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Hand tremor detection in videos with cluttered background using neural network based approaches


Wang, X and Garg, S and Tran, SN and Bai, Q and Alty, J, Hand tremor detection in videos with cluttered background using neural network based approaches, Health Information Science and Systems, 9, (1) pp. 1-14. ISSN 2047-2501 (2021) [Refereed Article]

Copyright Statement

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.

DOI: doi:10.1007/s13755-021-00159-3


With the increasing prevalence of neurodegenerative diseases, including Parkinsonís disease, hand tremor detection has become a popular research topic because it helps with the diagnosis and tracking of disease progression. Conventional hand tremor detection algorithms involved wearable sensors. A non-invasive hand tremor detection algorithm using videos as input is desirable but the existing video-based algorithms are sensitive to environmental conditions. An algorithm, with the capability of detecting hand tremor from videos with a cluttered background, would allow the videos recorded in a non-research environment to be used. Clinicians and researchers could use videos collected from patients and participants in their own home environment or standard clinical settings. Neural network based machine learning architectures provide high accuracy classification results in related fields including hand gesture recognition and body movement detection systems. We thus investigated the accuracy of advanced neural network architectures to automatically detect hand tremor in videos with a cluttered background. We examined configurations with different sets of features and neural network based classification models. We compared the performance of different combinations of features and classification models and then selected the combination which provided the highest accuracy of hand tremor detection. We used cross validation to test the accuracy of the trained model predictions. The highest classification accuracy for automatically detecting tremor (vs non tremor) was 80.6% and this was obtained using Convolutional Neural Network-Long Short-Term Memory and features based on measures of frequency and amplitude change.

Item Details

Item Type:Refereed Article
Keywords:tremor, computer vision, diagnostics, artificial intelligence, neurology, Advanced neural network, hand tremor detection, machine learning, videos with cluttered background
Research Division:Information and Computing Sciences
Research Group:Applied computing
Research Field:Applications in health
Objective Division:Health
Objective Group:Clinical health
Objective Field:Diagnosis of human diseases and conditions
UTAS Author:Wang, X (Miss Xinyi Wang)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Tran, SN (Dr Son Tran)
UTAS Author:Bai, Q (Dr Quan Bai)
UTAS Author:Alty, J (Associate Professor Jane Alty)
ID Code:145414
Year Published:2021
Web of Science® Times Cited:5
Deposited By:Wicking Dementia Research and Education Centre
Deposited On:2021-07-21
Last Modified:2022-08-23

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