Rodrigues, Thiago Braga ORCID: 0000-0002-2017-4492, Salgado, Debora Pereira, Ó Catháin, Ciarán ORCID: 0000-0002-8526-8924, O'Connor, Noel E. ORCID: 0000-0002-4033-9135 and Murray, Niall ORCID: 0000-0002-5919-0596 (2019) Human gait assessment using a 3D marker-less multimodal motion capture system. Multimedia Tools and Applications, 79 (3-4). pp. 2629-2651. ISSN 1380-7501
Abstract
Gait analysis is the measurement, processing and systematic interpretation of biomechanical parameters that characterize human locomotion. It supports the identification of movement limitations and development of rehabilitation procedures. Accurate Gait analysis is important in sports analysis, medical field, and rehabilitation. Although Gait analysis is performed in several laboratories in many countries, there are many issues such as: (i) the high cost of precise Motion Capture systems; (ii) the scarcity of qualified personnel to operate them; (iii) expertise required to interpret their results; (iv) space requirements to install and store these systems; as well as difficulties related to the measurement protocols of each system; (vi) limited availability (vii) and the use of markers can be a barrier for some clinical use cases (e.g. patients recovering from orthopedics surgeries). In this work, we present a low cost and more accessible system based on the integration of a Multiple Microsoft Kinect sensors and multiple Shimmer inertial sensors to capture human Gait. The novel multimodal system combines data from inertial and 3D depth cameras and outputs spatiotemporal Gait variables. A comparison of this system with the VICON system (the gold standard in Motion Capture) was performed. Our relatively low-cost marker-less multimodal motion generates a complete 360-degree skeleton view. We compare our system with the VICON via gait spatiotemporal variables: Gait cycle time, stride time, Gait length (distance between two strides), stride length, and velocity. The system was also evaluated with knee and hip joint angles measurement accuracy. The results show high correlation for spatiotemporal variables and joint angles inside the 95% bootstrap prediction when compared with VICON.
Metadata
Item Type: | Article (Published) |
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Refereed: | Yes |
Uncontrolled Keywords: | 3D model; Gait analysis; Motion Capture; Multimodal sensors |
Subjects: | Computer Science > Image processing Computer Science > Interactive computer systems Engineering > Signal processing |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering Research Initiatives and Centres > INSIGHT Centre for Data Analytics |
Publisher: | Springer |
Official URL: | http://dx.doi.org/10.1007/s11042-019-08275-9 |
Copyright Information: | © 2019 Springer |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | Irish Research Council under grant GOIPG/2017/803, Science Foundation Ireland (SFI) under grant number SFI/12/RC/2289. |
ID Code: | 23768 |
Deposited On: | 29 Nov 2019 14:14 by Noel Edward O'connor . Last Modified 14 Mar 2023 13:51 |
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