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CORDIC framework for quaternion-based joint angle computation to classify arm movements

CORDIC framework for quaternion-based joint angle computation to classify arm movements
CORDIC framework for quaternion-based joint angle computation to classify arm movements

We present a novel architecture for arm movement classification based on kinematic properties (joint angle and position), computed from MARG sensors, using a quaternion-based gradient-descent method and a 2-link model of the upper limb. The design based on Coordinate Rotation Digital Computer framework was validated on stroke survivors and healthy subjects performing three elementary arm movements (reach and retrieve, lift arm, rotate arm), involved in 'making-a-cup-of-tea' an archetypal daily activity, achieved an overall accuracy of 78% and 85% respectively. The design coded in System Verilog, was synthesized using STMicroelectronics 130 nm technology, occupies 340K NAND2 equivalent area and consumes 292 nW @ 150 Hz, besides being functionally verified up to 25 MHz making it suitable for real-time high speed operations. The orientation, arm position and the joint angle, are computed on-the-fly, with the classification performed at the end of movement duration.

activity recognition, classification, CORDIC, MARG sensor, quaternion
IEEE
Biswas, Dwaipayan
314a210f-c293-4d18-8b07-ddaaf57a1707
Ye, Zixuan
b5486adf-e6fb-4ad7-a648-fc1f997e80c7
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Jöbges, Michael
c249b79d-ca43-46ce-86cb-a97b33a0c3d8
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Biswas, Dwaipayan
314a210f-c293-4d18-8b07-ddaaf57a1707
Ye, Zixuan
b5486adf-e6fb-4ad7-a648-fc1f997e80c7
Mazomenos, Evangelos B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Jöbges, Michael
c249b79d-ca43-46ce-86cb-a97b33a0c3d8
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Biswas, Dwaipayan, Ye, Zixuan, Mazomenos, Evangelos B., Jöbges, Michael and Maharatna, Koushik (2018) CORDIC framework for quaternion-based joint angle computation to classify arm movements. In 2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Proceedings. vol. 2018-May, IEEE. 5 pp . (doi:10.1109/ISCAS.2018.8350967).

Record type: Conference or Workshop Item (Paper)

Abstract

We present a novel architecture for arm movement classification based on kinematic properties (joint angle and position), computed from MARG sensors, using a quaternion-based gradient-descent method and a 2-link model of the upper limb. The design based on Coordinate Rotation Digital Computer framework was validated on stroke survivors and healthy subjects performing three elementary arm movements (reach and retrieve, lift arm, rotate arm), involved in 'making-a-cup-of-tea' an archetypal daily activity, achieved an overall accuracy of 78% and 85% respectively. The design coded in System Verilog, was synthesized using STMicroelectronics 130 nm technology, occupies 340K NAND2 equivalent area and consumes 292 nW @ 150 Hz, besides being functionally verified up to 25 MHz making it suitable for real-time high speed operations. The orientation, arm position and the joint angle, are computed on-the-fly, with the classification performed at the end of movement duration.

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More information

Published date: 26 April 2018
Venue - Dates: 2018 IEEE International Symposium on Circuits and Systems, Florence, Italy, 2018-05-27 - 2018-05-30
Keywords: activity recognition, classification, CORDIC, MARG sensor, quaternion

Identifiers

Local EPrints ID: 426765
URI: https://eprints.soton.ac.uk/id/eprint/426765
PURE UUID: 428488a5-f984-458a-825a-9d3ab089c201

Catalogue record

Date deposited: 12 Dec 2018 17:30
Last modified: 02 Apr 2019 16:30

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Contributors

Author: Dwaipayan Biswas
Author: Zixuan Ye
Author: Evangelos B. Mazomenos
Author: Michael Jöbges

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