Conference Papers
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Item Water Salinity Assessment Using Remotely Sensed Images—A Comprehensive Survey(Springer Science and Business Media Deutschland GmbH, 2023) Priyadarshini, R.; Sudhakara, B.; Kamath S․, S.; Bhattacharjee, S.; Umesh, P.; Gangadharan, K.V.In the past few years, the problem of growing salinity in river estuaries has directly impacted living and health conditions, as well as agricultural activities globally, especially for those rivers which are the sources of daily water consumption for the surrounding community. Key contributing factors include hazardous industrial wastes, residential and urban wastewater, fish hatchery, hospital sewage, and high tidal levels. Conventional survey and sampling-based approaches for water quality assessment are often difficult to undertake on a large-scale basis and are also labor and cost-intensive. On the other hand, remote sensing-based techniques can be a good alternative to cost-prohibitive traditional practices. In this article, an attempt is made to comprehensively assess various approaches, datasets, and models for determining water salinity using remote sensing-based approaches and in situ observations. Our work revealed that remote sensing techniques coupled with other techniques for estimating the salinity of water offer a clear advantage over traditional practices and also is very cost-effective. We also highlight several observations and gaps that can be beneficial for the research community to contribute further in this significant research domain. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item 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.