Fall Detection Using Location Sensors and Accelerometers

Luštrek, Mitja; Gjoreski, Hristijan; Vega, Narciso Gonzalez; Kozina, Simon; Cvetković, Božidara; Mirchevska, Violeta; Gams, Matjaž

doi:10.1109/mprv.2015.84

Cited by 33 publications

(25 citation statements)

References 7 publications

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“…[51] In in-door conditions data can be collected by various sensors that monitor the participant's movement and position: intelligent chair sensor that detects posture [31], floor sensors for detecting falls [27], door sensors for detecting movement around home, out-house environment: For research purposes and maximizing of data collection, research will be conducted in urban environment. This data should represent the life style of the participants in outside environment.…”

Section: Discussionmentioning

confidence: 99%

Infrastructural Model for the Healthcare System based on Emerging Technologies

2014

APH

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Section: Discussionmentioning

confidence: 99%

Infrastructural Model for the Healthcare System based on Emerging Technologies

2014

APH

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“…First, we extract multiple features proposed in [24][25][26] . Features include max(a), min(a), STD(a), for each separate axis of the accelerometer: free-fall detection (Eq.…”

Section: Machine-learningmentioning

confidence: 99%

Intelligent assistant carer for active aging

Bizjak

Gradišek

Stepančič

et al. 2017

EURASIP J. Adv. Signal Process.

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We present the concept of an Intelligent Assistant Carer system for the elderly, designed to help with active aging and to facilitate the interactions with carers. The system is modular, allowing the users to choose the appropriate functions according to their needs, and is built on an open platform in order to make it compatible with third-party products and services. Currently, the system consists of a wearable device (a smartwatch) and an internet portal that manages the data and takes care of the interactions between the user, the carers, and the support services. We present in detail one of the modules, i.e., fall detection, and the results of a pilot study for the system on 150 users over the course of 3 months.

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“…Learning the pattern of acceleration data when a fall is occurring has been studies widely [28] [38][54] [55]. Raw data is often pre-processed by low-pass filter, re-sampling.…”

Section: ) Fall Detection By Machine Learning Techniquesmentioning

confidence: 99%