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An Open Source Based Assistive Clinical Application Development for Movement Disorder
Jaekyung Ha, Hyojeong Hwangbo, Hyojeong Gwon, Jihyun Jang, Suhan Bae, Daeun Yun, Yeonsu Kim, Hyunjin Yun, Yein Lee, Young Goo Kim, Minkyu Ahn
http://doi.org/10.5626/JOK.2021.48.5.518
Movement disorders, such as Parkinson"s disease and Essential tremor, are usually assessed using behavioral tests or clinical questionnaires. The assessment typically involves filling in questionnaires or observation by clinicians. Because of the nature of the process, data and records are not well organized and managed; consequently, accurate assessment may be difficult. In this study, we developed an open-source based application running on Tablet PC. Two clinical questionnaires (e.g. Unified Parkinson’s disease Rating Scale and Clinical rating scale of tremor) were implemented, and the application provides efficient management of assessment records. Two behavioral tests (e.g. line and spiral drawing) that are frequently used are also implemented for quick diagnosis. The source code for the application and user/developer manuals are currently open through the web repository (github), thus anyone can easily download and uses it or clinical or research purposes.
Effect Scene Detection using Multimodal Deep Learning Models
Jeongseon Lim, Mikyung Han, Hyunjin Yoon
http://doi.org/10.5626/JOK.2018.45.12.1250
A conventional movie can be converted into a 4D movie by identifying effect scenes. In order to automate this process, in this paper, we propose a multimodal deep learning model that detects effect scenes using both visual and audio features of a movie. We have classified effect/non-effect scenes using audio-based Convolutional Recurrent Neural Network (CRNN) model and video-based Long Short-term Memory (LSTM) and Multilayer Perceptron (MLP) model. Also, we have implemented feature-level fusion. In addition, based on our own observation that effects typically occur during non-dialog scenes, we further detected non-dialog scenes using audio-based Convolutional Neural Network (CNN) model. Subsequently, the prediction scores of audio-visual effect scene classification and audio-based non-dialog classification models were combined. Finally, we detected sequences of effect scenes of the entire movie using prediction score of the input window. Experiments using real-world 4D movies demonstrate that the proposed multimodal deep learning model outperforms unimodal models in terms of effect scene detection accuracy.
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