Computational methods to detect step events for normal and pathological gait evaluation using accelerometer

Abstract

The presented study highlights the feasibility and accuracy of novel computational methods based on a morphological filter and a least square acceleration filter to detect step events for evaluating normal and pathological gait parameters using a single accelerometer. This is the first evidence that demonstrates the feasibility and accuracy of the novel accelerometer-based system and methods in both normal and pathological populations.

Gait Event Detection in Real-World Environment for Long-Term Applications: Incorporating Domain Knowledge Into Time-Frequency Analysis

“…However, it is quite likely that external factors might disturb the original configuration during long-term analysis [28], and thus either the axis alignment should be checked and readjusted frequently or the exact orientation of the accelerometer must be known throughout, to compensate for the misalignment of the axes. An alternative is to analyze the magnitude of the resultant accelerometer signal instead which makes it invariant to individual axis alignment, as done in [4], [29]. While some methodologies instruct subjects to walk in a straight line or a given path at a selfselected pace [4], [13], [27], [29], others either pre-define a set of walking speeds or ask the subjects to walk slowly, normal and fast, in order to test the algorithmic robustness to different velocities [3], [14], [24]- [26], [28].…”

“…While some methodologies instruct subjects to walk in a straight line or a given path at a selfselected pace [4], [13], [27], [29], others either pre-define a set of walking speeds or ask the subjects to walk slowly, normal and fast, in order to test the algorithmic robustness to different velocities [3], [14], [24]- [26], [28]. A number of algorithms apply thresholds either to filtered accelerometer signals or use them at some intermediate stage after signal transformation, to perform peak detection for identifying events [25], [28], [29]. The performance of such algorithms is usually dependent on choosing the optimum values of these thresholds and tuning other parameters associated with them.…”

Repeatability of the accelerometric-based method to detect step events for hemiparetic stroke patients

“…3 illustrate the procedure and its results of this computational method. Detailed description is given at [5].…”

“…Especially, some related researches to detect step events were performed [1], [4]. Recently, we have developed novel computational methods to detect step events for gait evaluation using an accelerometer [5]. One of the computational methods is based on both the least squares acceleration filter and the morphological operators.…”

A Review on Accelerometry-Based Gait Analysis and Emerging Clinical Applications

“…The trunk mounted accelerometers have been used to investigate the gait patterns of chronic obstructive pulmonary disease (COPD) patients versus healthy subjects [45], create reference data for normal subjects [54], compare gaits in patients with fibromyalgia with those in controls using Locometrix system [47], assess gait parameters in children [57], investigate Gait variability and regularity of people with transtibial amputation [179], compare the CoM movement within PD patients [71], compare gait impairment of patients with neurological condition (including PD and ataxic patients) with those of healthy subjects [169], compare gait parameters in elderly people suffering from mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients using Locometrix system [59], discriminate hemiplegic gait from gait in the comparison group [170], assess spatiotemporal parameters in amputee gait using Dynaport [64], investigate the difference in gait patterns for dementia patients using Dynaport [172], evaluate gait events for Hemiplegic patients [66], distinguish the step events in normal and pathological populations [175], differentiate between fit and frail elderlies [43], describe the characteristics of stroke patient gait [178], differentiate between two groups of young and elderly [184], differentiate spatio-temporal gait parameters between young and old subjects [55], differentiate between PD patients and healthy subjects [185], differentiate PD and peripheral neuropathy (PN) patients from healthy subjects [196], [199] and validate the estimated stride event versus those by motion capture system [108], investigate the effects of age and gender on gait parameters [3], investigate the effects of age, gender and height on gait parameters [198], estimate gait asymmetry in patients with hemiparetic stroke [188], investigate the relationship between spatio-temporal gait parameters with increasing age [186], monitor gait of the orthopaedic patients with symptomatic gonarthrosis aimed for total knee arthroplasty [50], and investigate the ability to differentiate between functional knee limitations and its suitability for clinical ...…”