Detecting falls with 3D range camera in ambient assisted living applications: A preliminary study

Leone, Alessandro; Diraco, Giovanni; Siciliano, Pietro

doi:10.1016/j.medengphy.2011.02.001

Cited by 68 publications

(31 citation statements)

References 12 publications

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“…The features generally used for fall detection are the magnitude of the acceleration, posture monitoring, change in orientation, vertical velocity, angular velocity, and angular acceleration [3,4,6,12,18]. Automated image analysis systems based on video camera images have also been proposed [13]. Other approaches like the GoSafe system (http:// www.lifelinesys.com/content/), known as PERS (personal emergency response system), a commercial wearable device from Philips, allow users to push a emergency button in the event of a fall.…”

Section: Introductionmentioning

confidence: 99%

Exploring smartphone sensors for fall detection

et al. 2016

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Falling, and the fear of falling, is a serious health problem among the elderly. It often results in physical and mental injuries that have the potential to severely reduce their mobility, independence and overall quality of life. Nevertheless, the consequences of a fall can be largely diminished by providing fast assistance. These facts have lead to the development of several automatic fall detection systems. Recently, many researches have focused particularly on smartphone-based applications. In this paper, we study the capacity of smartphone built-in sensors to differentiate fall events from activities of daily living. We explore, in particular, the information provided by the accelerometer, magnetometer and gyroscope sensors. A collection of features is analyzed and the efficiency of different sensor output combinations is tested using experimental data. Based on these results, a new, simple, and reliable algorithm for fall detection is proposed. The proposed method is a threshold-based algorithm and is designed to require a low battery power consumption. The evaluation of the performance of the algorithm in collected data indicates 100 % for sensitivity and 93 % for specificity. Furthermore, evaluation conducted on a public dataset, for comparison with other existing smartphone-based fall detection algorithms, shows the high potential of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Exploring smartphone sensors for fall detection

et al. 2016

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“…Leone [21] also obtained a frame rate of 8fps, and the classification performances are [30] used FPGA in order to meet real-time requirements with high accuracy (86 %), but lower than ours (99.42 %). Moreover, it still is not robust enough in terms of recognition rates (global error 13 % and recall 83.33 %) compared to our global error (0.58 %) and recall (92.15 %).…”

Section: Comparison With State-of-the-artmentioning

confidence: 88%

“…Leone [21] proposed a preliminary study of an interesting framework to detect falls using two 3D time-offlight camera and showed that depth measurements using wall mounting setup are sufficient to detect falls with a real-time implementation. The main drawback of these cameras is that they are still expensive.…”

Section: Fall Detectionmentioning

confidence: 99%

Fast prototyping of a SoC-based smart-camera: a real-time fall detection case study

Senouci

Charfi

Heyrman

et al. 2014

J Real-Time Image Proc

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Smart camera, i.e. cameras that are able to acquire and process images in real-time, is a typical example of the new embedded computer vision systems. A key example of application is automatic fall detection, which can be useful for helping elderly people in daily life. In this paper, we propose a methodology for development and fast-prototyping of a fall detection system based on such a smart camera, which allows to reduce the development time compared to standard approaches. Founded on a supervised classification approach, we propose a HW/SW implementation to detect falls in a home environment using a single camera and an optimized descriptor adapted to real-time tasks. This heterogeneous implementation is based on Xilinx's system-on-chip named Zynq. The main contributions of this work are (i) the proposal of a codesign methodology. These methodologies enable the HW/ SW partitioning to be delayed using high-level algorithmic description and high-level synthesis tools. Our approach enables fast prototyping which allows fast architecture exploration and optimisation to be performed, (ii) the design of a hardware accelerator dedicated to boostingbased classification, which is a very popular and efficient algorithm used in image analysis, (iii) the proposal of falldetection embedded in a smart camera and enabling integration into the elderly people environment. Performances of our system are finally compared to the state-of-the-art.

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“…Occupancy can be measured by different technological solutions, among others: pressure mat (Almudevar, Leibovici, and Tentler 2008), force-to-resistor sensors, and force capacitive sensors (Fernandez-Luque et al 2014); . optical sensors, which nowadays refer mostly to different types of cameras: three-dimensional camera (Leone, Diraco, and Siciliano 2011), infrared camera (Darnall et al 2011), time-of-flight camera (Del Castillo et al 2006), and web camera (Ramlee, Tang, and Ismail 2012); . radio frequency identification sensors, which rely on electromagnetic fields to identify and track tags attached to objects in the kitchen (e.g.…”

Section: Intelligent Buildings Internationalmentioning

confidence: 99%

Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study

Labonnote

Høyland

2015

Intelligent Buildings International

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Objective: To provide an overview of the state of research concerning smart technologies that support independent living in buildings, as well as a deeper literature review of the specific challenges for the building industry. Methods: A systematic mapping methodology was applied in this study to provide basis knowledge on the state of the art. The initial search on three databases gave 3485 articles. Of these, 610 articles were finally selected as relevant to the study after removing the duplicates and screening by applying inclusion and exclusion criteria based on titles and abstracts. Keywording was performed in order to answer the research questions of the study. Results: Results from the systematic mapping technology allow the detection of almost mature technologies, as well as promising technologies and research gaps. Results from the deeper literature review have identified challenges and new trends specific to the building industry. Conclusions: Efforts are still needed for the building industry in order to contribute significantly to meeting societal needs. Further research: Research fields that would benefit from further research are highlighted.

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Detecting falls with 3D range camera in ambient assisted living applications: A preliminary study

Cited by 68 publications

References 12 publications

Exploring smartphone sensors for fall detection

Exploring smartphone sensors for fall detection

Fast prototyping of a SoC-based smart-camera: a real-time fall detection case study

Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study

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