Gait patterns during overground and virtual omnidirectional treadmill walking.

BACKGROUND: Omnidirectional treadmills (ODTs) offer a promising solution to the virtual reality (VR) locomotion problem, which describes the mismatch between visual and somatosensory information and contributes to VR sickness. However, little is known about how walking on ODTs impacts the biomechanics of gait. This project aimed to compare overground and ODT walking and turning in healthy young adults. METHODS: Fifteen young adults completed forward walk, 180° turn, and 360° turn tasks under three conditions: (1) overground, (2) on the Infinadeck ODT in a virtual environment without a handrail, and (3) on the ODT with a handrail. Kinematic data for all walking trials were gathered using 3D optical motion capture. RESULTS: Overall, gait speed was slower during ODT walking than overground. When controlling for gait speed, ODT walking resulted in shorter steps and greater variability in step length. There were no significant differences in other spatiotemporal metrics between ODT and overground walking. Turning on the ODT required more steps and slower rotational speeds than overground turns. The addition of the stability handrail to the ODT resulted in decreased gait variability relative to the ODT gait without the handrail. CONCLUSION: Walking on an ODT resembles natural gait patterns apart from slower gait speed and shorter step length. Slower walking and shorter step length are likely due to the novelty of physically navigating a virtual environment which may result in a more conservative approach to gait. Future work will evaluate how older adults and those with neurological disease respond to ODT walking.

The Microsoft HoloLens 2 Provides Accurate Measures of Gait, Turning, and Functional Mobility in Healthy Adults.

Augmented-reality (AR) headsets, such as the Microsoft HoloLens 2 (HL2), have the potential to be the next generation of wearable technology as they provide interactive digital stimuli in the context of ecologically-valid daily activities while containing inertial measurement units (IMUs) to objectively quantify the movements of the user. A necessary precursor to the widespread utilization of the HL2 in the fields of movement science and rehabilitation is the rigorous validation of its capacity to generate biomechanical outcomes comparable to gold standard outcomes. This project sought to determine equivalency of kinematic outcomes characterizing lower-extremity function derived from the HL2 and three-dimensional (3D) motion capture systems (MoCap). Sixty-six healthy adults completed two lower-extremity tasks while kinematic data were collected from the HL2 and MoCap: (1) continuous walking and (2) timed up-and-go (TUG). For all the continuous walking metrics (cumulative distance, time, number of steps, step and stride length, and velocity), equivalence testing indicated that the HL2 and MoCap were statistically equivalent (error ≤ 5%). The TUG metrics, including turn duration and turn velocity, were also statistically equivalent between the two systems. The accurate quantification of gait and turning using a wearable such as the HL2 provides initial evidence for its use as a platform for the development and delivery of gait and mobility assessments, including the in-person and remote delivery of highly salient digital movement assessments and rehabilitation protocols.

An Augmented Reality Rifle Qualification Test for Return-to-Duty Assessment in Service Members.

INTRODUCTION: Variability in return-to-duty (RTD) decision-making following mild traumatic brain injury (mTBI) is a threat to troop readiness. Current RTD assessments lack military-specific tasks and quantitative outcomes to inform stakeholders of a service member's (SM) capacity to successfully perform military duties. Augmented reality (AR), which places digital assets in a user's physical environment, provides a technological vehicle to deliver military-relevant tasks to a SM to be used in the RTD decision-making process. In addition to delivering digital content, AR headsets provide biomechanical data that can be used to assess the integrity of the central nervous system in movement control following mTBI. The objective of this study was to quantify cognitive and motor performance on an AR rifle qualification test (RQT) in a group of neurologically healthy military SMs. MATERIALS AND METHODS: Data were collected from 111 healthy SMs who completed a basic (single-task) and complex (dual-task) RQT with a simulated M4 rifle. The complex scenario required the SM to perform the RQT while simultaneously answering arithmetic problems. Position data from the AR headset were used to capture postural sway, and the built-in microphone gathered responses to the arithmetic problems. RESULTS: There were no differences in the number of targets hit, trigger pull reaction time, and transition time from kneeling to standing between the basic and complex scenarios. A significant worsening in postural sway following kneel-to-stand transition was observed in the complex scenario. The average reaction time to answer the arithmetic problems was nearly 2 times slower than the average reaction time to pull the trigger to a displayed target in the complex scenario. CONCLUSION: The complex scenario provoked dual-task interference in SMs as evidenced by worsening postural sway and reaction time differences between the cognitive and motor tasks. An AR RQT provides objective and quantitative outcomes during a military-specific task. Greater precision in evaluating cognitive and motor performance during a military-relevant task has the potential to aid in the detection and management of SMs and their RTD following MTBI.

Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records.

Objective: This study assesses the application of interpretable machine learning modeling using electronic medical record data for the prediction of conversion to neurological disease. Methods: A retrospective dataset of Cleveland Clinic patients diagnosed with Alzheimer's disease, amyotrophic lateral sclerosis, multiple sclerosis, or Parkinson's disease, and matched controls based on age, sex, race, and ethnicity was compiled. Individualized risk prediction models were created using eXtreme Gradient Boosting for each neurological disease at four timepoints in patient history. The prediction models were assessed for transparency and fairness. Results: At timepoints 0-months, 12-months, 24-months, and 60-months prior to diagnosis, Alzheimer's disease models achieved the area under the receiver operating characteristic curve on a holdout test dataset of 0.794, 0.742, 0.709, and 0.645; amyotrophic lateral sclerosis of 0.883, 0.710, 0.658, and 0.620; multiple sclerosis of 0.922, 0.877, 0.849, and 0.781; and Parkinson's disease of 0.809, 0.738, 0.700, and 0.651, respectively. Conclusions: The results demonstrate that electronic medical records contain latent information that can be used for risk stratification for neurological disorders. In particular, patient-reported outcomes, sleep assessments, falls data, additional disease diagnoses, and longitudinal changes in patient health, such as weight change, are important predictors.

Use of a Home-Based, Commercial Exercise Platform to Remotely Monitor Aerobic Exercise Adherence and Intensity in People With Parkinson Disease.

OBJECTIVE: Physical therapists are well-positioned to prescribe exercise outside of a clinical setting to promote positive health behaviors in people with Parkinson disease (PD). Traditionally, a barrier to precise exercise prescription has been reliance on participant self-reported exercise adherence and intensity. Home-based, commercially available exercise platforms offer an opportunity to remotely monitor exercise behavior and facilitate adherence based on objective performance metrics. The primary aim of this project was to characterize the feasibility and processes of remote aerobic exercise data monitoring from a home-based, commercially available platform in individuals participating in the 12-month Cyclical Lower Extremity Exercise for PD II (CYCLE-II) randomized clinical trial. Secondary aims focused on using exercise behavior to classify the cohort into exercise archetypes and describing a shared decision-making process to facilitate exercise adherence. METHODS: Data from each exercise session were extracted, visualized, and filtered to ensure ride integrity. Weekly exercise frequency was used to determine exercise archetypes: Adherent (2-4 exercise sessions per week), Over-adherent (>4 exercise sessions per week), and Under-adherent (<2 exercise sessions per week). RESULTS: A total of 123 people with PD completed 22,000+ exercise sessions. Analysis of exercise frequency indicated that 79% of participants were adherent; 8% were over-adherent; and 13% were under-adherent. Three case reports illustrate how shared decision-making with the use of exercise performance data points guided exercise prescription. CONCLUSIONS: The number of exercise sessions and completeness of the data indicate that people with PD were able to utilize a commercial, home-based exercise platform to successfully engage in long-term aerobic exercise. Physical therapists can use objective data as a part of a shared decision-making process to facilitate exercise adherence. IMPACT: Commercially available exercise platforms offer a unique approach for physical therapists to monitor exercise behavior outside of a clinical setting. The methods used in this project can serve as a roadmap to utilizing data from consumer-based platforms.

