Elder Tracking and Fall Detection System Using Smart Tiles

Daher, Mohamad; Diab, Ahmad; Najjar, Maan El Badaoui El; Khalil, Mohamad; Charpillet, François

doi:10.1109/jsen.2016.2625099

Cited by 89 publications

(66 citation statements)

References 36 publications

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“…The system could be set up indoors, even in the bathroom. Similarly, Dather et al [19] used force sensors and accelerometers that were concealed under intelligent tiles to locate, track, and recognize user activities, such as walking, standing, sitting, lying down, and falling. However, the proposed method was costly and could only be used indoors.…”

Section: Environmental-sensor-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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Due to advances in medical technology, the elderly population has continued to grow. Elderly healthcare issues have been widely discussed—especially fall accidents—because a fall can lead to a fracture and have serious consequences. Therefore, the effective detection of fall accidents is important for both elderly people and their caregivers. In this work, we designed an Image-based FAll Detection System (IFADS) for nursing homes, where public areas are usually equipped with surveillance cameras. Unlike existing fall detection algorithms, we mainly focused on falls that occur while sitting down and standing up from a chair, because the two activities together account for a higher proportion of falls than forward walking. IFADS first applies an object detection algorithm to identify people in a video frame. Then, a posture recognition method is used to keep tracking the status of the people by checking the relative positions of the chair and the people. An alarm is triggered when a fall is detected. In order to evaluate the effectiveness of IFADS, we not only simulated different fall scenarios, but also adopted YouTube and Giphy videos that captured real falls. Our experimental results showed that IFADS achieved an average accuracy of 95.96%. Therefore, IFADS can be used by nursing homes to improve the quality of residential care facilities.

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Section: Environmental-sensor-based Methodsmentioning

confidence: 99%

An Image-Based Fall Detection System for the Elderly

Chu

2018

Applied Sciences

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“…This can be considered a critical aspect because wearable sensors can lead to physical discomfort for the user. A different kind of sensor, less invasive and more discreet, is used in the system presented by [17], which can both track and detect the fall of elderly people using smart tiles.…”

Section: Ambient Assisted Living Systemsmentioning

confidence: 99%

A Vision-Based System for Monitoring Elderly People at Home

2020

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Assisted living technologies can be of great importance for taking care of elderly people and helping them to live independently. In this work, we propose a monitoring system designed to be as unobtrusive as possible, by exploiting computer vision techniques and visual sensors such as RGB cameras. We perform a thorough analysis of existing video datasets for action recognition, and show that no single dataset can be considered adequate in terms of classes or cardinality. We subsequently curate a taxonomy of human actions, derived from different sources in the literature, and provide the scientific community with considerations about the mutual exclusivity and commonalities of said actions. This leads us to collecting and publishing an aggregated dataset, called ALMOND (Assisted Living MONitoring Dataset), which we use as the training set for a vision-based monitoring approach.We rigorously evaluate our solution in terms of recognition accuracy using different state-of-the-art architectures, eventually reaching 97% on inference of basic poses, 83% on alerting situations, and 71% on daily life actions. We also provide a general methodology to estimate the maximum allowed distance between camera and monitored subject. Finally, we integrate the defined actions and the trained model into a computer-vision-based application, specifically designed for the objective of monitoring elderly people at their homes.

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“…In the smart space domain, detecting human presence is an important task in smart spaces to track people [6], detect their activities [1], provide services such as health-care [7], or increase safety and security [8]. For example, detecting activities such as falling can be helpful in medical applications such as elderly care for independent living that can improve their life expectancy [9].…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

Singh

Akşanlı

2019

JSAN

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This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user's position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users.

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Elder Tracking and Fall Detection System Using Smart Tiles

Cited by 89 publications

References 36 publications

An Image-Based Fall Detection System for the Elderly

An Image-Based Fall Detection System for the Elderly

A Vision-Based System for Monitoring Elderly People at Home

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

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