Development, Validity, and Reliability of a Novel Walking Speed Measurement Device: the GaitBox.

BACKGROUND: Gait speed is an important measure of health status for older adults and individuals with neurological conditions. Literature reports that measurements made by people are not as accurate as automatic timers. RESEARCH QUESTION: Is the GaitBox (GB), a device to measure walking speed (WS) automatically and accurately, a valid approach to walking speed measurement in a clinical setting? METHODS: Two prospective validation studies were completed comparing the GB to human timers (HT) and the Sprint Timing System (STS). Subjects were recruited from convenience samples of healthy older adults (S1, N = 35, 72.4 + 7.4 years of age) and individuals with Spinal Cord Injury (SCI), Traumatic Brain Injury (TBI), or unknown / no diagnosis (S2, N = 44, 35.3 + 13.5 years of age). Subjects completed 4 timed walks. The GB, HT, and STS simultaneously measured WS across a 4 m or 10 m course. Protocol followed an adapted version of the NIH Walk Test. Subjects were instructed to walk at a normal pace. Validity and reliability were determined using Pearson correlations, absolute mean differences, Intraclass Correlation Coefficients (ICC's) and Bland-Altman plots. RESULTS: WS measured in both studies demonstrated strong correlations between GB and STS (r = 0.98-0.99, p < 0.0001), excellent test-retest reliability GB ICC's (0.93-0.94), no systematic bias, and good precision. In S1 and S2, ICC's between GB and STS were excellent at 0.91 and 0.93, respectively. SIGNIFICANCE: Considering the increased use of WS as a clinically relevant measure of mobility, functional decline, and recovery, accurate measurement of WS are important. These studies show the GB is a valid and reliable measurement tool within various populations completing the 4 m and 10 m walk tests at a usual speed. Additional populations and walking distances should be evaluated further. Due to its accuracy, the GaitBox is a valid alternative to HT in the clinic setting.

Accelerometer-Based Machine Learning Categorization of Body Position in Adult Populations.

This manuscript describes tests and results of a study to evaluate classification algorithms derived from accelerometer data collected on healthy adults and older adults to better classify posture movements. Specifically, tests were conducted to 1) compare performance of 1 sensor vs. 2 sensors; 2) examine custom trained algorithms to classify for a given task 3) determine overall classifier accuracy for healthy adults under 55 and older adults (55 or older). Despite the current variety of commercially available platforms, sensors, and analysis software, many do not provide the data granularity needed to characterize all stages of movement. Additionally, some clinicians have expressed concerns regarding validity of analysis on specialized populations, such as hospitalized older adults. Accurate classification of movement data is important in a clinical setting as more hospital systems are using sensors to help with clinical decision making. We developed custom software and classification algorithms to identify laying, reclining, sitting, standing, and walking. Our algorithm accuracy is 93.2% for healthy adults under 55 and 95% for healthy older adults over 55 for the tasks in our setting. The high accuracy of this approach will aid future investigation into classifying movement in hospitalized older adults. Results from these tests also indicate that researchers and clinicians need to be aware of sensor body position in relation to where the algorithm used was trained. Additionally, results suggest more research is needed to determine if algorithms trained on one population can accurately be used to classify data from another population.

Development of a Novel Gait Analysis Tool Measuring Center of Pressure for Evaluation of Canine Chronic Thoracolumbar Spinal Cord Injury.

Gait evaluation after spinal cord injury (SCI) is an important component of determining functional status. Analysis of center of pressure (COP) provides a dynamic reflection of global locomotion and postural control and has been used to quantify various gait abnormalities. We hypothesized that COP variability would be greater for SCI versus normal dogs and that COP would be able to differentiate varying injury severity. Our objective was to investigate COP, COP variability, and body weight support percentage in dogs with chronic SCI. Eleven chronically non-ambulatory dogs after acute severe thoracolumbar SCI were enrolled. COP measurements in x (right-to-left, COPx) and y (craniocaudal, COPy) directions were captured while dogs walked on a pressure-sensitive treadmill with pelvic limb sling support. Root mean square values (RMS_COPx and RMS_COPy) were calculated to assess variability in COP. Body weight support percentage was measured using a load cell. Gait also was quantified using an open field scale (OFS) and treadmill-based stepping and coordination scores (SS, RI). Mean COPx, COPy, RMS_COPx, and RMS_COPy were compared between dogs with SCI and previously evaluated healthy controls. RMS measurements and support percentage were compared with standard gait scales (OFS, SS, RI). Mean COPy was more cranial and RMS_COPx and RMS_COPy were greater in SCI versus normal dogs (p < 0.001). Support percentage moderately correlated with SS (p = 0.019; R2 = 0.47). COP analysis and body weight support measurements offer information about post-injury locomotion. Further development is needed before consideration as an outcome measure to complement validated gait analysis methods in dogs with SCI.