A comprehensive approach to assess walking ability and fall risk using the Interactive Walkway

Neurological disorders may impair various aspects of walking ability that are needed for safe and independent walking. A comprehensive assessment addressing the key components of walking ability may help to tailor management strategies to the individual needs of each patient. The aspect of walking adaptability is usually not assessed in clinical tests, but seems important for safe walking and is related to fall risk. The Interactive Walkway is a promising, unobtrusive, low-cost and comprehensive assessment tool of walking ability in daily practice. It is a walkway instrumented with an integrated multi-Kinect v2 set-up for markerless registration of 3D full-body kinematics. Besides performing quantitative gait assessments, the Interactive Walkway may also be used to assess walking adaptability. The Interactive Walkway is equipped with a projector to augment the entire walkway with (gait-dependent) visual context, such as obstacles, sudden-stop-and-start cues and stepping targets, demanding step adjustments under time pressure demands in a safe manner. The aim of this thesis was to examine if 1) this approach can provide a valid assessment of walking ability and, if so, 2) if it has clinical potential in the assessment of walking ability and fall risk in patients with stroke and Parkinson’s Disease. The research presented in this thesis was part of the research program Technology in Motion (TIM [628.004.001]), which was financed by the Netherlands Organisation for Scientific Research (NWO).LUMC / Geneeskund

Gebruiksaanwijzing Cue X

Instructions for use Cue

Assessing Walking Adaptability in Parkinson's Disease:“The Interactive Walkway”

Introduction: In people with Parkinson's disease (PD) many aspects of walking ability deteriorate with advancing disease. Clinical tests typically evaluate single aspects of walking and to a lesser extent assess more complex walking tasks involving a combination of the three key aspects of walking ability (i.e., generating stepping, maintaining postural equilibrium, adapting walking). The Interactive Walkway allows for assessing more complex walking tasks to address features that are relevant for daily life walking of patients, including adaptive walking and dual-task walking. Methods: To evaluate the expected added value of Interactive Walkway assessments in people with PD, we first evaluated its known-groups validity for outcome measures of unconstrained walking, adaptive walking and dual-task walking. Subsequently, these outcome measures were related to commonly used clinical test scores. Finally, we evaluated the expected added value of these outcomes over clinical tests scores in discriminating people with PD with and without freezing of gait. Results: Interactive Walkway outcome measures showed significant differences between freezers, non-freezers and healthy controls, in expected directions. Most Interactive Walkway outcome measures were not or at best moderately correlated with clinical test scores. Finally, Interactive Walkway outcome measures of adaptive walking slightly better discriminated freezers from non-freezers than clinical tests scores. Conclusion: We confirmed the added value of Interactive Walkway assessments, which provides a comprehensive evaluation of walking ability incorporating features of its three key aspects. Future studies are warranted to examine the potential of the Interactive Walkway for the assessment of fall risk and informing on tailored falls prevention programs in people with PD and in other populations with impaired walking ability

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

‘Haste makes waste’:The tradeoff between walking speed and target-stepping accuracy

Background: When environmental conditions require accurate foot placement during walking (e.g., on a rough path), we typically walk slower to avoid tripping, slipping or stumbling. Likewise, hurrying too much is a common situational circumstance of walking-related falls. This suggests a tradeoff between walking speed and stepping accuracy in situations that demand precise foot placement. Research question: How can this expected tradeoff between walking speed and stepping accuracy best be parameterized? Methods: In Experiment 1, participants (n = 20) walked at five different speeds over an irregularly spaced sequence of projected stepping targets. Participants were instructed to place their feet accurately onto the targets, while following a constant-speed cue running alongside the walkway. Stepping accuracy was parameterized as overall (RMSE, root mean square error), variable (VE) and constant (CE) stepping errors, quantified over targets as well as per target. In Experiment 2, we determined preferred walking speed and stepping accuracy for regularly and irregularly spaced stepping targets. Results: Repeated-measures ANOVAs revealed that RMSE and VE grew linearly with increasing speeds, both over targets as well as per target. Per target CE varied in magnitude and sign with variations in inter-target spacing: for shorter inter-target spacing targets were overshot (CE > 0), while for longer inter-target spacing targets were undershot (CE < 0). This effect was stronger for faster speeds and for targets preceded by the shortest and longest inter-target spacing. Preferred walking speed and per-target VE did not differ between regularly and irregularly spaced targets. Significance: Participants stepped less precisely when walking faster. The linear increase in VE with faster speeds was consistent with Schmidt's law regarding the speed-accuracy tradeoff. The systematic comparison of stepping errors over regularly and irregularly spaced stepping-target conditions further provided important clues on how to best parameterize stepping accuracy: per stepping target using VE (i.e., stepping inconsistency), complemented with CE (i.e., stepping bias) in case of irregular inter-target spacing

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

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

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

<div><p>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.</p></div

Assessing walking adaptability in stroke patients

Purpose: The ability to adapt walking is important for safe ambulation. Assessments of impairments in walking adaptability with the Interactive Walkway may aid in the development of individualized therapy strategies of stroke patients. The Interactive Walkway is an overground walkway with Kinect v2 sensors for a markerless registration of full-body kinematics, which can be augmented with (gait-dependent) visual context to assess walking adaptability. This study aims to evaluate the potential of the Interactive Walkway as a new technology for assessing walking adaptability in stroke patients. Materials and methods: 30 stroke patients and 30 controls performed clinical tests, quantitative gait assessments and various walking-adaptability tasks on the Interactive Walkway. Outcome measures were compared between stroke patients and controls to examine known-groups validity. Pearson’s correlation coefficients were calculated to assess the relationship between walking-adaptability outcomes and commonly used clinical test scores of walking ability and spatiotemporal gait parameters of unconstrained walking. Results: Good known-groups validity for walking-adaptability outcomes was demonstrated. In addition, the vast majority of walking-adaptability outcomes did not or only moderately correlate with clinical test scores of walking ability and unconstrained walking parameters. Conclusion: Interactive Walkway walking-adaptability outcomes have good known-groups validity and complement standard clinical tests and spatiotemporal gait parameters.IMPLICATIONS FOR REHABILITATION The Interactive Walkway allows for a comprehensive walking-adaptability assessment. Good known-groups validity for walking-adaptability tasks was demonstrated and walking-adaptability tasks complemented clinical tests and gait parameters. The Interactive Walkway has potential for monitoring recovery of walking after stroke. Assessments of walking adaptability may contribute to individualized interventions