or AR cues, imposing changes in gait speed, step length, crossing step length, and step height. Two different AR headsets, differing in AR field of view (AR-FOV) size, were used to evaluate potential AR-FOV-size effects on the gait-modifying effects of AR cues as well as on the head orientation required for interacting with them.

Results

Participants modified their gait speed, step length, and crossing step length significantly to changes in both real-world and AR cues, with step lengths also being statistically equivalent to those imposed. Due to technical issues, step-height modulation could not be analyzed. AR-FOV size had no significant effect on gait modifications, although small differences in head orientation were observed when interacting with nearby objects between AR headsets.

Conclusion

People with PD can modify their gait to AR cues as effectively as to real-world cues with state-of-the-art AR headsets, for which AR-FOV size is no longer a limiting factor. Future studies are warranted to explore the merit of a library of cue modalities and individually-tailored AR cueing for facilitating gait in real-world environments.

A Randomized Clinical Trial to Evaluate a Digital Therapeutic to Enhance Gait Function in Individuals With Parkinson's Disease

BACKGROUND

Postural instability and gait dysfunction (PIGD) is a cardinal symptom of Parkinson's disease (PD) and is exacerbated under dual-task conditions. Dual-task training (DTT), enhances gait performance, however it is time and cost intensive. Digitizing DTT via the Dual-task Augmented Reality Treatment (DART) platform can expand the availability of an effective intervention to address PIGD.

OBJECTIVE

The aim of this project was to evaluate DART in the treatment of PIGD in people with PD compared to a Traditional DTT intervention. It was hypothesized that both groups would exhibit significant improvements in gait, and the improvements for the DART group would be non-inferior to Traditional DTT.

METHODS

A single-blind randomized controlled trial was conducted with 47 PD participants with PIGD. Both groups completed 16 therapeutic sessions over 8 weeks; the DART platform delivered DTT via the Microsoft HoloLens2. Primary outcomes included clinical ratings and single- and dual-task gait biomechanical outcomes.

RESULTS

Clinical measures of PD symptoms remained stable for DART and Traditional DTT groups. However, both groups exhibited a significant increase in gait velocity, cadence, and step length during single- and multiple dual-task conditions following the interventions. Improvements in gait velocity in the DART group were non-inferior to Traditional DTT under the majority of conditions.

CONCLUSION

Non-inferior improvements in gait parameters across groups provides evidence of the DART platform being an effective digital therapeutic capable of improving PIGD. Effective digital delivery of DTT has the potential to increase use and accessibility to a promising, yet underutilized and difficult to administer, intervention for PIGD.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Dual-task Augmented Reality Treatment for Parkinson's Disease (DART) NCT04634331; posted November 18, 2020.

Digitizing a Therapeutic: Development of an Augmented Reality Dual-Task Training Platform for Parkinson's Disease

Augmented reality (AR) may be a useful tool for the delivery of dual-task training. This manuscript details the development of the Dual-task Augmented Reality Treatment (DART) platform for individuals with Parkinson's disease (PD) and reports initial feasibility, usability, and efficacy of the DART platform in provoking dual-task interference in individuals with PD. The DART platform utilizes the head-mounted Microsoft HoloLens2 AR device to deliver concurrent motor and cognitive tasks. Biomechanical metrics of gait and cognitive responses are automatically computed and provided to the supervising clinician. To assess feasibility, individuals with PD (N = 48) completed a bout of single-task and dual-task walking using the DART platform. Usability was assessed by the System Usability Scale (SUS). Dual-task interference was assessed by comparing single-task walking and walking during an obstacle course while performing a cognitive task. Average gait velocity decreased from 1.06 to 0.82 m/s from single- to dual-task conditions. Mean SUS scores were 81.3 (11.3), which placed the DART in the "good" to "excellent" category. To our knowledge, the DART platform is the first to use a head-mounted AR system to deliver a dual-task paradigm and simultaneously provide biomechanical data that characterize cognitive and motor performance. Individuals with PD were able to successfully use the DART platform with satisfaction, and dual-task interference was provoked. The DART platform should be investigated as a platform to treat dual-task declines associated with PD.

