Real-time gait event detection for lower limb amputees using a single wearable sensor

Abstract

ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Overall, detection accuracy was 99.78% for all the events in both groups. Based on the validated results, the proposed system can be used to accurately detect the temporal gait events in real-time that leads to the detection of gait phase system and therefore, can be utilized in gait analysis applications and the control of lower limb prostheses.

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

“…One of the most well known application is to differentiate the gait characteristics of the healthy normal subjects and those of people with walking impairments. This includes patients with pathological gait disorders such as those caused by cerebral palsy [130] [122], spinal-cord injuries [112], transtibial amputation [88], lower limb amputation [61][106] [108], hemiplegic gaits [124][103] [126], hip dysplasia [102], Parkinson's disease (PD) [63][69], geriatric disorder [120], osteoarthritis [132] and orthopaedic [100]. It is also applied to monitor rehabilitation of patients following anterior cruciate ligament and lower limb reconstruction [98], [99].…”

“…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 ...…”

ED-FNN: A New Deep Learning Algorithm to Detect Percentage of the Gait Cycle for Powered Prostheses

“…Although they state that their algorithm also works in an on-line setting, they do not show any evidence that supports their results. In summary, in the course of our literature review, we encountered gait event detection systems that limit their experiments to one IMU and to gait phases that were partitioned between two [ 9 , 41 , 42 ] and three phases [ 3 ]. In this limited framework, they illustrated that a high accuracy can be achieved.…”

“…In this limited framework, they illustrated that a high accuracy can be achieved. For instance, authors in [ 41 , 42 ] illustrated an accuracy of 100% in IC and TO event detection. Zhou et al [ 9 ] showed an accuracy of above 98% for IC event detection and 95% for TO event detection on three different terrains.…”

Heuristic Real-Time Detection of Temporal Gait Events for Lower Limb Amputees

“…There are very few studies that focused on the gait events of the inner-stance phase [6,10,11,20,21]. A preliminary work related to the detection of temporal gait events has already been carried out in our previous work [22,23].…”