An automated, electronic assessment tool can accurately classify older adult postural stability.

Current methods of balance assessment in the clinical environment are often subjective, time-consuming and lack clinical relevance for non-ambulatory older adults. The objective of this study was to develop a novel method of balance assessment that utilizes data collected using the Microsoft Kinect 2 to create a Berg Balance Scale score, which is completely determined by statistical methods rather than by human evaluators. 74 older adults, both healthy and balance impaired, were recruited for this trial. All participants completed the Berg Balance Scale (BBS) which was scored independently by trained physical therapists. Participants then completed the items of the "Modified Berg Balance Scale" in front of the Microsoft Kinect camera. Kinematic data collected during this measurement was used to train a feed-forward neural network that was used to assign a Berg Balance Scale score. The neural network model estimated the clinician-assigned BBS score to within a median of 0.93 points for the participants in our sample population (range: 0.02-5.69). Using low-cost depth sensing camera technology and a clinical protocol that takes less than 5 min to complete in both ambulatory and non-ambulatory older adults, the method outlined in this manuscript can accurately predict a participant's BBS score and thereby identify whether they are deemed a high fall risk or not. If implemented correctly, this could enable fall prevention services to be deployed in a timely fashion using low-cost, accessible technology, resulting in improved safety of older adults.

Using data from the Microsoft Kinect 2 to determine postural stability in healthy subjects: A feasibility trial.

The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy subjects. Twelve subjects were recruited for the project, and were instructed to perform a sequence of simple postural stability tasks. The movement sequence was performed as subjects were seated on top of a force platform, and the MK2 was positioned in front of them. This sequence of tasks was performed by each subject under three different postural conditions: "both feet on the ground" (1), "One foot off the ground" (2), and "both feet off the ground" (3). We compared force platform and MK2 data to quantify the degree to which the MK2 was returning reliable data across subjects. We then applied a novel machine-learning paradigm to the MK2 data in order to determine the extent to which data from the MK2 could be used to reliably classify different postural conditions. Our initial comparison of force plate and MK2 data showed a strong agreement between the two devices, with strong Pearson correlations between the trunk centroids "Spine_Mid" (0.85 ± 0.06), "Neck" (0.86 ± 0.07) and "Head" (0.87 ± 0.07), and the center of pressure centroid inferred by the force platform. Mean accuracy for the machine learning classifier from MK2 was 97.0%, with a specific classification accuracy breakdown of 90.9%, 100%, and 100% for conditions 1 through 3, respectively. Mean accuracy for the machine learning classifier derived from the force platform data was lower at 84.4%. We conclude that data from the MK2 has sufficient information content to allow us to classify sequences of tasks being performed under different levels of postural stability. Future studies will focus on validating this protocol on large populations of individuals with actual balance impairments in order to create a toolkit that is clinically validated and available to the medical community.

Using Data From the Microsoft Kinect 2 to Quantify Upper Limb Behavior: A Feasibility Study.

The objective of this study was to assess whether the novel application of a machine learning approach to data collected from the Microsoft Kinect 2 (MK2) could be used to classify differing levels of upper limb impairment. Twenty-four healthy subjects completed items of the Wolf Motor Function Test (WMFT), which is a clinically validated metric of upper limb function for stroke survivors. Subjects completed the WMFT three times: 1) as a healthy individual; 2) emulating mild impairment; and 3) emulating moderate impairment. A MK2 was positioned in front of participants, and collected kinematic data as they completed the WMFT. A classification framework, based on Riemannian geometry and the use of covariance matrices as feature representation of the MK2 data, was developed for these data, and its ability to successfully classify subjects as either "healthy," "mildly impaired," or "moderately impaired" was assessed. Mean accuracy for our classifier was 91.7%, with a specific accuracy breakdown of 100%, 83.3%, and 91.7% for the "healthy," "mildly impaired," and "moderately impaired" conditions, respectively. We conclude that data from the MK2 is of sufficient quality to perform objective motor behavior classification in individuals with upper limb impairment. The data collection and analysis framework that we have developed has the potential to disrupt the field of clinical assessment. Future studies will focus on validating this protocol on large populations of individuals with actual upper limb impairments in order to create a toolkit that is clinically validated and available to the clinical community.

Quantifying Poststroke Apathy With Actimeters.

The authors tested the hypothesis that wrist-worn actimeters can quantify the severity of poststroke apathy. The authors studied 57 patients admitted to an acute rehabilitation unit for ischemic or hemorrhagic stroke. After accounting for motor deficit of the affected arm and accounting for age, each increment of the Apathy Inventory score correlated with 5.6 fewer minutes of moving per hour. The overall statistical model had an R(2) of only 0.34, suggesting unexplained factors for total movement time. Wrist-worn actimeters may serve as an objective, quantifiable measure of poststroke apathy in patients with an intact upper extremity but cannot be used alone to diagnose apathy.