Literature DB >> 26163348

Comparative assessment of different methods for the estimation of gait temporal parameters using a single inertial sensor: application to elderly, post-stroke, Parkinson's disease and Huntington's disease subjects.

Diana Trojaniello1, Andrea Ravaschio2, Jeffrey M Hausdorff3, Andrea Cereatti4.   

Abstract

The estimation of gait temporal parameters with inertial measurement units (IMU) is a research topic of interest in clinical gait analysis. Several methods, based on the use of a single IMU mounted at waist level, have been proposed for the estimate of these parameters showing satisfactory performance when applied to the gait of healthy subjects. However, the above mentioned methods were developed and validated on healthy subjects and their applicability in pathological gait conditions was not systematically explored. We tested the three best performing methods found in a previous comparative study on data acquired from 10 older adults, 10 hemiparetic, 10 Parkinson's disease and 10 Huntington's disease subjects. An instrumented gait mat was used as gold standard. When pathological populations were analyzed, missed or extra events were found for all methods and a global decrease of their performance was observed to different extents depending on the specific group analyzed. The results revealed that none of the tested methods outperformed the others in terms of accuracy of the gait parameters determination for all the populations except the Parkinson's disease subjects group for which one of the methods performed better than others. The hemiparetic subjects group was the most critical group to analyze (stride duration errors between 4-5 % and step duration errors between 8-13 % of the actual values across methods). Only one method provides estimates of the stance and swing durations which however should be interpreted with caution in pathological populations (stance duration errors between 6-14 %, swing duration errors between 10-32 % of the actual values across populations).
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Accelerometry; Gait events; Inertial sensor; Temporal parameters; Trunk

Mesh:

Year:  2015        PMID: 26163348     DOI: 10.1016/j.gaitpost.2015.06.008

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  29 in total

1.  Gait event detection using inertial measurement units in people with transfemoral amputation: a comparative study.

Authors:  Emeline Simonetti; Coralie Villa; Joseph Bascou; Giuseppe Vannozzi; Elena Bergamini; Hélène Pillet
Journal:  Med Biol Eng Comput       Date:  2019-12-23       Impact factor: 2.602

Review 2.  Gait metrics analysis utilizing single-point inertial measurement units: a systematic review.

Authors:  Ralph Jasper Mobbs; Jordan Perring; Suresh Mahendra Raj; Monish Maharaj; Nicole Kah Mun Yoong; Luke Wicent Sy; Rannulu Dineth Fonseka; Pragadesh Natarajan; Wen Jie Choy
Journal:  Mhealth       Date:  2022-01-20

3.  Objectifying clinical gait assessment: using a single-point wearable sensor to quantify the spatiotemporal gait metrics of people with lumbar spinal stenosis.

Authors:  Callum Betteridge; Ralph J Mobbs; R Dineth Fonseka; Pragadesh Natarajan; Daniel Ho; Wen Jie Choy; Luke W Sy; Nina Pell
Journal:  J Spine Surg       Date:  2021-09

Review 4.  How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.

Authors:  Erika Rovini; Carlo Maremmani; Filippo Cavallo
Journal:  Front Neurosci       Date:  2017-10-06       Impact factor: 4.677

5.  Comprehensive measurement of stroke gait characteristics with a single accelerometer in the laboratory and community: a feasibility, validity and reliability study.

Authors:  Sarah A Moore; Aodhan Hickey; Sue Lord; Silvia Del Din; Alan Godfrey; Lynn Rochester
Journal:  J Neuroeng Rehabil       Date:  2017-12-29       Impact factor: 4.262

6.  An Acceleration-Based Gait Assessment Method for Children with Cerebral Palsy.

Authors:  Xiang Chen; Songmei Liao; Shuai Cao; Xu Zhang
Journal:  Sensors (Basel)       Date:  2017-05-02       Impact factor: 3.576

7.  Is the Assessment of 5 Meters of Gait with a Single Body-Fixed-Sensor Enough to Recognize Idiopathic Parkinson's Disease-Associated Gait?

Authors:  M E Micó-Amigo; I Kingma; G S Faber; A Kunikoshi; J M T van Uem; R C van Lummel; W Maetzler; J H van Dieën
Journal:  Ann Biomed Eng       Date:  2017-01-20       Impact factor: 3.934

8.  Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson's disease using multiple inertial sensors.

Authors:  Hung Nguyen; Karina Lebel; Patrick Boissy; Sarah Bogard; Etienne Goubault; Christian Duval
Journal:  J Neuroeng Rehabil       Date:  2017-04-07       Impact factor: 4.262

9.  Multimodal Wearable Sensors to Measure Gait and Voice.

Authors:  Dimitrios Psaltos; Kara Chappie; Fikret Isik Karahanoglu; Rachel Chasse; Charmaine Demanuele; Amey Kelekar; Hao Zhang; Vanessa Marquez; Tairmae Kangarloo; Shyamal Patel; Matthew Czech; David Caouette; Xuemei Cai
Journal:  Digit Biomark       Date:  2019-10-29

10.  Wearable sensors objectively measure gait parameters in Parkinson's disease.

Authors:  Johannes C M Schlachetzki; Jens Barth; Franz Marxreiter; Julia Gossler; Zacharias Kohl; Samuel Reinfelder; Heiko Gassner; Kamiar Aminian; Bjoern M Eskofier; Jürgen Winkler; Jochen Klucken
Journal:  PLoS One       Date:  2017-10-11       Impact factor: 3.240

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