Abstract
Smart expert systems line up with various applications to enhance the quality of lifestyle of human beings, such as major applications for smart health monitoring systems. An intelligent assistive system is one such application to assist Alzheimer’s patients in carrying out day-to-day activities and real-time monitoring by the caretakers. Fall detection is one of the tasks of an assistive system; many existing methods primarily focus on either vision or sensor data. Vision-based methods suffer from false positive results because of occlusion, and sensor-based methods yield false results because of the patient’s long-term lying posture. We address this problem by proposing a multimodel fall detection system (MMFDS) with hybrid data, which includes both vision and sensor data. Random forest and long-term recurrent convolution networks (LRCN) are the primary classification algorithms for sensor data and vision data, respectively. MMFDS integrates sensor and vision data to enhance fall detection accuracy by incorporating an ensemble approach named majority voting for the hybrid data. On evaluating the proposed work on the UP fall detection dataset, accuracy was enhanced to 99.2%, with an improvement in precision, F1 score, and recall.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.