Video-based Human Fall Detection in Smart Homes Using Deep Learning

Shojaei-Hashemi, Anahita; Nasiopoulos, Panos; Little, James J.; Pourazad, Mahsa T.

doi:10.1109/iscas.2018.8351648

Cited by 43 publications

(22 citation statements)

References 12 publications

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“…Vision-based detection is another prominent method. Extensive effort in this direction has been demonstrated, and some of which (Akagündüz et al, 2017 ; Ko et al, 2018 ; Shojaei-Hashemi et al, 2018 ) show promising performance. Although most cameras are not as portable as wearable devices, they offer other advantages which deem them as decent options depending upon the scenario.…”

Section: Fall Detection Using Individual Sensorsmentioning

confidence: 99%

“…Though such a limitation is imposed, literature shows that traditional machine learning, based on support vector machines, hidden Markov models, and decision trees are still very active in the field of fall detection that uses individual wearable non-visual or ambient sensors (e.g., accelerometer) (Wang et al, 2017a , b ; Chen et al, 2018 ; Saleh and Jeannès, 2019 ; Wu et al, 2019 ). For visual sensors the trend has been moving toward deep learning for convolutional neural networks (CNN) (Adhikari et al, 2017 ; Kong et al, 2019 ; Han et al, 2020 ), or LSTM (Shojaei-Hashemi et al, 2018 ). Deep learning is a sophisticated learning framework that besides the mapping function (as mentioned above and used in traditional machine learning), it also learns the features (in a hierarchy fashion) that characterize the concerned classes (e.g., falls and no falls).…”

Section: Fall Detection Using Individual Sensorsmentioning

confidence: 99%

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Elderly Fall Detection Systems: A Literature Survey

2020

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Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

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Section: Fall Detection Using Individual Sensorsmentioning

confidence: 99%

Section: Fall Detection Using Individual Sensorsmentioning

confidence: 99%

Elderly Fall Detection Systems: A Literature Survey

2020

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“…They utilized ML, SVM classification for identifying falls & non-fall activities. Shojaei-Hashemi et al [13] proposed DL based method for detecting human fall, with the help of LSTM-NN. This module isn't limited to other certain conditions, and efficiency evaluation shows that it exceeds overall present techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare

et al. 2021

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Recently, the techniques of Internet of Things (IoT) and mobile communications have been developed to gather human and environment information data for a variety of intelligent services and applications. Remote monitoring of elderly and disabled people living in smart homes is highly challenging due to probable accidents which might occur due to daily activities such as falls. For elderly people, fall is considered as a major reason for death of post-traumatic complication. So, early identification of elderly people falls in smart homes is needed to increase the survival rate of the person or offer required support. Recently, the advent of artificial intelligence (AI), IoT, wearables, smartphones, etc makes it feasible to design fall detection systems for smart homecare. In this view, this paper presents an IoT enabled elderly fall detection model using optimal deep convolutional neural network (IMEFD-ODCNN) for smart homecare. The goal of the IMEFD-ODCNN model is to enable smartphones and intelligent deep learning (DL) algorithms to detect the occurrence of falls in the smart home. Primarily, the input video captured by the IoT devices is pre-processed in different ways like resizing, augmentation, and min-max based normalization. Besides, SqueezeNet model is employed as a feature extraction technique to derive appropriate feature vectors for fall detection. In addition, the hyperparameter tuning of the SqueezeNet model takes place using the salp swarm optimization (SSO) algorithm. Finally, sparrow search optimization algorithm (SSOA) with variational autoencoder (VAE), called SSOA-VAE based classifier is employed for the classification of fall and non-fall events. Finally, in case of fall event detected, the smartphone sends an alert to the caretakers and hospital management. The performance validation of the IMEFD-ODCNN model takes place on UR fall detection dataset and multiple cameras fall dataset. The experimental outcomes highlighted the promising performance of the IMEFD-ODCNN model over the recent methods with the maximum accuracy of 99.76% and 99.57% on the multiple cameras fall and UR fall detection dataset.

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“…A deep learning technique of long short-term memory (LSTM) feed-forward neural network for human fall detection is presented using a transfer learning approach and outperformed existing works based on hand-crafted features [39]. A privacy-preserving fall method was proposed, wherein the Kinect sensor 3D skeleton image input was utilized to train the SVM classifier.…”

Section: Camera-(vision-) Basedmentioning

confidence: 99%

Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano

Mohan

Pillai²,

Chai

et al. 2021

Advances in Human-Computer Interaction

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The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.

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Video-based Human Fall Detection in Smart Homes Using Deep Learning

Cited by 43 publications

References 12 publications

Elderly Fall Detection Systems: A Literature Survey

Elderly Fall Detection Systems: A Literature Survey

Internet of Things and Deep Learning Enabled Elderly Fall Detection Model for Smart Homecare

Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano

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