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

Abstract

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.

Validity of Hololens Augmented Reality Head Mounted Display for Measuring Gait Parameters in Healthy Adults and Children with Cerebral Palsy

“…But limits of agreement obtained with Bland-Altman analysis were quite narrow. Still, walking speed and step length were underestimated with biases increasing with faster walking speeds (min bias of 0.6 for people with PD to 3.1 for healthy people walking faster) [ 32 ]. They also found statistical difference between step length measured with Hololens and IWS ( p < 0.05).…”

“…They found an absolute errors between 55 to 250 mm in all 3 planes [ 38 ]. Using the same device, Geerse et al tested a method using head vertical maximal position to estimate foot step location [ 32 ]. This algorithm has shown a good test-retest reliability and a good concurrent validity at different walking speeds for healthy participants and for people with Parkinson’s disease (PD).…”

“…Several methods exist that use full kinematic captures [ 22 , 23 , 24 ] or lighter inertial sensors [ 25 , 26 , 27 , 28 , 29 , 30 ]. Two recent works exploit an AR HMD but do not involve children with CP [ 31 , 32 ]. In this paper, we present a new algorithm, called HoloStep, to compute spatiotemporal gait parameters of children with CP using only the head pose recorded with a Hololens AR HMD.…”

Design and Experimental Validation of an Augmented Reality System With Wireless Integration for Context Aware Enhanced Show Experience in Auditoriums

“…The following references have in common the use of Microsoft HoloLens smart glasses for indoor localization, using different techniques to improve their accuracy. A preliminary step towards localization is the work described in [56]. Such an article focuses on quantifying gait parameters like step length, cadence of walking speed in healthy adults and people with Parkinson.…”

“…Among the available dedicated devices, the implemented AR system is based on the Microsoft HoloLens smart glasses, which are currently the most advanced commercial AR device and have been previously used in multiple scenarios [56,57,59,86]. Microsoft HoloLens contains a custom-built Holographic Processing Unit (HPU 1.0), 2 GB of RAM and 64 GB of Flash, embedded sensors (ambient light sensor, accelerometer, gyroscope, magnetometer), seethrough holographic lenses, automatic pupillary distance calibration and human-machine interfaces (voice support, gaze tracking, spatial sound and gesture recognition).…”