Sensor Fusion-Oriented Fall Detection for Assistive Technologies Applications

Cagnoni, Stefano; Matrella, Guido; Mordonini, Monica; Sassi, Federico; Ascari, Luca

doi:10.1109/isda.2009.203

Cited by 19 publications

(10 citation statements)

References 13 publications

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“…The watch samples the X, Y and Z axes each 1/10 th of a second, and sends the data to a PC. Compared to other research experiments, which use higher sampling rates (up to 160 Hz) (Cagnoni et al, 2009), a sampling rate of 10 Hz can be considered as quite low. The use of a lower sampling rate has three main benefits:…”

Section: Experimenal Setupmentioning

confidence: 93%

FALL DETECTION USING BIOLOGIALLY INSPIRED MONITORING - Artificial Immune System in the Area of Assisted Living

Bersch¹,

Azzi²,

Khusainov³

2011

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

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This position paper supports the use of Artificial Immune System (AIS) in the area of Ambient Assisted Living (AAL). While AIS has been used for anomaly detection and classification in a wide range of applications, little work has been done on using AIS for detecting abnormal behaviour in health monitoring applications. In this paper, we propose to use AIS for fall detection, since falls can be seen as deviations from the normal behaviour. We justify our proposal by analysing research that has been carried out in the past using AIS in different fields and emphasising on the similarities to the area of AAL. The paper also describes the experimental setup that is currently being used for our current and future work.

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Section: Experimenal Setupmentioning

confidence: 93%

FALL DETECTION USING BIOLOGIALLY INSPIRED MONITORING - Artificial Immune System in the Area of Assisted Living

Bersch¹,

Azzi²,

Khusainov³

2011

Proceedings of the International Conference on Evolutionary Computation Theory and Applications

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“…None of the reviewed studies deployed thresholding technique on individual or fusion level and the only example of rule-based approach was complicated by semantic web rule language. The most popular 8 Journal of Sensors algorithm is fuzzy logic utilized as fuzzy inference system [53] or fuzzy logic decision tree [55]. Other methods include evidential networks, Dempster-Shafer theory, or Hidden Markov Models.…”

Section: Wearable/ambient Sensor Fusionmentioning

confidence: 99%

Challenges and Issues in Multisensor Fusion Approach for Fall Detection: Review Paper

2016

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Emergency situations associated with falls are a serious concern for an aging society. Yet following the recent development within ICT, a significant number of solutions have been proposed to track body movement and detect falls using various sensor technologies, thereby facilitating fall detection and in some cases prevention. A number of recent reviews on fall detection methods using ICT technologies have emerged in the literature and an increasingly popular approach considers combining information from several sensor sources to assess falls. The aim of this paper is to review in detail the subfield of fall detection techniques that explicitly considers the use of multisensor fusion based methods to assess and determine falls. The paper highlights key differences between the single sensor-based approach and a multifusion one. The paper also describes and categorizes the various systems used, provides information on the challenges of a multifusion approach, and finally discusses trends for future work.

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“…In [21], the authors describe two fall sensors, an embedded video-based one and a wearable accelerometerbased one, which can be managed within a data-fusion-oriented framework, implementing policies aimed at maximizing system reliability and minimizing the presence of false alarms. The two sensors can be integrated into a modular architecture to compensate for each other's limits, favoring the development of a harmonic, modular and easily extensible system able to manage different areas of the environment.…”

Section: Sensors Used In Wearable Devices For Fall Detectionmentioning

confidence: 99%

“…When used in conjunction with other sensors (audio, wearable, etc. ), such a compact, economical and little-invasive device can provide a description of the environment being monitored [21]. In [22] the authors speak of their plans to use additional sensors, such as RFID for object localization, floor-mounted vibration sensors for fall detection in privacy-sensitive areas, and infrared cameras, in conjunction with their smart cameras, for additional tracking and health monitoring capabilities.…”

Section: Sensors Used In Wearable Devices For Fall Detectionmentioning

confidence: 99%