Abstract
Fall detection has become a critical concern in the medical and healthcare fields due to the growing population of the elderly people. The research on fall and movement detection using wearable devices has made strides. Accurately recognizing the fall behavior in surveillance video and providing the early feedback can significantly minimize the fall-related injury and death of elderly people. However, the fall event is highly dynamic, impairing categorization accuracy. The current study sought to construct a fall detection architecture based on deep learning to predict falls and the Activities of Daily Living (ADLs). This paper proposes an efficient method for representing extracted features as RGB images and a CNN model for learning the features needed for accurate fall detection. Additionally, the proposed CNN model is used to test for and locate the target in video using threshold-based categorization. The suggested CNN model was evaluated on the SisFall dataset and was found to be capable of detecting falls prior to impact with a sensitivity of 100%, a specificity of 96.48%, and a response time of 223ms. The experimental findings attained an overall accuracy of 97.43%.
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Reddy Anakala, V.M., Rashmi, M., Natesha, B.V., Reddy Guddeti, R.M. (2023). Fall Detection and Elderly Monitoring System Using the CNN. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_16
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DOI: https://doi.org/10.1007/978-981-99-0047-3_16
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