Literature DB >> 25375679

Gait and balance analysis for patients with Alzheimer's disease using an inertial-sensor-based wearable instrument.

Yu-Liang Hsu, Pau-Choo Julia Chung, Wei-Hsin Wang, Ming-Chyi Pai, Chun-Yao Wang, Chien-Wen Lin, Hao-Li Wu, Jeen-Shing Wang.   

Abstract

Despite patients with Alzheimer's disease (AD) were reported of revealing gait disorders and balance problems, there is still lack of objective quantitative measurement of gait patterns and balance capability of AD patients. Based on an inertial-sensor-based wearable device, this paper develops gait and balance analyzing algorithms to obtain quantitative measurements and explores the essential indicators from the measurements for AD diagnosis. The gait analyzing algorithm is composed of stride detection followed by gait cycle decomposition so that gait parameters are developed from the decomposed gait details. On the other hand, the balance is measured by the sway speed in anterior-posterior (AP) and medial-lateral (ML) directions of the projection path of body's center of mass (COM). These devised gait and balance parameters were explored on twenty-one AD patients and fifty healthy controls (HCs). Special evaluation procedure including single-task and dual-task walking experiments for observing the cognitive function and attention is also devised for the comparison of AD and HC groups. Experimental results show that the wearable instrument with the designed gait and balance analyzing system is a promising tool for automatically analyzing gait information and balance ability, serving as assistant indicators for early diagnosis of AD.

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Year:  2014        PMID: 25375679     DOI: 10.1109/JBHI.2014.2325413

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  23 in total

1.  Mastication Evaluation With Unsupervised Learning: Using an Inertial Sensor-Based System.

Authors:  Caroline Vieira Lucena; Marcelo Lacerda; Rafael Caldas; Fernando Buarque De Lima Neto; Diego Rativa
Journal:  IEEE J Transl Eng Health Med       Date:  2018-04-02       Impact factor: 3.316

Review 2.  Mapping Movement: Applying Motion Measurement Technologies to the Psychiatric Care of Older Adults.

Authors:  Stephanie Collier; Patrick Monette; Katherine Hobbs; Edward Tabasky; Brent P Forester; Ipsit V Vahia
Journal:  Curr Psychiatry Rep       Date:  2018-07-24       Impact factor: 5.285

Review 3.  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

4.  Skin-mountable stretch sensor for wearable health monitoring.

Authors:  Jonathan D Pegan; Jasmine Zhang; Michael Chu; Thao Nguyen; Sun-Jun Park; Akshay Paul; Joshua Kim; Mark Bachman; Michelle Khine
Journal:  Nanoscale       Date:  2016-10-06       Impact factor: 8.307

Review 5.  Wearable Sensors for Remote Health Monitoring.

Authors:  Sumit Majumder; Tapas Mondal; M Jamal Deen
Journal:  Sensors (Basel)       Date:  2017-01-12       Impact factor: 3.576

6.  Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

Authors:  Yu-Liang Hsu; Po-Huan Chou; Hsing-Cheng Chang; Shyan-Lung Lin; Shih-Chin Yang; Heng-Yi Su; Chih-Chien Chang; Yuan-Sheng Cheng; Yu-Chen Kuo
Journal:  Sensors (Basel)       Date:  2017-07-15       Impact factor: 3.576

7.  Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders.

Authors:  Can Tunca; Nezihe Pehlivan; Nağme Ak; Bert Arnrich; Gülüstü Salur; Cem Ersoy
Journal:  Sensors (Basel)       Date:  2017-04-11       Impact factor: 3.576

Review 8.  Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges.

Authors:  Aliénor Vienne; Rémi P Barrois; Stéphane Buffat; Damien Ricard; Pierre-Paul Vidal
Journal:  Front Psychol       Date:  2017-05-18

Review 9.  Fifteen Years of Wireless Sensors for Balance Assessment in Neurological Disorders.

Authors:  Alessandro Zampogna; Ilaria Mileti; Eduardo Palermo; Claudia Celletti; Marco Paoloni; Alessandro Manoni; Ivan Mazzetta; Gloria Dalla Costa; Carlos Pérez-López; Filippo Camerota; Letizia Leocani; Joan Cabestany; Fernanda Irrera; Antonio Suppa
Journal:  Sensors (Basel)       Date:  2020-06-07       Impact factor: 3.576

10.  [Use of machine learning for the prediction of stress using the example of logistics].

Authors:  Hermann Foot; Benedikt Mättig; Michael Fiolka; Tim Grylewicz; Michael Ten Hompel; Veronika Kretschmer
Journal:  Z Arbeitswiss       Date:  2021-07-13
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