Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Thummar, D.Subject: search.filters.subject.Machine learning

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    Importance of Knee Angle and Trunk Lean in the Detection of an Abnormal Walking Pattern Using Machine Learning
    (Springer Science and Business Media Deutschland GmbH, 2023) Pandit, P.; Thummar, D.; Verma, K.; Gangadharan, K.V.; Das, B.; Kamat, Y.
    Human gait can be quantified using motion capture systems. Three-dimensional (3D) gait analysis is considered the gold standard for gait assessment. However, the process of three-dimensional analysis is cumbersome and time-consuming. It also requires complex software and a sophisticated environment. Hence, it is limited to a smaller section of the population. We, therefore, aim to develop a system that can predict abnormal walking patterns by analyzing trunk lean and knee angle information. A vision-based OpenPose algorithm was used to calculate individual trunk lean and knee angles. Web applications have been integrated with this algorithm so that any device can use it. A Miqus camera system of Qualisys 3D gait analysis system was used to validate the OpenPose algorithm. The validation method yielded an error of ± 9° in knee angle and ± 8° in trunk lean. The natural walking pattern of 100 healthy individuals was compared to simulated walking patterns in an unconstrained setting in order to develop a machine learning program. From the collected data, an RNN-based LSTM machine learning model was trained to distinguish between normal and abnormal walkings. LSTM-based models were able to distinguish between normal and abnormal gaits with an accuracy of 80%. This study shows that knee angle and trunk lean patterns collected during walking can be significant indicators of abnormal gait. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Machine learning approach for optimization and performance prediction of triangular duct solar air heater: A comprehensive review
    (Elsevier Ltd, 2023) Nidhul, K.; Thummar, D.; Yadav, A.K.; Anish, S.
    This paper presents a comprehensive review of various kinds of distinct artificial roughness employed in rectangular and triangular duct solar air heaters to aid prospective researchers in finding a critical gap in the domain of solar air heaters. A Machine Learning (ML) model is developed using 72 distinct rib combinations compiled to 454 datasets and trained using an Artificial Neural Network (ANN) to predict the performance of ribbed triangular duct Solar Air Heater (SAH). The developed ML model predicts the data with an average deviation of <3%. Owing to reasonably accurate predictions, the same could be increased when more cases (geometric or operating parameters) are added to the databases by retraining the ANN. Further, a second law analysis of the rib configurations features collector efficiency and entropy generation variation with Re for various rib parameters. For the Re range of 4000 to 18000, optimum parameters such as rib height, pitch, chamfer angle, and inclinations are obtained for triangular duct SAH. This could help design engineers obtain the performance parameters of ribbed triangular duct SAH with other artificial roughness designs, possibly with a combination of different geometrical and operating parameters, without having to perform tests. © 2023 International Solar Energy Society