The Microsoft HoloLens 2 Provides Accurate Biomechanical Measures of Performance During Military-Relevant Activities in Healthy Adults.

INTRODUCTION: Augmented reality systems, like the HoloLens 2 (HL2), have the potential to provide accurate assessments of mild traumatic brain injury (mTBI) symptoms in military personnel by simulating complex military scenarios while objectively measuring the user's movements with embedded motion sensors. The aim of this project was to determine if biomechanical measures of marching and squatting, derived from the HL2 motion sensors, were statistically equivalent, within 5%, to metrics derived from the gold-standard three-dimensional motion capture (MoCap) system. MATERIALS AND METHODS: Sixty-four adults (18-45 years; 34 males) completed a squatting and a marching task under single- (motor) and dual-task (motor + cognitive) conditions. Positional data from the HL2 and MoCap were simultaneously collected during the tasks and used to calculate and compare biomechanical outcomes. The HL2's augmented reality capabilities were utilized to deliver the cognitive component of the marching dual task. RESULTS: Equivalence testing indicated the HL2 and MoCap measures were within 5% in all squatting metrics-trial time, squat duration, squat velocity, squat depth, and dwell time. Marching metrics, including trial time, step count, stepping rate, and step interval, were also equivalent between the two systems. The mean reaction time for responses during the Stroop test was 810 (125) milliseconds per response. CONCLUSIONS: Biomechanical outcomes characterizing performance during two common military movements, squatting and marching, were equivalent between the HL2 and MoCap systems in healthy adults. Squatting and marching are two military-relevant tasks that require strength, motor coordination, and balance to perform, all of which are known to be affected by mTBI. Taken together, the data provide support for using the HL2 platform to deliver military-specific assessment scenarios and accurately measure performance during these activities. Utilizing objective and quantitative measures of motor function may enhance the management of military mTBI and reduce unnecessary risk to service members.

Development of the Troop Readiness Evaluation With Augmented Reality Return-to-Duty (Troop READY) Platform to Aid in the Detection and Treatment of Military Mild Traumatic Brain Injury.

INTRODUCTION: Mild traumatic brain injury (mTBI) is prevalent in service members (SMs); however, there is a lack of consensus on the appropriate approach to return to duty (RTD). Head-mounted augmented reality technology, such as the HoloLens 2, can create immersive, salient environments to more effectively evaluate relevant military task performance. The Troop Readiness Evaluation with Augmented Reality Return-to-Duty (READY) platform was developed to objectively quantify cognitive and motor performance during military-specific activities to create a comprehensive approach to aid in mTBI detection and facilitate appropriate RTD. The aim of this project was to detail the technical development of the Troop READY platform, the outcomes, and its potential role in the aiding detection and RTD decision-making post mTBI. The secondary aim included evaluating the safety, feasibility, and SM usability of the Troop READY platform. MATERIALS AND METHODS: The Troop READY platform comprises three assessment modules of progressing complexity: (1) Static and Dynamic Mobility, (2) Rifle Qualification Test, and (3) Small Unit Operations Capacity-Room Breach/Clearing Exercise. The modules were completed by 137 active duty SMs. Safety was assessed through monitoring of adverse events. Feasibility was assessed using the self-directed module completion rate. Usability was measured using the Systems Usability Scale. RESULTS: No adverse events occurred. Completion rates of the three modules ranged from 98 to 100%. In terms of usability, the mean Systems Usability Scale score of all participants was 83.92 (13.95), placing the Troop READY platform in the good-to-excellent category. Objective motor and cognitive outcomes were generated for each module. CONCLUSION: The Troop READY platform delivers self-directed, salient assessment modules to quantify single-task, dual-task, and unit-based performance in SMs. The resultant data provide insight into SM performance through objective outcomes and identify specific areas of executive or motor function that may be slow to recover following mTBI.