Holocue: A Wearable Holographic Cueing Application for Alleviating Freezing of Gait in Parkinson's Disease.

External visual cueing is a well-known means to target freezing of gait (FOG) in Parkinson's disease patients. Holocue is a wearable visual cueing application that allows the HoloLens 1 mixed-reality headset to present on-demand patient-tailored action-relevant 2D and 3D holographic visual cues in free-living environments. The aim of this study involving 24 Parkinson's disease patients with dopaminergic "ON state" FOG was two-fold. First, to explore unfamiliarity and habituation effects associated with wearing the HoloLens on FOG. Second, to evaluate the potential immediate effect of Holocue on alleviating FOG in the home environment. Three sessions were conducted to examine (1) the effect of wearing the unfamiliar HoloLens on FOG by comparing walking with and without the HoloLens, (2) habituation effects to wearing the HoloLens by comparing FOG while walking with HoloLens over sessions, and (3) the potential immediate effect of Holocue on FOG by comparing walking with HoloLens with and without Holocue. Wearing the HoloLens (without Holocue) did significantly increase the number and duration of FOG episodes, but this unfamiliarity effect disappeared with habituation over sessions. This not only emphasizes the need for sufficient habituation to unfamiliar devices, but also testifies to the need for research designs with appropriate control conditions when examining effects of unfamiliar wearable cueing devices. Holocue had overall no immediate effect on FOG, although objective and subjective benefits were observed for some individuals, most notably those with long and/or many FOG episodes. Our participants raised valuable opportunities to improve Holocue and confirmed our assumptions about current and anticipated future design choices, which supports ongoing Holocue development for and with end users.

Quantifying Spatiotemporal Gait Parameters with HoloLens in Healthy Adults and People with Parkinson's Disease: Test-Retest Reliability, Concurrent Validity, and Face Validity.

Microsoft's HoloLens, a mixed-reality headset, provides, besides holograms, rich position data of the head, which can be used to quantify what the wearer is doing (e.g., walking) and to parameterize such acts (e.g., speed). The aim of the current study is to determine test-retest reliability, concurrent validity, and face validity of HoloLens 1 for quantifying spatiotemporal gait parameters. This was done in a group of 23 healthy young adults (mean age 21 years) walking at slow, comfortable, and fast speeds, as well as in a group of 24 people with Parkinson's disease (mean age 67 years) walking at comfortable speed. Walking was concurrently measured with HoloLens 1 and a previously validated markerless reference motion-registration system. We comprehensively evaluated HoloLens 1 for parameterizing walking (i.e., walking speed, step length and cadence) in terms of test-retest reliability (i.e., consistency over repetitions) and concurrent validity (i.e., between-systems agreement), using the intraclass correlation coefficient (ICC) and Bland-Altman's bias and limits of agreement. Test-retest reliability and between-systems agreement were excellent for walking speed (ICC ≥ 0.861), step length (ICC ≥ 0.884), and cadence (ICC ≥ 0.765), with narrower between-systems than over-repetitions limits of agreement. Face validity was demonstrated with significantly different walking speeds, step lengths and cadences over walking-speed conditions. To conclude, walking speed, step length, and cadence can be reliably and validly quantified from the position data of the wearable HoloLens 1 measurement system, not only for a broad range of speeds in healthy young adults, but also for self-selected comfortable speed in people with Parkinson's disease.

Avoiding 3D Obstacles in Mixed Reality: Does It Differ from Negotiating Real Obstacles?

