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Computer Methods in Biomechanics and Biomedical Engineering Volume 15, 2012 - Issue 3
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Original Articles

Inertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts

C. Scheffer Department of Mechanical and Mechatronic Engineering, Stellenbosch University, Private Bag X1, Matieland, 7602, South AfricaCorrespondence[email protected]
&
T. Cloete Department of Mechanical and Mechatronic Engineering, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa
Pages 285-294 | Received 12 Feb 2010, Accepted 25 Sep 2010, Published online: 05 Apr 2011

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Gustavo José Luvizutto, Gabrielly Fernanda Silva, Monalisa Resende Nascimento, Kelly Cristina Sousa Santos, Pablo Andrei Appelt, Eduardo de Moura Neto, Juli Thomaz de Souza, Fernanda Cristina Wincker, Luana Aparecida Miranda, Pedro Tadao Hamamoto Filho, Luciane Aparecida Pascucci Sande de Souza, Rafael Plana Simões, Edison Iglesias de Oliveira Vidal & Rodrigo Bazan. (2022) Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept. Topics in Stroke Rehabilitation 29:5, pages 331-346.
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Christian Mitschke, Falk Zaumseil & Thomas L. Milani. (2017) The influence of inertial sensor sampling frequency on the accuracy of measurement parameters in rearfoot running. Computer Methods in Biomechanics and Biomedical Engineering 20:14, pages 1502-1511.
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Articles from other publishers (11)

Hyeonjong Kim, Ji-Won Kim & Junghyuk Ko. (2023) Adaptive Control Method for Gait Detection and Classification Devices with Inertial Measurement Unit. Sensors 23:14, pages 6638.
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Yaqin Fu, Qi Li & Ding Ma. (2023) User Experience of a Serious Game for Physical Rehabilitation Using Wearable Motion Capture Technology. IEEE Access 11, pages 108407-108417.
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Di Shi, Liduan Wang, Yanqiu Zhang, Wuxiang Zhang, Hang Xiao & Xilun Ding. (2022) Review of human—robot coordination control for rehabilitation based on motor function evaluation. Frontiers of Mechanical Engineering 17:2.
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Marco Iosa, Maria Grazia Benedetti, Gabriella Antonucci, Stefano Paolucci & Giovanni Morone. (2022) Artificial Neural Network Detects Hip Muscle Forces as Determinant for Harmonic Walking in People after Stroke. Sensors 22:4, pages 1374.
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Dhanya Menoth Mohan, Ahsan Habib Khandoker, Sabahat Asim Wasti, Sarah Ismail Ibrahim Ismail Alali, Herbert F. Jelinek & Kinda Khalaf. (2021) Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis. Frontiers in Neurology 12.
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Marco Iosa, Edda Capodaglio, Silvia Pelà, Benedetta Persechino, Giovanni Morone, Gabriella Antonucci, Stefano Paolucci & Monica Panigazzi. (2021) Artificial Neural Network Analyzing Wearable Device Gait Data for Identifying Patients With Stroke Unable to Return to Work. Frontiers in Neurology 12.
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Sanghee Moon, Pedram Ahmadnezhad, Hyun-Je Song, Jeffrey Thompson, Kristof Kipp, Abiodun E. Akinwuntan & Hannes Devos. (2020) Artificial neural networks in neurorehabilitation: A scoping review. NeuroRehabilitation 46:3, pages 259-269.
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Chengkun Cui, Gui-Bin Bian, Zeng-Guang Hou, Jun Zhao, Guodong Su, Hao Zhou, Liang Peng & Weiqun Wang. (2018) Simultaneous Recognition and Assessment of Post-Stroke Hemiparetic Gait by Fusing Kinematic, Kinetic, and Electrophysiological Data. IEEE Transactions on Neural Systems and Rehabilitation Engineering 26:4, pages 856-864.
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Kathrin Tyryshkin, Angela M Coderre, Janice I Glasgow, Troy M Herter, Stephen D Bagg, Sean P Dukelow & Stephen H Scott. (2014) A robotic object hitting task to quantify sensorimotor impairments in participants with stroke. Journal of NeuroEngineering and Rehabilitation 11:1.
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Jakob Wikström, George Georgoulas, Thucydides Moutsopoulos & Aris Seferiadis. (2014) Intelligent data analysis of instrumented gait data in stroke patients—A systematic review. Computers in Biology and Medicine 51, pages 61-72.
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David J. van den Heever, Cornie Scheffer, Pieter Erasmus & Edwin Dillon. (2012) Classification of gender and race in the distal femur using self organising maps. The Knee 19:4, pages 488-492.
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