Continuous monitoring of cardiac activity is paramount to understanding the functioning of the heart in addition to identifying precursors to conditions such as Atrial Fibrillation. Through continuous cardiac monitoring, early indications of any potential disorder can be detected before the actual event, allowing timely preventive measures to be taken. Electrocardiography (ECG) is an established standard for monitoring the function of the heart for clinical and non-clinical applications, but its electrode-based implementation makes it cumbersome, especially for uninterrupted monitoring. Hence we propose SeismoNet, a Deep Convolutional Neural Network which aims to provide an end-to-end solution to robustly observe heart activity from Seismocardiogram (SCG) signals. These SCG signals are motion-based and can be acquired in an easy, user-friendly fashion. Furthermore, the use of deep learning enables the detection of R-peaks directly from SCG signals in spite of their noise-ridden morphology and obviates the need for extracting hand-crafted features. SeismoNet was modelled on the publicly available CEBS dataset and achieved a high overall Sensitivity and Positive Predictive Value of 0.98 and 0.98 respectively.

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As a result of diverse elements like outdated learning methodology and imbalance of theoretical and experimental knowledge, over 75% of engineering graduates in India are not readily employable. They do not possess real-world skills needed by the corporations. This research discusses strategies for a combined approach of project based learning (PBL) and student social responsibility (SSR) to reinforce real-world skills among undergraduate students. A comprehensive case study carried out by IEEE Madras Section Special Interest Group on Humanitarian Technology (SIGHT) on students from multiple institutions based on these strategies have been illustrated in this paper. Furthermore, the outcomes of the case study relating to student proficiency development and its social implications are presented.

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