![]() Arganda-Carreras, Vision-based fall detection with convolutional neural networks. Houacine, Combined curvelets and hidden Markov models for human fall detection, Multimedia Tools Appl, 1–20 (2017)Ī. Houacine, An integrated visionbased approach for efficient human fall detection in a home environment. Zhang, A novel video-surveillance- based algorithm of fall detection, in Proceedings of 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) (Beijing, China, 2018), pp. Kepski, Human fall detection on embedded platform using depth maps and wireless accelerometer. Ignatov, Real-time human activity recognition from accelerometer data using convolutional neural networks. Yang, Device-free human localization and tracking with UHF passive RFID tags: a data-driven approach. Li, PAWS: Passive human activity recognition based on WiFi ambient signals. Paveglio, Intent to adopt location sharing for logging safety applications. Li, A survey on deep learning for big data. Sample, Sensor enabled wearable RFID technology for mitigating the risk of falls near beds, in 2013 IEEE International Conference on RFID (RFID) (IEEE, 2013), pp. Lu, Device-free human activity recognition using commercial WiFi devices. Alexander, Posture based recognition of the visual focus of attention for adaptive mobile information systems, in International Conference on Augmented Cognition (Springer, 2016), pp. Luo, Simultaneous indoor tracking and activity recognition using pyroelectric infrared sensors. Bergevin, Semantic human activity recognition: a literature review. Escalera, RGB-D-basedhuman motion recognition with deep learning: a survey. Jiang, Verity: an ambient assisted living platform. Huang, A customized human fall detection system using omni-camera images and personal information, in 1st Trans disciplinary Conference on Distributed Diagnosis and Home Healthcare (Arlington, USA, 2006) Chauvet, Implementation of a monitoring system for fall detection in elderly healthcare. Chambers, A posture recognition-based fall detection system for monitoring an elderly person in a smart home environment. Mrozek, Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Experimental analysis on the ‘UR fall detection dataset’ proved the significance and robustness of the proposed approach in terms of accuracy and precision. The development of artificial neural network (ANN) for fall detection, employing wearable sensors is also investigated in this research. It also describes the design of a wearable-based fall detection system that uses an accelerometer and a gyroscope as motion sensors to detect body rotation and movement. In this paper, a video-based fall detection system in an indoor environment is created using a convolution neural network (CNN). Therefore, a dedicated monitoring system is highly desirable in order to improve independent living. In particular, fall detection is a major challenging issue since elderly people are more likely to fall. Deep learning-based HAR is one of the most promising assistive technology tools for supporting elderly people in their daily lives by monitoring their cognitive and physical function. In order to handle the assemblage of big data and to analyze in real-time, deep learning is vital to extract useful information from complex systems. Human activity recognition (HAR) has grown in popularity over recent years, owing to the recent advancements in communication and wearable sensors.
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