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Low-Complexity Framework for Movement Classification Using Body-Worn Sensors

Biswas, D; Maharatna, K; Panic, G; Mazomenos, EB; Achner, J; Klemke, J; Joebges, M; (2017) Low-Complexity Framework for Movement Classification Using Body-Worn Sensors. IEEE Transactions on Very Large Scale Integration (VLSI) Systems , 25 (4) pp. 1537-1548. 10.1109/TVLSI.2016.2641046. Green open access

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Abstract

We present a low-complexity framework for classifying elementary arm movements (reach retrieve, lift cup to mouth, and 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 multidimensional feature space. Subsequent movements performed in an uncontrolled testing phase are associated with the proximal cluster using a minimum distance classifier (MDC). The framework involves performing the compute-intensive clustering on the training data set offline (MATLAB), whereas the computation of selected features on the testing data set and the minimum distance (Euclidean) from precomputed 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 that classifies a movement in (9n + 31) clock cycles, n being number of data samples. The design synthesized in STMicroelectronics 130-nm technology consumed 5.3 nW at 50 Hz, besides being functionally verified up to 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 field-programmable gate array-based real-time system, interfacing with a streaming sensor unit.

Type: Article
Title: Low-Complexity Framework for Movement Classification Using Body-Worn Sensors
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TVLSI.2016.2641046
Publisher version: http://doi.org/10.1109/TVLSI.2016.2641046
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Real-time systems, Testing, Sensor systems, Training data, Computer architecture, Feature extraction
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/1540622
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