Dataset Information

Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

ABSTRACT: Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, which were designed to discriminate small differences in defined sequences of hand movements. We recorded hand movement data from 17 younger and 17 older adults using an exoskeletal data glove while they were performing six different movement tasks. Accuracy rates in decoding the different movement types were similarly high for SVM and LSTM in across-subject classification, but, for within-subject classification, SVM outperformed LSTM. The SVM-based approach, therefore, appears particularly promising for the development of movement decoding tools, in particular if the goal is to generalize across age groups, for example for detecting specific motor disorders or tracking their progress over time.

SUBMITTER: Reichert C

PROVIDER: S-EPMC9412700 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

Reichert Christoph C Klemm Lisa L Mushunuri Raghava Vinaykanth RV Kalyani Avinash A Schreiber Stefanie S Kuehn Esther E Azañón Elena E

Sensors (Basel, Switzerland) 20220815 16

Decoding natural hand movements is of interest for human-computer interaction and may constitute a helpful tool in the diagnosis of motor diseases and rehabilitation monitoring. However, the accurate measurement of complex hand movements and the decoding of dynamic movement data remains challenging. Here, we introduce two algorithms, one based on support vector machine (SVM) classification combined with dynamic time warping, and the other based on a long short-term memory (LSTM) neural network, ...[more]

PMID: 36015862

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Project description:PurposeTo provide early and localized glioblastoma (GBM) recurrence prediction, we introduce a novel postsurgery multiparametric magnetic resonance-based support vector machine (SVM) method coupling with stem cell niche (SCN) proximity estimation.Methods and materialsThis study used postsurgery magnetic resonance imaging (MRI) scans from 50 patients with recurrent GBM, obtained approximately 2 months before clinically diagnosed recurrence. The main prediction pipeline consisted of a proximity-based estimator to identify regions with high risk of recurrence (HRRs) and an SVM classifier to provide voxelwise prediction in HRRs. The HRRs were estimated using the weighted sum of inverse distances to 2 possible origins of recurrence-the SCN and the tumor cavity. Subsequently, multiparametric voxels (from T1, T1 contrast-enhanced, fluid-attenuated inversion recovery, T2, and apparent diffusion coefficient) within the HRR were grouped into recurrent (warped from the clinical diagnosis) and nonrecurrent subregions and fed into the proximity estimation-coupled SVM classifier (SVMPE). The cohort was randomly divided into 40% and 60% for training and testing, respectively. The trained SVMPE was then extrapolated to an earlier time point for earlier recurrence prediction. As an exploratory analysis, the SVMPE predictive cluster sizes and the image intensities from the 5 magnetic resonance sequences were compared across time to assess the progressive subclinical traces.ResultsOn 2-month prerecurrence MRI scans from 30 test cohort patients, the SVMPE classifier achieved a recall of 0.80, a precision of 0.69, an F1-score of 0.73, and a mean boundary distance of 7.49 mm. Exploratory analysis at early time points showed spatially consistent but significantly smaller subclinical clusters and significantly increased T1 contrast-enhanced and apparent diffusion coefficient values over time.ConclusionsWe demonstrated a novel voxelwise early prediction method, SVMPE, for GBM recurrence based on clinical follow-up MR scans. The SVMPE is promising in localizing subclinical traces of recurrence 2 months ahead of clinical diagnosis and may be used to guide more effective personalized early salvage therapy.

Dataset Information

Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

Publications

Discriminating Free Hand Movements Using Support Vector Machine and Recurrent Neural Network Algorithms.

Similar Datasets

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