Comprehensive Review of Vision-Based Fall Detection Systems

Gutiérrez, Jesús; Rodríguez, Víctor Manuel Zamudio; Martín, Sergio

doi:10.3390/s21030947

Cited by 82 publications

(45 citation statements)

References 133 publications

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“…Deep learning models have seen significant success in a wide range of applications in recent years. In fact, different deep learning methods have been introduced for image and motion segmentation [10][11][12][13], detection [14][15][16], tracking [17,18] and classification [19,20]. Due to the success of deep learning methods, CNN models have also been used in the literature to provide relevant information on the number of people present on the stage.…”

Section: Related Workmentioning

confidence: 99%

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

Chouai

Doležel

Štursa

et al. 2021

Sensors

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In the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.

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Section: Related Workmentioning

confidence: 99%

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

Chouai

Doležel

Štursa

et al. 2021

Sensors

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“…A first group is formed by camera-based systems [15][16][17][18][19]. By means of a camera implemented in a room or on a person, a fall is detected based on an extensive algorithm that runs on a PC.…”

Section: Related Workmentioning

confidence: 99%

Bluetooth-Low-Energy-Based Fall Detection and Warning System for Elderly People in Nursing Homes

et al. 2022

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Due to the ever growing population of elderly people, there is a dramatic increase in fall accidents. Currently, multiple ideas exist to prevent the elderly from falling, by means of technology or individualised fall prevention training programs. Most of them are costly, difficult to implement or less used by the elderly, and they do not deliver the required results. Furthermore, the increasingly older population will also impact the workload of the medical and nursing personnel. Therefore, we propose a novel fall detection and warning system for nursing homes, relying on Bluetooth Low Energy wireless communication. This paper describes the hardware design of a fall-acceleration sensing wearable for the elderly. Moreover, the paper also focuses on a novel algorithm for real-time filtering of the measurement data as well as on a strategy to confirm the detected fall events, based on changes in the person’s orientation. In addition, we compare the performance of the algorithm to a machine learning procedure using a convolutional neural network. Finally, the proposed filtering technique is validated via measurements and simulation. The results show that the proposed algorithm as well as the convolutional neural network both results in an excellent accuracy when validating on a common database.

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“…To build an initial set of relevant references, we used an approach inspired from Gutiérrez et al [ 7 ]. The block (or flow) diagram of Figure 1 describes it.…”

Section: Survey Of Literature On Driver Monitoringmentioning

confidence: 99%

Survey and Synthesis of State of the Art in Driver Monitoring

Halin

Verly

Droogenbroeck

2021

Sensors

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Road vehicle accidents are mostly due to human errors, and many such accidents could be avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing interest in the automotive industry, and it will remain relevant for all vehicles that are not fully autonomous, and thus for decades for the average vehicle owner. The present paper focuses on the first step of DM, which consists of characterizing the state of the driver. Since DM will be increasingly linked to driving automation (DA), this paper presents a clear view of the role of DM at each of the six SAE levels of DA. This paper surveys the state of the art of DM, and then synthesizes it, providing a unique, structured, polychotomous view of the many characterization techniques of DM. Informed by the survey, the paper characterizes the driver state along the five main dimensions—called here “(sub)states”—of drowsiness, mental workload, distraction, emotions, and under the influence. The polychotomous view of DM is presented through a pair of interlocked tables that relate these states to their indicators (e.g., the eye-blink rate) and the sensors that can access each of these indicators (e.g., a camera). The tables factor in not only the effects linked directly to the driver, but also those linked to the (driven) vehicle and the (driving) environment. They show, at a glance, to concerned researchers, equipment providers, and vehicle manufacturers (1) most of the options they have to implement various forms of advanced DM systems, and (2) fruitful areas for further research and innovation.

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Comprehensive Review of Vision-Based Fall Detection Systems

Cited by 82 publications

References 133 publications

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

New End-to-End Strategy Based on DeepLabv3+ Semantic Segmentation for Human Head Detection

Bluetooth-Low-Energy-Based Fall Detection and Warning System for Elderly People in Nursing Homes

Survey and Synthesis of State of the Art in Driver Monitoring

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