Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Human Activity Recognition in Smart Home using Deep Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2021) Kolkar, R.; Geetha, V.To understand the human activities and anticipate his intentions Human Activity Recognition(HAR) research is rapidly developing in tandem with the widespread availability of sensors. Various applications like elderly care and health monitoring systems in smart homes use smartphones and wearable devices. This paper proposes an effective HAR framework that uses deep learning methodology like Convolution Neural Networks(CNN), variations of LSTM(Long Short term Memory) and Gated Recurrent Units(GRU) Networks to recognize the activities based on smartphone sensors. The hybrid use of CNN-LSTM eliminates the handcrafted feature engineering and uses spatial and temporal data deep. The experiments are carried on UCI HAR and WISDM data sets, and the comparison results are obtained. The result shows a better 96.83 % and 98.00% for the UCI-HAR and WISDM datasets, respectively. © 2021 IEEE.Item Single Person Occupancy Detection Using PIR Sensors(Springer Science and Business Media Deutschland GmbH, 2024) Kolkar, R.; Geetha, V.; Salvi, S.The occupancy detection system presented in this study utilizes a combination of two PIR sensors and a micro-controller board to detect and store occupancy information in different rooms accurately. The PIR sensors detect motion within their field of view while the micro-controller processes the sensor inputs and controls the storage of occupancy data in a memory device. The circuit provides real-time occupancy status updates and allows for data retrieval for further analysis. The setup offers significant advantages such as energy efficiency, simplicity, and cost-effectiveness. The experimental results demonstrate the system's effectiveness in accurately detecting and storing occupancy information. The results show the elderly spend time in various rooms. The combined circuit has potential applications in various domains, including smart homes, energy management, and security systems, where knowledge of room occupancy patterns is crucial for optimizing resources and enhancing user experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.