A Taxonomy for Augmented and Mixed Reality Applications to Support Physical Exercises in Medical Rehabilitation—A Literature Review

In the past 20 years, a vast amount of research has shown that Augmented and Mixed Reality applications can support physical exercises in medical rehabilitation. In this paper, we contribute a taxonomy, providing an overview of the current state of research in this area. It is based on a comprehensive literature review conducted on the five databases Web of Science, ScienceDirect, PubMed, IEEE Xplore, and ACM up to July 2021. Out of 776 identified references, a final selection was made of 91 papers discussing the usage of visual stimuli delivered by AR/MR or similar technology to enhance the performance of physical exercises in medical rehabilitation. The taxonomy bridges the gap between a medical perspective (Patient Type, Medical Purpose) and the Interaction Design, focusing on Output Technologies and Visual Guidance. Most approaches aim to improve autonomy in the absence of a therapist and increase motivation to improve adherence. Technology is still focused on screen-based approaches, while the deeper analysis of Visual Guidance revealed 13 distinct, reoccurring abstract types of elements. Based on the analysis, implications and research opportunities are presented to guide future work.

Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.

Assessment of Motor Dysfunction with Virtual Reality in Patients Undergoing [123I]FP-CIT SPECT/CT Brain Imaging

[¹²³I]FP-CIT SPECT has been valuable for distinguishing Parkinson disease (PD) from essential tremor. However, its performance for quantitative assessment of motor dysfunction has not been established. A virtual reality (VR) application was developed and compared with [¹²³I]FP-CIT SPECT/CT for detection of severity of motor dysfunction. Forty-four patients (21 males, 23 females, age 64.5 ± 12.4) with abnormal [¹²³I]FP-CIT SPECT/CT underwent assessment of bradykinesia, activities of daily living, and tremor with VR. Support vector machines (SVM) machine learning models were applied to VR and SPECT data. Receiver operating characteristic (ROC) analysis demonstrated greater area under the curve (AUC) for VR (0.8418, 95% CI 0.6071–0.9617) compared with brain SPECT (0.5357, 95% CI 0.3373–0.7357, p = 0.029) for detection of motor dysfunction. Logistic regression identified VR as an independent predictor of motor dysfunction (Odds Ratio 326.4, SE 2.17, p = 0.008). SVM for prediction of the Unified Parkinson’s Disease Rating Scale Part III (UPDRS-III) demonstrated greater R-squared of 0.713 (p = 0.008) for VR, compared with 0.0764 (p = 0.361) for brain SPECT. This study demonstrates that VR can be safely used in patients prior to [¹²³I]FP-CIT SPECT imaging and may improve prediction of motor dysfunction. This test has the potential to provide a simple, objective, quantitative analysis of motor symptoms in PD patients.

Wearables als unterstützendes Tool für den Paradigmenwechsel in der Versorgung von Parkinson Patienten

Tragbare Sensoren – „Wearables“ – eignen sich, Funktionsstörungen bei Parkinson Patienten zu erheben und werden zur Prävention, Prädiktion, Diagnostik und Therapieunterstützung genutzt. In der Forschung erhöhen sie die Reliabilität der erhobenen Daten und stellen bessere Studien-Endpunkte dar, als die herkömmlichen, subjektiven und wenig quantitativen Rating- und Selbstbeurteilungsskalen. Untersucht werden motorische Symptome wie Tremor, Bradykinese und Gangstörungen und auch nicht motorische Symptome. In der Home-Monitoringanwendung kann der Ist-Zustand des Patienten im realen Leben untersucht werden, die Therapie überwacht, die Adhärenz verbessert und die Compliance überprüft werden. Zusätzlich können Wearables interventionell zur Verbesserung von Symptomen eingesetzt werden wie z. B. Cueing, Gamification oder Coaching. Der Transfer von Laborbedingungen in den häuslichen Alltag ist eine medizinisch-technische Herausforderung. Optimierte Versorgungsmodelle müssen entwickelt werden und der tatsächliche Nutzen für den individuellen Patienten in weiteren Studien belegt werden.