Literature DB >> 11474964

A reliable gait phase detection system.

I P Pappas1, M R Popovic, T Keller, V Dietz, M Morari.   

Abstract

A new highly reliable gait phase detection system, which can be used in gait analysis applications and to control the gait cycle of a neuroprosthesis for walking, is described. The system was designed to detect in real-time the following gait phases: stance, heel-off, swing, and heel-strike. The gait phase detection system employed a gyroscope to measure the angular velocity of the foot and three force sensitive resistors to assess the forces exerted by the foot on the shoe sole during walking. A rule-based detection algorithm, which was running on a portable microprocessor board, processed the sensor signals. In the presented experimental study ten able body subjects and six subjects with impaired gait tested the device in both indoor and outdoor environments (0-25 degrees C). The subjects were asked to walk on flat and irregular surfaces, to step over small obstacles, to walk on inclined surfaces, and to ascend and descend stairs. Despite the significant variation in the individual walking styles the system achieved an overall detection reliability above 99% for both subject groups for the tasks involving walking on flat, irregular, and inclined surfaces. In the case of stair climbing and descending tasks the success rate of the system was above 99% for the able body subjects and above 96 % for the subjects with impaired gait. The experiments also showed that the gait phase detection system, unlike other similar devices, was insensitive to perturbations caused by nonwalking activities such as weight shifting between legs during standing, feet sliding, sitting down, and standing up.

Entities:  

Mesh:

Year:  2001        PMID: 11474964     DOI: 10.1109/7333.928571

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  47 in total

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5.  Bipedal gait model for precise gait recognition and optimal triggering in foot drop stimulator: a proof of concept.

Authors:  Muhammad Faraz Shaikh; Zoran Salcic; Kevin I-Kai Wang; Aiguo Patrick Hu
Journal:  Med Biol Eng Comput       Date:  2018-03-10       Impact factor: 2.602

6.  Quasi real-time gait event detection using shank-attached gyroscopes.

Authors:  Jung Keun Lee; Edward J Park
Journal:  Med Biol Eng Comput       Date:  2011-01-26       Impact factor: 2.602

7.  Command control for functional electrical stimulation hand grasp systems using miniature accelerometers and gyroscopes.

Authors:  K Y Tong; A F T Mak; W Y Ip
Journal:  Med Biol Eng Comput       Date:  2003-11       Impact factor: 2.602

8.  Validity and repeatability of inertial measurement units for measuring gait parameters.

Authors:  Edward P Washabaugh; Tarun Kalyanaraman; Peter G Adamczyk; Edward S Claflin; Chandramouli Krishnan
Journal:  Gait Posture       Date:  2017-04-12       Impact factor: 2.840

9.  Control of triceps surae stimulation based on shank orientation using a uniaxial gyroscope during gait.

Authors:  C C Monaghan; W J B M van Riel; P H Veltink
Journal:  Med Biol Eng Comput       Date:  2009-10-15       Impact factor: 2.602

10.  Activity recognition using a single accelerometer placed at the wrist or ankle.

Authors:  Andrea Mannini; Stephen S Intille; Mary Rosenberger; Angelo M Sabatini; William Haskell
Journal:  Med Sci Sports Exerc       Date:  2013-11       Impact factor: 5.411

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