Quantitative characterization of walking on sand inecological conditions: Speed, temporal segmentation, and variability.

BACKGROUND: Walking on compliant surfaces, on sand in particular, is now recommended for training in both elderlies and injured subjects/individuals, allowing to perform high intensity exercises (i.e. augmented energy expenditure) in safe conditions (i.e. minimizing the impact on the joints and the risk of fall). Nevertheless, despite the assessment of energetics of walking on sand, the quantitative biomechanical characterization of walking on sand in ecological conditions is largely lacking. RESEARCH QUESTION: Which is the effect of sand surface on gait speed, gait temporal segmentation and their variability as related to surface compliance in ecological condition? METHODS: Eighteen healthy adults were assessed while walking on solid ground, dry-, and wet sand in ecological conditions by means of wearable inertial sensors (Miniwave, Cometa s.r.l., Italy). The best performing algorithm for the segmentation of walking on sand was selected among 17 algorithms designed for solid ground. Gait timing (i.e. speed, temporal segmentation, variability) was analysed, for the first time, with respect to sand compliance, and compared to walking on solid ground. RESULTS: Self-selected speed on a 60 m distance increased when walking on sand with respect to solid ground (Median 1.02 m/s), with the highest speed on wet sand (Median 1.15 m/s). A stabilizing strategy on the uneven surface provided by sand was highlighted by i) increased stance and double support durations with respect to speed on wet sand, and ii) increased short-term variability of stride, corresponding to continual adjustments of the lower limbs due to shifting surface provided by sand. SIGNIFICANCE: This study represents the first step in the objective characterization of walking on compliant surfaces as sand, necessary for the definition of training and rehabilitative programs.

Timing estimation for gait in water from inertial sensor measurements: Analysis of the performance of 17 algorithms.

BACKGROUND AND OBJECTIVES: Walking in water is used for rehabilitation in different pathological conditions. For the characterization of gait alterations related to pathology, gait timing assessment is of primary importance. With the widespread use of inertial sensors, several algorithms have been proposed for gait timing estimation (i.e. gait events and temporal parameters) out of the water, while an assessment of their performance for walking in water is still missing. The purpose of the present study was to assess the performance in the temporal segmentation for gait in water of 17 algorithms proposed in the literature. METHODS: Ten healthy volunteers mounting 5 tri-axial inertial sensors (trunk, shanks and feet) walked on dry land and in water. Seventeen different algorithms were implemented and classified based on: 1) sensor position, 2) target variable, and 3) computational approach. Gait events identified from synchronized video recordings were assumed as reference. Temporal parameters were calculated from gait events. Algorithm performance was analysed in terms of sensitivity, positive predictive value, accuracy, and repeatability. RESULTS: For walking in water, all Trunk-based algorithms provided a sensitivity lower than 81% and a positive predictive value lower than 94%, as well as acceleration-based algorithms, independently from sensor location, with the exception of two Shank-based ones. Drop in algorithm sensitivity and positive predictive value was associated to significant differences in the stride pattern of the specific analysed variables during walking in water as compared to walking on dry land, as shown by the intraclass correlation coefficient. When using Shank- or Foot-based algorithms, gait events resulted delayed, but the delay was compensated in the estimate of Stride and Step time; a general underestimation of Stance- and overestimation of Swing-time was observed, with minor exceptions. CONCLUSION: Sensor position, target variable and computational approach determined different error distributions for different gait events and temporal parameters for walking in water. This work supports an evidence-based selection of the most appropriate algorithm for gait timing estimation for walking in water as related to the specific application, and provides relevant information for the design of new algorithms for the specific motor task.

Analysis of the performance of 17 algorithms from a systematic review: Influence of sensor position, analysed variable and computational approach in gait timing estimation from IMU measurements.

BACKGROUND: The quantification of gait temporal parameters (i.e. step time, stance time) is crucial in human motion analysis and requires the accurate identification of gait events (i.e. heel strike, toe off). With the widespread use of inertial wearable sensors, many algorithms were proposed and applied for the purpose. Nevertheless, only few studies addressed the assessment of the actual performance of these algorithms, rather considering each proposed algorithm as a whole. RESEARCH QUESTION: How different implementation characteristics influence the assessment of gait events and temporal parameters from inertial sensor measures in terms of accuracy and repeatability? METHODS: Seventeen different algorithms were identified from a systematic review and classified based on: 1) sensor position, 2) target variable, 3) computational approach. The influence of these characteristics was analysed on walking data of 35 healthy volunteers mounting 5 tri-axial inertial sensors. Foot contact events identified by 2 force platforms were assumed as gold standard. Temporal parameters were calculated from gait events. Algorithm performance was analysed in terms of accuracy (error median value) and repeatability (error 25th and 75th percentile values). RESULTS: Shank- and foot-based algorithms performed better (in terms of accuracy and repeatability) in gait events detection and stance time estimation than lower trunk-based ones, while sensor position did not affect step estimate, given the error bias characteristics. Angular velocity-based algorithms performed significantly better than acceleration-based ones for toe off detection in terms of repeatability (68 ms and 102 ms, 25th-75th percentile error range, respectively) and, for heel strike detection, showed better repeatability (40 ms and 111 ms) and comparable accuracy (65 ms and 60 ms median error, respectively) than acceleration-based ones. The performance of different computational approaches varied depending on sensor positioning. SIGNIFICANCE: Present results support the selection of the proper algorithm for the estimation of gait events and temporal parameters in relation to the specific application.