Literature DB >> 24110835

Gait phase detection in able-bodied subjects and dementia patients.

Xiaoli Meng, Haoyong Yu, Ming Po Tham.   

Abstract

Accurate detection of gait phases allows identification of specific functional deficits at each phase of the gait cycle for motor function assessment. This paper proposes a robust gait phase detection method to identify the seven gait phases in overground walking for normal and pathologic gaits. Four inertial sensors are used to obtain knee angles, tibia angles and feet angular rate patterns in the sagittal plane. The key events segmenting the gait cycles are searched using an adaptive threshold in adaptive searching intervals to make sure it works well for different subjects with high variation in cadence and step length during walking. The subjects involved in this study are categorized into three groups: five healthy adult subjects, two healthy elderly subjects and two severe dementia patients. The experimental results have shown our method can reliably detect all gait phases for able-bodied subjects and dementia patients without subject-specific calibration.

Entities:  

Mesh:

Year:  2013        PMID: 24110835     DOI: 10.1109/EMBC.2013.6610648

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  5 in total

Review 1.  Gait Partitioning Methods: A Systematic Review.

Authors:  Juri Taborri; Eduardo Palermo; Stefano Rossi; Paolo Cappa
Journal:  Sensors (Basel)       Date:  2016-01-06       Impact factor: 3.576

Review 2.  A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses.

Authors:  Huong Thi Thu Vu; Dianbiao Dong; Hoang-Long Cao; Tom Verstraten; Dirk Lefeber; Bram Vanderborght; Joost Geeroms
Journal:  Sensors (Basel)       Date:  2020-07-17       Impact factor: 3.576

3.  Quantitative gait analysis in mild cognitive impairment, dementia, and cognitively intact individuals: a cross-sectional case-control study.

Authors:  Sunee Bovonsunthonchai; Roongtiwa Vachalathiti; Vimonwan Hiengkaew; Mon S Bryant; Jim Richards; Vorapun Senanarong
Journal:  BMC Geriatr       Date:  2022-09-23       Impact factor: 4.070

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

Authors:  Huong Thi Thu Vu; Felipe Gomez; Pierre Cherelle; Dirk Lefeber; Ann Nowé; Bram Vanderborght
Journal:  Sensors (Basel)       Date:  2018-07-23       Impact factor: 3.576

Review 5.  Advances in neuroprosthetic management of foot drop: a review.

Authors:  Javier Gil-Castillo; Fady Alnajjar; Aikaterini Koutsou; Diego Torricelli; Juan C Moreno
Journal:  J Neuroeng Rehabil       Date:  2020-03-25       Impact factor: 4.262

  5 in total

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