Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Multi-stream Multi-attention Deep Neural Network for Context-Aware Human Action Recognition(Institute of Electrical and Electronics Engineers Inc., 2022) Rashmi, M.; Guddeti, R.M.R.Technological innovations in deep learning models have enabled reasonably close solutions to a wide variety of computer vision tasks such as object detection, face recognition, and many more. On the other hand, Human Action Recognition (HAR) is still far from human-level ability due to several challenges such as diversity in performing actions. Due to data availability in multiple modalities, HAR using video data recorded by RGB-D cameras is frequently used in current research. This paper proposes an approach for recognizing human actions using depth and skeleton data captured using the Kinect depth sensor. Attention modules have been introduced in recent years to assist in focusing on the most important features in computer vision tasks. This paper proposes a multi-stream deep learning model with multiple attention blocks for HAR. At first, the depth and skeletal modalities' action data are represented using two distinct action descriptors. Each generates an image from the action data gathered from numerous frames. The proposed deep learning model is trained using these descriptors. Additionally, we propose a set of score fusion techniques for accurate HAR using all the features and trained CNN + LSTM streams. The proposed method is evaluated on two benchmark datasets using well known cross-subject evaluation protocol. The proposed technique achieved 89.83% and 90.7% accuracy on the MSRAction3D and UTDMHAD datasets, respectively. The experimental results establish the validity and effectiveness of the proposed model. © 2022 IEEE.Item An Efficient AI and IoT Enabled System for Human Activity Monitoring and Fall Detection(Institute of Electrical and Electronics Engineers Inc., 2024) Verma, N.; Mundody, S.; Guddeti, R.M.R.Falls present a significant health risk, particularly among the elderly, necessitating reliable wearable fall detection systems. This paper introduces an advanced AI-powered system that integrates Generative Adversarial Networks (GANs) for synthetic data augmentation and Convolutional Neural Networks (CNNs) for robust fall detection and daily activity recognition. The primary challenge in developing effective fall detection systems lies in the scarcity and diversity of real-world fall data. This paper addresses this challenge innovatively by employing a GAN trained on datasets of authentic fall events to generate synthetic data. This augmentation strategy significantly expands the training dataset, enhancing the model's capacity to generalize across various fall scenarios and daily activities. The system leverages a specialized 1D CNN architecture designed for processing accelerometer and gyroscope readings obtained from wearable devices, enabling precise feature extraction to distinguish subtle differences between falls and routine movements. The evaluation results demonstrate a notable advancement by achieving a superior accuracy of 99 % for fall detection while minimizing false positives. The developed CNN model can also classify 15 kinds of falls and 19 types of daily life activities. © 2024 IEEE.