Detection of falls among the elderly by a floor sensor using the electric near field

Rimminen, Henry; Lindström, J.; Linnavuo, Matti; Sepponen, Raimo

doi:10.1109/titb.2010.2051956

Cited by 155 publications

(79 citation statements)

References 13 publications

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“…The authors use k-nearest neighbor classifier to categorize the body posture and a fall is decided based on the time difference of event transitions. Several other approaches are also employed such as Rule-based techniques [21], Bayesian filtering [22], Hidden Markov Models [23], Threshold techniques [24] and Fuzzy Logic [25]. Among these decision and extraction techniques, none of them shows outstanding performance to the others and no appropriate comparison has been done yet.…”

Section: Related Workmentioning

confidence: 99%

Automatic Fall Detection using Smartphone Acceleration Sensor

Dang¹,

Truong²,

Dang³

2016

ijacsa

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Abstract-In this paper, we describe our work on developing an automatic fall detection technique using smart phone. Fall is detected based on analyzing acceleration patterns generated during various activities. An additional long lie detection algorithm is used to improve fall detection rate while keeping false positive rate at an acceptable value. An application prototype is implemented on Android operating system and is used to evaluate the proposed technique performance. Experiment results show the potential of using this app for fall detection. However, more realistic experiment setting is needed to make this technique suitable for use in real life situations.

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Section: Related Workmentioning

confidence: 99%

Automatic Fall Detection using Smartphone Acceleration Sensor

Dang¹,

Truong²,

Dang³

2016

ijacsa

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“…Sensing and interaction with the environment does not only involve infrastructure elements such as digital signs (electronic displays) [42], interactive walls [47,50,139] and smart floors [63,131], etc., but, to apply user-adaptive or context-aware behavior, also the users themselves [7,78,101,116]. Since in many cases a user (agent) is more than a digital device or entity, e. g. a human being, the inclusion of social behavior into pervasive applications is increasingly gaining importance.…”

Section: Exploiting Social Contextmentioning

confidence: 99%

Socio-inspired ICT

Ferscha

Farrahi

Hoven

et al. 2012

Eur. Phys. J. Spec. Top.

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Abstract. Modern ICT (Information and Communication Technology) has developed a vision where the "computer" is no longer associated with the concept of a single device or a network of devices, but rather the entirety of situated services originating in a digital world, which are perceived through the physical world. It is observed that services with explicit user input and output are becoming to be replaced by a computing landscape sensing the physical world via a huge variety of sensors, and controlling it via a plethora of actuators. The nature and appearance of computing devices is changing to be hidden in the fabric of everyday life, invisibly networked, and omnipresent, with applications greatly being based on the notions of context and knowledge. Interaction with such globe spanning, modern ICT systems will presumably be more implicit, at the periphery of human attention, rather than explicit, i.e. at the focus of human attention. Socio-inspired ICT assumes that future, globe scale ICT systems should be viewed as social systems. Such a view challenges research to identify and formalize the principles of interaction and adaptation in social systems, so as to be able to ground future ICT systems on those principles. This position paper therefore is concerned with the intersection of social behaviour and modern ICT, creating or recreating social conventions and social contexts through the use of pervasive, globe-spanning, omnipresent and participative ICT.

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“…Although his systems achieved about the same level of positioning accuracy as our TileTrack system [48], the loading-mode measurement method used in his studies has not been demonstrated in user activity recognition applications, nor can it be modified to measure human height and multiple postures, as is possible with the transmit-mode systems presented in this chapter. Nevertheless, in [34] they demonstrated a fall detector that was able to reason when a person was lying on the floor.…”

Section: State Of the Art In Capacitive User Trackingmentioning

confidence: 99%