Browsing by Author "Goyal, D."

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    Affective Feedback Synthesis Towards Multimodal Text and Image Data
    (Association for Computing Machinery, 2023) Kumar, P.; Bhatt, G.; Ingle, O.; Goyal, D.; Raman, B.
    In this article, we have defined a novel task of affective feedback synthesis that generates feedback for input text and corresponding images in a way similar to humans responding to multimodal data. A feedback synthesis system has been proposed and trained using ground-truth human comments along with image-text input. We have also constructed a large-scale dataset consisting of images, text, Twitter user comments, and the number of likes for the comments by crawling news articles through Twitter feeds. The proposed system extracts textual features using a transformer-based textual encoder. The visual features have been extracted using a Faster region-based convolutional neural networks model. The textual and visual features have been concatenated to construct multimodal features that the decoder uses to synthesize the feedback. We have compared the results of the proposed system with baseline models using quantitative and qualitative measures. The synthesized feedbacks have been analyzed using automatic and human evaluation. They have been found to be semantically similar to the ground-truth comments and relevant to the given text-image input. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
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    Detection of Gait Abnormalities caused by Neurological Disorders
    (Institute of Electrical and Electronics Engineers Inc., 2020) Goyal, D.; Jerripothula, K.; Mittal, A.
    In this paper, we leverage gait to potentially detect some of the important neurological disorders, namely Parkinson's disease, Diplegia, Hemiplegia, and Huntington's Chorea. Persons with these neurological disorders often have a very abnormal gait, which motivates us to target gait for their potential detection. Some of the abnormalities involve the circumduction of legs, forward-bending, involuntary movements, etc. To detect such abnormalities in gait, we develop gait features from the key-points of the human pose, namely shoulders, elbows, hips, knees, ankles, etc. To evaluate the effectiveness of our gait features in detecting the abnormalities related to these diseases, we build a synthetic video dataset of persons mimicking the gait of persons with such disorders, considering the difficulty in finding a sufficient number of people with these disorders. We name it NeuroSynGait video dataset. Experiments demonstrated that our gait features were indeed successful in detecting these abnormalities. © 2020 IEEE.