Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Kamath, S.

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    Machine learning for mobile wound assessment
    (SPIE spie@spie.org, 2018) Kamath, S.; Sirazitdinova, E.; Deserno, T.M.
    Chronic wounds affect millions of people around the world. In particular, elderly persons in home care may develop decubitus. Here, mobile image acquisition and analysis can provide a good assistance. We develop a system for mobile wound capture using mobile devices such as smartphones. The photographs are acquired with the integrated camera of the device and then calibrated and processed to determine the size of various tissues that are present in a wound, i.e., necrotic, sloughy, and granular tissue. The random forest classifier based on various color and texture features is used for that. These features are Sobel, Hessian, membrane projections, variance, mean, median, anisotropic diffusion, and bilateral as well as Kuwahara filters. The resultant probability output is thresholded using the Otsu technique. The similarity between manual ground truth labeling and the classification is measured. The acquired results are compared to those achieved with a basic technique of color thresholding, as well as those produced by the SVM classifier. The fast random forest was found to produce better results. It is also seen to have a superior performance when the method is applied only to the wound regions having the background subtracted. Mean similarity is 0.89, 0.39, and 0.44 for necrotic, sloughy, and granular tissue, respectively. Although the training phase is time consuming, the trained classifier performs fast enough to be implemented on the mobile device. This will allow comprehensive monitoring of skin lesions and wounds. © 2018 SPIE.
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    Modeling and experimental studies on the dynamics of bolted joint structure: Comparison of three vibration-based techniques for structural health monitoring
    (Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Deka, A.; Rao, A.; Kamath, S.; Gaurav, A.; Gangadharan, K.V.
    Detection of inadequate tightening in bolted joints is quintessential to ensure structural rigidity and to prevent catastrophic failure. Studies show that 30% of assembly failures occur due to inadequate tightening. In the present study, three vibration-based techniques are presented and compared to detect inadequate tightening of bolted joints. Variation in the damped natural frequency, variation in the damping ratio, and variation in the dynamic joint stiffness are studied with varying tightening torques in the bolted joint. The results show that all the three dynamic parameters vary with the tightness of the bolted joint. Dynamic joint stiffness varies significantly as opposed to the damping ratio and damped natural frequency as tightening torque reduces. In order to verify the results of dynamic stiffness method, ANSYS is used to model and analyze the joint. The experimental setup used to calculate the parameters consists of two Euler–Bernoulli beams connected with single lap bolted joint. © Springer Nature Singapore Pte Ltd 2020.
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    Engagement Analysis of Students in Online Learning Environments
    (Springer Science and Business Media Deutschland GmbH, 2022) Kamath, S.; Singhal, P.; Jeevan, G.; Annappa, B.
    Engagement rate is considered a metric that measures the extent of engagement a particular content is receiving from the audience. In e-learning settings, educators want to observe the level of interest of learners to appropriately modify their courses and make the educational process more effective. In this paper, an ensemble approach is proposed to detect student engagement levels while watching an e-learning video. The ensemble model consists of a deep convolutional neural network (DCNN) for facial expression recognition and a deep recurrent neural network (DRNN) for establishing a relationship between eye-gaze and engagement intensity. OpenFace 2.0 toolbox abilities are leveraged for feature extraction. Experimental results on the test datasets give an accuracy of 55.64% on DAiSEE and an MSE of 0.0598 on Engagement in the Wild Dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.