A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset

Espinosa, Ricardo; Ponce, Hiram; Gutiérrez, Sebastián; Martínez-Villaseñor, Lourdes; Brieva, Jorge; Moya-Albor, Ernesto

doi:10.1016/j.compbiomed.2019.103520

Cited by 96 publications

(41 citation statements)

References 38 publications

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“…As for visual sensor based fusion techniques, the limited studies that were included in our survey applied either traditional machine learning or deep learning (Espinosa et al, 2019;Ma et al, 2019) approaches. Fusion of multiple visual sensors from a public data set was presented by Espinosa et al (2019), where a 2D CNN was trained to classify falls during daily life activities.…”

Section: Machine Learning Deep Learning and Deep Reinforcement Learmentioning

confidence: 99%

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: Machine Learning Deep Learning and Deep Reinforcement Learmentioning

confidence: 99%

Elderly Fall Detection Systems: A Literature Survey

2020

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“…LSTM) would be deployed. Espinosa et al [78] developed a CNN based fall detection system utilizing UP-Fall detection multi-modal dataset. The system worked only with vision dataset and used multiple cameras for fall detection.…”

Section: A Convolutional Neural Network (Cnn) Based Fall Detection Smentioning

confidence: 99%

“…Various datasets have been used in the reviewed systems. Some of them used publicly available dataset such as KTH dataset [85], Uni-MiB SHAR [75], [77], [79] SisFall [70], [77], [79] UMA fall [79], UP-Fall [78], Multicam [78], URFD [64], [65], [73] and [76]. Some of them used simulated datasets generated by trainers.…”

Section: )mentioning

confidence: 99%

Deep Learning Based Systems Developed for Fall Detection: A Review

et al. 2020

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Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This paper provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.

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“…Fall detection methods using camera- (vision)-based sensors are some of the most widely applied methods, Espinosa et al [ 18 ] used a background subtraction technique to separate the object (person) in the image from the background, generated a bounding box by connecting the furthest foreground pixels, and extracted the aspect ratio of the person. Through this, the normal posture (standing up straight posture) and abnormal posture (fall) were distinguished.…”

Section: Introductionmentioning

confidence: 99%

“…This fall detection technique involves using the extracted features to analyze changes in the shape of the person’s silhouette through information on changes in the upper and lower parts of the human body. Camera-based fall detection methods exhibit higher detection accuracy and robustness than other technologies [ 18 ]. Moreover, they cause less disturbance to daily life than wearable sensors.…”

Section: Introductionmentioning

confidence: 99%

IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

Han

Kang

Choi

2020

Sensors

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Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into “Fall” and “Activities of daily living (ADL)” while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into “Fall” and “ADL.” The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

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A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset

Cited by 96 publications

References 38 publications

Elderly Fall Detection Systems: A Literature Survey

Elderly Fall Detection Systems: A Literature Survey

Deep Learning Based Systems Developed for Fall Detection: A Review

IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm

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