Deep feature fusion with computer vision driven fall detection approach for enhanced assisted living safety

Abstract

Assisted living facilities cater to the demands of the elderly population, providing assistance and support with day-to-day activities. Fall detection is fundamental to ensuring their well-being and safety. Falls are frequent among older persons and might cause severe injuries and complications. Incorporating computer vision techniques into assisted living environments is revolutionary for these issues. By leveraging cameras and complicated approaches, a computer vision (CV) system can monitor residents' movements continuously and identify any potential fall events in real time. CV, driven by deep learning (DL) techniques, allows continuous surveillance of people through cameras, investigating complicated visual information to detect potential fall risks or any instances of falls quickly. This system can learn from many visual data by leveraging DL, improving its capability to identify falls while minimalizing false alarms precisely. Incorporating CV and DL enhances the efficiency and reliability of fall detection and allows proactive intervention, considerably decreasing response times in emergencies. This study introduces a new Deep Feature Fusion with Computer Vision for Fall Detection and Classification (DFFCV-FDC) technique. The primary purpose of the DFFCV-FDC approach is to employ the CV concept for detecting fall events. Accordingly, the DFFCV-FDC approach uses the Gaussian filtering (GF) approach for noise eradication. Besides, a deep feature fusion process comprising MobileNet, DenseNet, and ResNet models is involved. To improve the performance of the DFFCV-FDC technique, improved pelican optimization algorithm (IPOA) based hyperparameter selection is performed. Finally, the detection of falls is identified using the denoising autoencoder (DAE) model. The performance analysis of the DFFCV-FDC methodology was examined on the benchmark fall database. A widespread comparative study reported the supremacy of the DFFCV-FDC approach with existing techniques.

Keywords: Computer vision; Deep feature fusion; Deep learning; Denoising autoencoder; Fall detection; Pelican optimization algorithm.

Fig. 1

Working flow of DFFCV-FDC technique.

Fig. 2

Architecture of ResNet.

Fig. 3

Sample Images a) Multiple cameras’ fall detection database [Source Link: E. Auvinet, C. Rougier, J. Meunier, A. S. Arnaud and J. Rousseau, “Multiple cameras fall dataset,” DIROuniversité de montréal, Montreal, QC, Canada, tech. Rep. 1350,” 2010] b) UR fall detection database [Source Link: UR Fall Detection (URFD) dataset with an overhead sequence (available at http://fenix.univ.rzeszow.pl/~mkepski/ds/uf.html)].

Fig. 4

Confusion matrices of DFFCV-FDC technique under frontal sequence database (a-f) Epochs 500–3000.

Fig. 5

Average of DFFCV-FDC technique at frontal sequence database.

Fig. 6

Accu_y curve of DFFCV-FDC technique at frontal sequence database.

Fig. 7

Loss curve of DFFCV-FDC technique at frontal sequence database.

Fig. 8

PR curve of DFFCV-FDC technique at frontal sequence database.

Fig. 9

ROC curve of DFFCV-FDC technique on frontal sequence database.

Fig. 10

Accu_y outcome of DFFCV-FDC technique on frontal sequence database ^,.

Fig. 11

Confusion matrices of DFFCV-FDC technique under overhead sequence database (a-f) Epochs 500–3000.

Fig. 12

Average outcome of DFFCV-FDC technique on overhead sequence database.

Fig. 13

Accu_y curve of DFFCV-FDC technique on overhead sequence database^,.

Fig. 14

Loss curve of DFFCV-FDC technique on overhead sequence database.

Fig. 15

PR curve of DFFCV-FDC technique on overhead sequence database.

Fig. 16

ROC curve of DFFCV-FDC technique on overhead sequence database.

Fig. 17

Accu_y outcome of DFFCV-FDC technique on overhead sequence database^,.