Comparison of kinematic and dynamic sensor modalities and derived features for human motion segmentation

Jonathan Feng Shun Lin, Vincent Bonnet, Vladimir Joukov, Gentiane Venture, Dana Kulic

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


Human motion segmentation aims to extract individual motion repetitions from a continuous stream of data, typically using a single sensor modality. However, with the numerous sensor modalities available for motion measurement, it can be difficult to determine which modality is the most suitable. This paper investigates how segmentation accuracy is affected by the choice of sensing modality. Motion capture joint position, kinematic, force plate ground reaction force, centre of pressure, and joint torque features were considered, and their segmentation accuracy compared using classifier-based segmentation. It was found that joint position, joint angle, and ground reaction force produced similar accuracy values at 96%. These results suggest that raw motion capture and force plate sensor data can provide comparable accuracy to joint angles, reducing the need for computationally expensive inverse kinematic/dynamic computation and difficult parameter estimation.

Original languageEnglish
Title of host publication2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT 2016)
EditorsKi Chon, Emilio Sacristan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781509011667
ISBN (Print)9781509011674
Publication statusPublished - 2016
Externally publishedYes
EventIEEE Healthcare Innovation Point-of-Care Technologies Conference 2016 - Cancun, Mexico
Duration: 9 Nov 201611 Nov 2016


ConferenceIEEE Healthcare Innovation Point-of-Care Technologies Conference 2016
Abbreviated titleHI-POCT 2016

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