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      The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment

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          Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in older adults. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLens TM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older and 12 young adults) participated in this study. An automated functional mobility assessment app was developed, based on the HoloLens platform. The mobility performance was recorded with the headset built-in sensor and reference inertial sensor (Opal, APDM) taped on the headset and lower back. The results indicate that the vertical kinematic measurements by HoloLens were in good agreement with the reference sensor (Normalized RMSE ~ 10%, except for cases where the inertial sensor drift correction was not viable). Additionally, the HoloLens-based test completion time was in perfect agreement with the clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users (without severe mobility deficit) to complete common mobility tests, and this approach has the potential to provide an objective and efficient sensor-based mobility assessment that does not require any direct research/clinical oversight.

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          Most cited references 29

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            The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.

            To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia. Validation study. A community clinic and an academic center. Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC). The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD. Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively). MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
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              Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test.

              This study examined the sensitivity and specificity of the Timed Up & Go Test (TUG) under single-task versus dual-task conditions for identifying elderly individuals who are prone to falling. Fifteen older adults with no history of falls (mean age=78 years, SD=6, range=65-85) and 15 older adults with a history of 2 or more falls in the previous 6 months (mean age=86.2 years, SD=6, range=76-95) participated. Time taken to complete the TUG under 3 conditions (TUG, TUG with a subtraction task [TUGcognitive], and TUG while carrying a full cup of water [TUGmanual]) was measured. A multivariate analysis of variance and discriminant function and logistic regression analyses were performed. The TUG was found to be a sensitive (sensitivity=87%) and specific (specificity=87%) measure for identifying elderly individuals who are prone to falls. For both groups of older adults, simultaneous performance of an additional task increased the time taken to complete the TUG, with the greatest effect in the older adults with a history of falls. The TUG scores with or without an additional task (cognitive or manual) were equivalent with respect to identifying fallers and nonfallers. The results suggest that the TUG is a sensitive and specific measure for identifying community-dwelling adults who are at risk for falls. The ability to predict falls is not enhanced by adding a secondary task when performing the TUG.

                Author and article information

                [1 ]Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; aldunate@ (R.G.A.); jsosnoff@ (J.J.S.)
                [2 ]Department of Orthopaedic Surgery, Stanford University, Stanford, CA 94305, USA
                Author notes
                [* ]Correspondence: rusun@
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                11 May 2019
                May 2019
                : 19
                : 9
                31083514 6539854 10.3390/s19092183 sensors-19-02183
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (



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