Abstract
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.
Highlights
While there is an abundance of data characterizing gait and timed up-and-go (TUG) metrics in older populations and in those with neurological disease, the assessment of gait speed and functional mobility in a clinical setting is inconsistent
The results indicated that the 3D position and orientation data from the HoloLens 1 (HL1) resulted in the accurate and reliable calculation of spatiotemporal gait variables (≤5% error) during straight-line walking compared to a motion capture systems (MoCap) system across multiple walking speeds [18]
The time series data of the HoloLens 2 (HL2) showed excellent agreement with the MoCap data based on RMS error values of 8.5 cm, 2.1 cm, and 0.8 cm in the X, Y, and Z axes, respectively
Summary
Identifying individuals at risk of falling remains a critical unmet need across neurological populations and older adults [1,2]. Gait speed has been proposed to be the “the sixth vital sign” [3], as it has been shown to predict hospitalizations, rehabilitation destinations, and falls [4–7]. The Timed Up and Go Test (TUG) is one of the most widely used clinical tests of functional mobility in neurological and geriatric populations [8], and provides an assessment of walking, turning, and postural transitions, including standing and sitting [9,10]. The TUG is a reliable and valid measure of fall prediction in older adults [11]. The fields of rehabilitation and neurology continue to identify a solution to provide a userfriendly, objective assessment of functional mobility that can be integrated into busy clinical workflows [12–14]
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