TY - GEN
T1 - Fall Detection and Elderly Monitoring System Using the CNN
AU - Reddy Anakala, Vijay Mohan
AU - Rashmi, M.
AU - Natesha, B. V.
AU - Reddy Guddeti, Ram Mohana
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - 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%.
AB - 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%.
UR - https://www.scopus.com/pages/publications/85161102360
UR - https://www.scopus.com/pages/publications/85161102360#tab=citedBy
U2 - 10.1007/978-981-99-0047-3_16
DO - 10.1007/978-981-99-0047-3_16
M3 - Conference contribution
AN - SCOPUS:85161102360
SN - 9789819900466
T3 - Lecture Notes in Electrical Engineering
SP - 171
EP - 182
BT - Machine Learning and Computational Intelligence Techniques for Data Engineering - Proceedings of the 4th International Conference, MISP 2022
A2 - Singh, Pradeep
A2 - Singh, Deepak
A2 - Tiwari, Vivek
A2 - Misra, Sanjay
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Machine Intelligence and Signal Processing, MISP 2022
Y2 - 12 March 2022 through 14 March 2022
ER -