Test-retest reliability of wireless inertial-sensor derived measurements of knee joint kinematics.

Advances in sensor technology have provided an opportunity to measure gait characteristics using body-worn inertial measurement units (IMUs). Whilst research investigating the validity of IMUs in reporting gait characteristics is extensive, research investigating the reliability of IMUs is limited. This study aimed to investigate the inter-session reliability of wireless IMU derived measures of gait (i.e., knee angle, range of motion) taking multiple test administrators into account. Fifteen healthy volunteers (43 ± 15 years) completed two visits. Within each visit, participants were required to perform two sets of 6 gait trials (6-metre walk tests). IMUs were placed on the participant in 7 locations on the lower limbs and waist. A different test administrator (n = 3) applied the IMUs at each set. At visit 2, this procedure was repeated with the same test administrators as visit 1. Kinematic measures of maximum angle (Knee_Max), minimum angle (Knee_Min), and range of motion (RoM) are reported for the left and right knee. The intraclass correlation coefficients (ICC), standard error of measurement (SEM) and minimum detectable change (MDC) are reported to determine IMU reliability. The results confirmed moderate to good inter-session reliability across all features (0.73-0.87). SEM values ranged from 1.21-3.32° and MDC values ranged from 3.37 - 9.21°. Therefore, IMUs appear to be a reliable method to determine inter-session gait characteristics across multiple test administrators.

The Feasibility of Equine Field-Based Postural Sway Analysis Using a Single Inertial Sensor.

(1) Background: Postural sway is frequently used to quantify human postural control, balance, injury, and neurological deficits. However, there is considerably less research investigating the value of the metric in horses. Much of the existing equine postural sway research uses force or pressure plates to examine the centre of pressure, inferring change at the centre of mass (COM). This study looks at the inverse, using an inertial measurement unit (IMU) on the withers to investigate change at the COM, exploring the potential of postural sway evaluation in the applied domain. (2) Methods: The lipopolysaccharide model was used to induce transient bilateral lameness in seven equines. Horses were monitored intermittently by a withers fixed IMU over seven days. (3) Results: There was a significant effect of time on total protein, carpal circumference, and white blood cell count in the horses, indicating the presence of, and recovery from, inflammation. There was a greater amplitude of displacement in the craniocaudal (CC) versus the mediolateral (ML) direction. A significant difference was observed in the amplitude of displacement in the ML direction between 4-12 h and 168 h. (4) Conclusions: The significant reduction in ML displacement during the acute inflammation period alongside greater overall CC displacement may be a compensatory behaviour for bilateral lameness.

Adaptive gait segmentation algorithm for walking bout detection using tri-axial accelerometers.

Gait analysis has many potential applications in understanding the activity profiles of individuals in their daily lives, particularly when studying the progression of recovery following injury, or motor deterioration in pathological conditions. One of the many challenges of conducting such analyses in the home environment is the correct and automatic identification of bouts of gait activity. To address this, a novel method for determining bouts of gait from accelerometer data recorded from the shank is presented. This method is fully automated and includes an adaptive thresholding approach which avoids the necessity for identifying subject-specific thresholds. The algorithm was tested on data recorded from 15 healthy subjects during self-selected slow, normal and fast walking speeds ranging from 0.48 ± 0.19 to 1.38 ± 0.33m/s and a single subject with PD walking at their normal walking speed (1.41 ± 0.08m/s) using accelerometers on the shanks. Intra-Class Correlation (ICC) confirmed high levels of agreement between bout onset/offset times and durations estimated using the algorithm, experimentally recorded stopwatch times and manual annotation for the healthy subjects (r=0.975, p <; 0.001; r=0.984, p<; 0.001) and moderate agreement for the PD subject (r=0.663, p<; 0.001). Mean absolute errors between accelerometer-derived and manually-annotated times were calculated, and ranged from 0.91 ± 0.05 s to 1.17 ± 2.26 s for bout onset detection, 0.80 ± 0.23 s to 2.41 ± 3.77 s for offset detection and 1.27 ± 0.13 s to 3.67 ± 4.59 s for bout durations.

Regression-based analysis of front crawl swimming using upper-arm mounted accelerometers.

Wearable accelerometers can be used to quantify movement during swimming, enabling objective performance analysis. This study examined arm acceleration during front crawl swimming, and investigated how accelerometer-derived features change with lap times. Thirteen participants swam eight 50m laps using front crawl with a tri-axial accelerometer attached to each upper arm. Data were segmented into individual laps; lap times estimated and individual strokes extracted. Stroke times, root mean squared (RMS) acceleration, RMS jerk and spectral edge frequencies (SEF) were calculated for each stroke. Movement symmetry was assessed as the ratio of the minimum to maximum feature value for left and right arms. A regularized multivariate regression model was developed to estimate lap time using a subset of the accelerometer-derived features. Mean lap time was 56.99±11.99s. Fifteen of the 42 derived features were significantly correlated with lap time. The regression model included 5 features (stroke count, mean SEF of the X and Z axes, stroke count symmetry, and the coefficient of variation of stroke time symmetry) and estimated 50m lap time with a correlation coefficient of 0.86, and a cross-validated RMS error of 6.38s. The accelerometer-derived features and developed regression model may provide a useful tool to quantitatively evaluate swimming performance.