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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
1088-1099
Mazomenos, Evangelos B.
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Biswas, Dwaipayan
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Cranny, Andy
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Rajan, Amal
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Maharatna, Koushik
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Achner, Josy
1eb12fc1-6d4e-41e6-8e2e-6b7f4fec7daf
Klemke, Jasmin
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Jobges, Michael
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Ortmann, Steffen
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Langendorfer, Peter
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Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Biswas, Dwaipayan
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Cranny, Andy
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Rajan, Amal
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Maharatna, Koushik
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Achner, Josy
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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 (2016) Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors. IEEE Journal of Biomedical and Health Informatics, 20 (4), 1088-1099. (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.

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Accepted/In Press date: 28 April 2015
e-pub ahead of print date: 8 May 2015
Published date: 6 July 2016
Organisations: Electronic & Software Systems

Identifiers

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

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Date deposited: 16 Jun 2015 13:15
Last modified: 14 Mar 2024 20:08

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Contributors

Author: Evangelos B. Mazomenos
Author: Dwaipayan Biswas
Author: Andy Cranny
Author: Amal Rajan
Author: Koushik Maharatna
Author: Josy Achner
Author: Jasmin Klemke
Author: Michael Jobges
Author: Steffen Ortmann
Author: Peter Langendorfer

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