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An evaluation of a 3D multimodal marker-less motion analysis system

Rodrigues, Thiago Braga orcid logoORCID: 0000-0002-2017-4492, Ó Catháin, Ciarán orcid logoORCID: 0000-0002-8526-8924, Devine, Declan orcid logoORCID: 0000-0002-1364-5583, Moran, Kieran orcid logoORCID: 0000-0003-2015-8967, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Murray, Niall orcid logoORCID: 0000-0002-5919-0596 (2019) An evaluation of a 3D multimodal marker-less motion analysis system. In: ACM Multimedia Systems Conference 2019 (MMSys’19), 18 - 21 June, 2019, Amherst, MA, USA. ISBN 978-1-4503-6297-9

Abstract
Motion analysis is a technique used by clinicians (among many others) that quantifies human movement by using camera-based systems. Marker-based motion analysis systems have been used across a variety of application domains, from Interactive 3D TeleImmersion (i3DTI) environments to the diagnosis of neuromuscular and musculoskeletal diseases. Although such analysis is performed in several laboratories in many countries, numerous issues exist: (1) the high cost of precise motion capture systems; (2) scarcity of qualified personnel to operate them; (3) expertise required to interpret their results; (4) space requirements to install and store these systems; (5) complexity in terms of measurement protocol required for such systems; (6) limited availability; (7) and in some situations the use of markers means they are unsuitability for certain clinical use cases (e.g. for patients recovering from orthopaedic surgery). In this paper, we present, from a system perspective, an alternative, cheaper, and more accessible system for motion analysis. The ultimate aim is to use the output of this multimodal marker-less system as part of an immersive multimedia gait re-education tool. In real-time, it will advise the user on their gait performance (as well as potentially providing accurate clinical data to clinicians). With the initial focus on the capture system, we have developed and evaluated a novel multimodal system which integrates Multiple Microsoft Kinects (which employ RGB-D cameras) with multiple Shimmer Inertial Measurement Unit (IMU) sensors. We have compared this system with the VICON system (the gold standard in motion capture). Our marker-less motion capture system combines data from 4 skeletons generating 3D and complete 360 degrees in view skeleton. The system combines unit quaternions from each Kinect joint with quaternions from 4 inertial measurement units to promote integration. We used our system to measure 3D points of 12 joints from the Kinect fused skeleton and flexion-extension angles of the knee and hip in a walking trial in 8 participants with 8-10 trials per participant. The analysis found component similarity of 0.97 for knee angles and 0.98 for hip angles. These results show that our system, through combination of Multi Kinect system and Shimmer IMUs, offers a cheaper, sufficiently accurate and more accessible human motion analysis system
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Motion capture; marker-less, inertial sensors; 3D Model; Multimodal data fusion; GAIT Re-education; Immersive Multimedia
Subjects:Computer Science > Interactive computer systems
Computer Science > Multimedia systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings of ACM Multimedia System Conference (MMSys’19). . ACM. ISBN 978-1-4503-6297-9
Publisher:ACM
Official URL:http://dx.doi.org/10.1145/3304109.3306236
Copyright Information:© 2019 Association for Computing Machinery
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland grant number SFI/12/RC/2289 and SFI/12/RC/3918., Irish Research Council under grant GOIPG/2017/803
ID Code:22993
Deposited On:20 Feb 2019 09:50 by Noel Edward O'connor . Last Modified 19 Oct 2020 12:19
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