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Low-complexity framework for movement classification using body-worn sensors

Low-complexity framework for movement classification using body-worn sensors
Low-complexity framework for movement classification using body-worn sensors
We present a low-complexity framework for classifying elementary arm-movements (reach-retrieve, lift-cup-to-mouth, rotate-arm) using wrist-worn, inertial sensors. We propose that this methodology could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies tracking occurrence of specific movements performed by patients with their paretic arm. Movements performed in a controlled training-phase are processed to form unique clusters in a multi-dimensional feature-space. Subsequent movements performed in an uncontrolled testing-phase are associated to the proximal cluster using a minimum distance classifier (MDC). The framework involves performing the compute-intensive clustering on the training-dataset offline (Matlab) whereas the computation of selected features on the testing-dataset and the minimum distance (Euclidean) from pre-computed cluster centroids are done in hardware with an aim of low-power execution on sensor nodes.
The architecture for feature-extraction and MDC are realized using Coordinate Rotation Digital Computer based design which classifies a movement in (9n+31) clock cycles, n being number of data samples. The design synthesized in STMicroelectronics 130nm technology consumed 5.3 nW @50 HZ, besides being functionally verified upto 20 MHz, making it applicable for real-time high-speed operations. Our experimental results show that the system can recognize all three arm-movements with average accuracies of 86% and 72% for four healthy subjects using accelerometer and gyroscope data respectively, whereas for stroke survivors the average accuracies were 67% and 60%. The framework was further demonstrated as a FPGA-based real-time system, interfacing with a streaming sensor unit.
1063-8210
1-12
Biswas, Dwaipayan
bc8a9147-64df-451f-b00b-e1265087b6f3
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Panic, Goran
708c0c34-2f72-4ed4-ae6e-ec1aec0a4a4a
Mazomenos, Evangelos B.
aeda0154-8464-4f5b-b634-a8aebe364e32
Achner, Josy
1eb12fc1-6d4e-41e6-8e2e-6b7f4fec7daf
Klemke, Jasmin
0c095460-4faf-4d56-bfcc-c04a26e70249
Jöbges, Michael
c249b79d-ca43-46ce-86cb-a97b33a0c3d8
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321
Biswas, Dwaipayan
bc8a9147-64df-451f-b00b-e1265087b6f3
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Panic, Goran
708c0c34-2f72-4ed4-ae6e-ec1aec0a4a4a
Mazomenos, Evangelos B.
aeda0154-8464-4f5b-b634-a8aebe364e32
Achner, Josy
1eb12fc1-6d4e-41e6-8e2e-6b7f4fec7daf
Klemke, Jasmin
0c095460-4faf-4d56-bfcc-c04a26e70249
Jöbges, Michael
c249b79d-ca43-46ce-86cb-a97b33a0c3d8
Ortmann, Steffen
dc43ef51-5657-45ed-b634-9a5e3cf6b321

Biswas, Dwaipayan, Maharatna, Koushik and Panic, Goran et al. (2017) Low-complexity framework for movement classification using body-worn sensors. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 1-12. (doi:10.1109/TVLSI.2016.2641046).

Record type: Article

Abstract

We present a low-complexity framework for classifying elementary arm-movements (reach-retrieve, lift-cup-to-mouth, rotate-arm) using wrist-worn, inertial sensors. We propose that this methodology could be used as a clinical tool to assess rehabilitation progress in neurodegenerative pathologies tracking occurrence of specific movements performed by patients with their paretic arm. Movements performed in a controlled training-phase are processed to form unique clusters in a multi-dimensional feature-space. Subsequent movements performed in an uncontrolled testing-phase are associated to the proximal cluster using a minimum distance classifier (MDC). The framework involves performing the compute-intensive clustering on the training-dataset offline (Matlab) whereas the computation of selected features on the testing-dataset and the minimum distance (Euclidean) from pre-computed cluster centroids are done in hardware with an aim of low-power execution on sensor nodes.
The architecture for feature-extraction and MDC are realized using Coordinate Rotation Digital Computer based design which classifies a movement in (9n+31) clock cycles, n being number of data samples. The design synthesized in STMicroelectronics 130nm technology consumed 5.3 nW @50 HZ, besides being functionally verified upto 20 MHz, making it applicable for real-time high-speed operations. Our experimental results show that the system can recognize all three arm-movements with average accuracies of 86% and 72% for four healthy subjects using accelerometer and gyroscope data respectively, whereas for stroke survivors the average accuracies were 67% and 60%. The framework was further demonstrated as a FPGA-based real-time system, interfacing with a streaming sensor unit.

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Accepted/In Press date: 14 December 2016
e-pub ahead of print date: 9 January 2017
Organisations: Electronic & Software Systems

Identifiers

Local EPrints ID: 404740
URI: https://eprints.soton.ac.uk/id/eprint/404740
ISSN: 1063-8210
PURE UUID: cce72dbd-2694-4d1b-869c-07decbfa45bb

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Date deposited: 20 Jan 2017 15:05
Last modified: 09 Jan 2018 17:36

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