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Improving the activity recognition using GMAF and transfer learning in post-stroke rehabilitation assessment

Boukhennoufa, Issam and Zhai, Xiaojun and Utti, Victor and McDonald-Maier, Klaus and Jackson, Jo (2020) Improving the activity recognition using GMAF and transfer learning in post-stroke rehabilitation assessment. In: SAMI 2021- IEEE 19th World Symposium on Applied Machine Intelligence and Informatics, 2021-01-21 - 2021-01-23, Herl'any, Slovakia (virtual). (In Press)

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Abstract

An important part of developing a performantassessment algorithm for post-stroke rehabilitation is to achievea high-precision activity recognition. Convolutional Neural Net-works (CNN) are known to give very accurate results, howeverthey require the data to be of a specific structure that differsfrom the sequential time-series format typically collected fromwearable sensors. In this paper, we describe models to improvethe activity recognition using the CNN classifier. At first bymodifying the Gramian angular field algorithm by encoding allthe sensors’ channels from a single time window into a single2D image allows to map the maximum activity characteristics.Feeding the resulting images to a simple 1D CNN classifierimproves the accuracy of the test data from 94% for thetraditional segmentation approach to 97.06%. Subsequently, weconvert the 2D images into the RGB format and use a 2D CNNclassifier. This results in increasing the test data accuracy to97.52%. Finally, we employ transfer learning with the popularVGG16 model to the RGB images, which yields to improvingthe accuracy further more to reach 98.53%.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: _not provided_
Uncontrolled Keywords: Stroke, GMAF, CNN, Activity recognition, Transfer learning
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of
Depositing User: Elements
Date Deposited: 15 Dec 2020 16:01
Last Modified: 15 Dec 2020 16:15
URI: http://repository.essex.ac.uk/id/eprint/29367

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