The University of Southampton
University of Southampton Institutional Repository

Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors

Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors
Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors
This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close (±6%) to the average value, for different task durations further attesting to the algorithms robustness.
2168-2194
1-12
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Cranny, Andy
2ebc2ccb-7d3e-4a6a-91ac-9f089741939e
Rajan, Amal
43a9a34d-9ba8-4715-b678-4118a83005fd
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Achner, Josy
1eb12fc1-6d4e-41e6-8e2e-6b7f4fec7daf
Klemke, Jasmin
0c095460-4faf-4d56-bfcc-c04a26e70249
Jobges, Michael
56f78929-cd51-4837-930d-57efaee6d6ef
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321
Langendorfer, Peter
e2deee34-aa04-46fd-b7f8-3b87a252225e
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Biswas, Dwaipayan
76983b74-d729-4aae-94c3-94d05e9b2ed4
Cranny, Andy
2ebc2ccb-7d3e-4a6a-91ac-9f089741939e
Rajan, Amal
43a9a34d-9ba8-4715-b678-4118a83005fd
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Achner, Josy
1eb12fc1-6d4e-41e6-8e2e-6b7f4fec7daf
Klemke, Jasmin
0c095460-4faf-4d56-bfcc-c04a26e70249
Jobges, Michael
56f78929-cd51-4837-930d-57efaee6d6ef
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321
Langendorfer, Peter
e2deee34-aa04-46fd-b7f8-3b87a252225e

Mazomenos, Evangelos B., Biswas, Dwaipayan, Cranny, Andy, Rajan, Amal, Maharatna, Koushik, Achner, Josy, Klemke, Jasmin, Jobges, Michael, Ortmann, Steffen and Langendorfer, Peter (2015) Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors. IEEE Journal of Biomedical and Health Informatics, 1-12. (doi:10.1109/JBHI.2015.2431472).

Record type: Article

Abstract

This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close (±6%) to the average value, for different task durations further attesting to the algorithms robustness.

Text
JBHI-00554-2014.pdf - Other
Download (1MB)

More information

Published date: 8 May 2015
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 377734
URI: https://eprints.soton.ac.uk/id/eprint/377734
ISSN: 2168-2194
PURE UUID: eaf8873d-0f2b-4dd2-87d7-af3833b90fe7

Catalogue record

Date deposited: 16 Jun 2015 13:15
Last modified: 16 Sep 2019 18:27

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×