Inertial sensor based and shoe size independent gait analysis including heel and toe clearance estimation

Abstract

Falls are a major cause for morbidity and mortality in the ageing society. Inertial sensor based gait assessment including the analysis of the heel and toe clearance can be an indicator for the risk of falling. This paper presents a method for calculating the continuous heel and toe clearance without the knowledge of the shoe dimensions as well as the foot angle in the sagittal plane. These gait parameters were validated using an optical motion capture system. 20 healthy subjects from 3 different age groups (young, mid age, old) performed gait trials with different stride lengths and stride velocities. We obtained low mean absolute errors, low standard deviations and high Pearson correlations (0.91-0.99) for all gait parameters. In summary, we implemented a viable algorithm for the calculation of the heel and toe clearance without knowing the shoe dimensions as well as the foot angle in sagittal plane. We conclude that the given method is applicable for a mobile and unobtrusive gait assessment for healthy subjects from all age classes.

Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

“…However, there are studies that have evaluated the validity of IMUs and smart devices to measure joint angles against an optoelectronic system. These studies demonstrate that wearable devices are reliable to measure joint angles, where the error usually ranges between 1 • -6 • [34,[38][39][40][41]58].…”

“…Kanzler et al [39] 2015 Present a method for calculating continuous heel and toe clearance and foot angle in the sagittal plane without knowing shoe dimensions.…”

“…Other researchers mounted IMUs to the thighs and shanks to extract Euler angles and measure knee flexion [25]. Kanzler et al mounted an IMU on a shoe while subjects performed walking exercises to measure ankle joint angles using quaternions [39].…”

Wearable sensors objectively measure gait parameters in Parkinson’s disease

“…Data were streamed wirelessly via Bluetooth ® with a sampling rate of 51.2 Hz. The following gait parameters were extracted from each stride: stride length, stride time, gait velocity, cadence, stance phase time, swing phase time, HS angle, TO angle, gait variation, and foot clearance [ 15 , 19 , 20 ] ( S1 Table and S1B Fig ). Stride length is either given normalized or non-normalized to height [ 25 , 26 ].…”

An Objective Functional Evaluation of Myoelectrically-Controlled Hand Prostheses: A Pilot Study Using the Virtual Peg Insertion Test

“…Therefore, nine metrics (range, standard deviation, and maximum per axis) were extracted per peg from the 3D angular velocity trajectories of the handle to capture abrupt changes in orientation during handle drops. Similarly, a sliding-window (duration of 0.5 s without overlap) and energy-based (i.e., sum of squared values within each window) approach [19] was used to calculate eight metrics (0th, 5th, 10th, and 15th percentile of energy across windows per trajectory) from the grip force and cursor velocity trajectories to capture grip readjustments (i.e., cursor temporarily remaining static without any force applied to handle). The binary classification problems were evaluated in a 10-fold cross-validation using a Random Forest [20].…”