Mixed-reality technologies are evolving rapidly, allowing for gradually more realistic interaction with digital content while moving freely in real-world environments. In this study, we examined the suitability of the Microsoft HoloLens mixed-reality headset for creating locomotor interactions in real-world environments enriched with 3D holographic obstacles. In Experiment 1, we compared the obstacle-avoidance maneuvers of 12 participants stepping over either real or holographic obstacles of different heights and depths. Participants' avoidance maneuvers were recorded with three spatially and temporally integrated Kinect v2 sensors. Similar to real obstacles, holographic obstacles elicited obstacle-avoidance maneuvers that scaled with obstacle dimensions. However, with holographic obstacles, some participants showed dissimilar trail or lead foot obstacle-avoidance maneuvers compared to real obstacles: they either consistently failed to raise their trail foot or crossed the obstacle with extreme lead-foot margins. In Experiment 2, we examined the efficacy of mixed-reality video feedback in altering such dissimilar avoidance maneuvers. Participants quickly adjusted their trail-foot crossing height and gradually lowered extreme lead-foot crossing heights in the course of mixed-reality video feedback trials, and these improvements were largely retained in subsequent trials without feedback. Participant-specific differences in real and holographic obstacle avoidance notwithstanding, the present results suggest that 3D holographic obstacles supplemented with mixed-reality video feedback may be used for studying and perhaps also training 3D obstacle avoidance.

Validation of Foot Placement Locations from Ankle Data of a Kinect v2 Sensor.

The Kinect v2 sensor may be a cheap and easy to use sensor to quantify gait in clinical settings, especially when applied in set-ups integrating multiple Kinect sensors to increase the measurement volume. Reliable estimates of foot placement locations are required to quantify spatial gait parameters. This study aimed to systematically evaluate the effects of distance from the sensor, side and step length on estimates of foot placement locations based on Kinect's ankle body points. Subjects (n = 12) performed stepping trials at imposed foot placement locations distanced 2 m or 3 m from the Kinect sensor (distance), for left and right foot placement locations (side), and for five imposed step lengths. Body points' time series of the lower extremities were recorded with a Kinect v2 sensor, placed frontoparallelly on the left side, and a gold-standard motion-registration system. Foot placement locations, step lengths, and stepping accuracies were compared between systems using repeated-measures ANOVAs, agreement statistics and two one-sided t-tests to test equivalence. For the right side at the 2 m distance from the sensor we found significant between-systems differences in foot placement locations and step lengths, and evidence for nonequivalence. This distance by side effect was likely caused by differences in body orientation relative to the Kinect sensor. It can be reduced by using Kinect's higher-dimensional depth data to estimate foot placement locations directly from the foot's point cloud and/or by using smaller inter-sensor distances in the case of a multi-Kinect v2 set-up to estimate foot placement locations at greater distances from the sensor.

Walking-adaptability assessments with the Interactive Walkway: Between-systems agreement and sensitivity to task and subject variations.

The ability to adapt walking to environmental circumstances is an important aspect of walking, yet difficult to assess. The Interactive Walkway was developed to assess walking adaptability by augmenting a multi-Kinect-v2 10-m walkway with gait-dependent visual context (stepping targets, obstacles) using real-time processed markerless full-body kinematics. In this study we determined Interactive Walkway's usability for walking-adaptability assessments in terms of between-systems agreement and sensitivity to task and subject variations. Under varying task constraints, 21 healthy subjects performed obstacle-avoidance, sudden-stops-and-starts and goal-directed-stepping tasks. Various continuous walking-adaptability outcome measures were concurrently determined with the Interactive Walkway and a gold-standard motion-registration system: available response time, obstacle-avoidance and sudden-stop margins, step length, stepping accuracy and walking speed. The same holds for dichotomous classifications of success and failure for obstacle-avoidance and sudden-stops tasks and performed short-stride versus long-stride obstacle-avoidance strategies. Continuous walking-adaptability outcome measures generally agreed well between systems (high intraclass correlation coefficients for absolute agreement, low biases and narrow limits of agreement) and were highly sensitive to task and subject variations. Success and failure ratings varied with available response times and obstacle types and agreed between systems for 85-96% of the trials while obstacle-avoidance strategies were always classified correctly. We conclude that Interactive Walkway walking-adaptability outcome measures are reliable and sensitive to task and subject variations, even in high-functioning subjects. We therefore deem Interactive Walkway walking-adaptability assessments usable for obtaining an objective and more task-specific examination of one's ability to walk, which may be feasible for both high-functioning and fragile populations since walking adaptability can be assessed at various levels of difficulty.

Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments.

Walking ability is frequently assessed with the 10-meter walking test (10MWT), which may be instrumented with multiple Kinect v2 sensors to complement the typical stopwatch-based time to walk 10 meters with quantitative gait information derived from Kinect's 3D body point's time series. The current study aimed to evaluate a multi-Kinect v2 set-up for quantitative gait assessments during the 10MWT against a gold-standard motion-registration system by determining between-systems agreement for body point's time series, spatiotemporal gait parameters and the time to walk 10 meters. To this end, the 10MWT was conducted at comfortable and maximum walking speed, while 3D full-body kinematics was concurrently recorded with the multi-Kinect v2 set-up and the Optotrak motion-registration system (i.e., the gold standard). Between-systems agreement for body point's time series was assessed with the intraclass correlation coefficient (ICC). Between-systems agreement was similarly determined for the gait parameters' walking speed, cadence, step length, stride length, step width, step time, stride time (all obtained for the intermediate 6 meters) and the time to walk 10 meters, complemented by Bland-Altman's bias and limits of agreement. Body point's time series agreed well between the motion-registration systems, particularly so for body points in motion. For both comfortable and maximum walking speeds, the between-systems agreement for the time to walk 10 meters and all gait parameters except step width was high (ICC ≥ 0.888), with negligible biases and narrow limits of agreement. Hence, body point's time series and gait parameters obtained with a multi-Kinect v2 set-up match well with those derived with a gold standard in 3D measurement accuracy. Future studies are recommended to test the clinical utility of the multi-Kinect v2 set-up to automate 10MWT assessments, thereby complementing the time to walk 10 meters with reliable spatiotemporal gait parameters obtained objectively in a quick, unobtrusive and patient-friendly manner.

Online gait event detection using a large force platform embedded in a treadmill.

Gait research and clinical gait training may benefit from movement-dependent event control, that is, technical applications in which events such as obstacle appearance or visual/acoustic cueing are (co)determined online on the basis of current gait properties. A prerequisite for successful gait-dependent event control is accurate online detection of gait events such as foot contact (FC) and foot off (FO). The objective of the present study was to assess the feasibility of online FC and FO detection using a single large force platform embedded in a treadmill. Center-of-pressure, total force output and kinematic data were recorded simultaneously in 12 healthy participants. Online FC and FO estimates and spatial and temporal gait parameters estimated from the force platform data--i.e., center-of-pressure profiles--were compared to offline kinematic counterparts, which served as the gold standard. Good correspondence was achieved between online FC detections using center-of-pressure profiles and those derived offline from kinematic data, whereas FO was detected 31 ms too late. A good relative and absolute agreement was achieved for both spatial and temporal gait parameters, which was improved further by applying more fine-grained FO estimation procedures using characteristic local minima in the total force output time series. These positive results suggest that the proposed system for gait-dependent event control may be successfully implemented in gait research as well as gait interventions in clinical practice.

Human kinematics and event control: on-line movement registration as a means for experimental manipulation.

In human movement and sports science, manipulations of perception and action are common and often comprise the control of events, such as opening or closing liquid crystal goggles. Most of these events are externally controlled, independent of the actions of the participants. Less common, although sometimes desirable, are event manipulations that are dependent on the unconstrained movements of participants. As an example, we describe a method we used previously to manipulate vision of basketball jump shooters on the basis of on-line registration of their own movements. The shooters wore liquid crystal goggles that opened or shut as a function of specific kinematic features of these movements. The novel aspect of this method is that the criteria for detecting movement patterns and performing the appropriate manipulations are adjustable to the specific sport context and the complexity and variations of the unconstrained movements. The method was implemented as a finite state machine: a computer system that can be used for pattern recognition. We discuss this method, how it works and the potential it has for studying perceptual-motor skills in sport. Furthermore, the results of the basketball experiment are briefly summarized and complemented with new analyses.