Determination of simple thresholds for accelerometry-based parameters for fall detection

Kangas, Maarit; Konttila, Antti; Winblad, Ilkka; Jämsä, Timo

doi:10.1109/iembs.2007.4352552

Cited by 167 publications

(144 citation statements)

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“…Typical approach is detection of falls by applying thresholds to accelerations, velocities and angles [7]. In [12] they used a 3-axis accelerometer worn on the chest; by applying a simple threshold to the acceleration, they detected falls with 98.9% accuracy.…”

Section: Related Workmentioning

confidence: 99%

Accelerometer Placement for Posture Recognition and Fall Detection

Gjoreski

Luštrek

Gams

2011

2011 Seventh International Conference on Intelligent Environments

185

112

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Abstract-This paper presents an approach to fall detection with accelerometers that exploits posture recognition to identify postures that may be the result of a fall. Posture recognition as a standalone task was also studied. Nine placements of up to four sensors were considered: on the waist, chest, thigh and ankle. The results are compared to the results of a system using ultrawideband location sensors on a scenario consisting of events difficult to recognize as falls or non-falls. Three accelerometers proved sufficient to correctly recognize all the events except one (a slow fall). The location-based system was comparable to two accelerometers, except that it was able to recognize the slow fall because it resulted in lying outside the bed, whose location was known to the system. One accelerometer was able to recognize only the most clear-cut fall. Two accelerometers achieved over 90% accuracy of posture recognition, which was better than the location-based system. Chest and waist accelerometers proved best at both tasks, with the chest accelerometer having a slight advantage in posture recognition.

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Section: Related Workmentioning

confidence: 99%

Accelerometer Placement for Posture Recognition and Fall Detection

Gjoreski

Luštrek

Gams

2011

2011 Seventh International Conference on Intelligent Environments

185

112

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“…A postural change detection sensor module (PCDSM) was developed to investigate postural changes and movement patterns in ADL events and falls [11]. Bourke to be the best position for attaching the tri-axial accelerometer [12], whereas Kangas et al reported the waist to be the best position [13]. Other researchers too suggested the chest (or trunk)…”

Section: Itroductionmentioning

confidence: 99%

Discrimination of Fall and Fall-like ADL Using Tri-axial Accelerometer and Bi-axial Gyroscope

Park¹,

Kim²,

Baik³

et al. 2017

Journal of Sensor Science and Technology

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A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (ω res ), and angular variation (θ res ) were calculated using MATLAB. When the measured values of SVM_Acc, ω res , and θ res were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ω res was larger than 1.75 rad/s (TH2), and θ res was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ω res , and θ res were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences. In this study, a threshold-based fall recognition algorithm using Received: Jan. 19, 2017, Revised: Jun. 23, 2017, Accepted: Jan. 30, 2017 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License(http://creativecommons.org/ licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…A very well addressed sub-task of Health Condition is the fall detection in which accelerometers are seen as the suitable technology. [17], [11], [57], [73], [65], [55], [16], [74], [56], [130], [137], [119]; A B [82]; 2xA B [23]; G [18]; AG [64] Floor vibration-based sensor [4]; array of acoustic sensors [94]; Tilt sensor + piezoelectronic A + vibration sensor [83]; tri-axial MTx [134]; A T +G T [50], [127]; multiple A T [44]; A T +BAR [114] Kin [72], [90]; Cam [101], [7], [41] IR, pressure sensor, audio…”

Section: Physiological Health Condition and Healthcarementioning

confidence: 99%

Classification and suitability of sensing technologies for activity recognition

Woznowski

Kaleshi

Oikonomou

et al. 2016

Computer Communications

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Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply di↵erent AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a di↵erent angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for homebased AR algorithms, it highlights the problem of technology-driven cycle of development in this area. IntroductionIn the last two decades sensors have become cheaper, smaller and widely available, residing at the edge of the Internet. Some such examples are wearable personal activity (PA) trackers (e.g. Fitbit, Nike+ FuelBand, etc.). However, the available commercial o↵-the-shelf (COTS) sensors are only capable of 'sensing' a small subset of user activities -mostly outdoor sport activities (type of activity, distance covered, time taken, etc.) and estimation of additional information such as energy expenditure (either kcal or self-crafted metrics e.g. Nike's fuel-points). However, a large part of our lives, and increasingly so in the advanced age, is spent in the home, yet very little is known about our activities and behaviour in there.We are surrounded by a multitude of sensing devices and Mark Weiser's vision of ubiquitous computing [120] is starting to materialise in the advances made in embedded networked systems currently addressed as the Internet of Things (IoT). The significant increase in devices streaming low-level information over the Web presents many new challenges. Whilst many researchers present this as a big data challenge, we believe that many of the environments and applications will require to justify the value and process relatively small data, making this a two-faceted problem requiring to consider the highly distributed, non-interoperable, small and relatively "lonely" data. E cient and accurate activity recognition (AR) algorithms are needed in order to make sense of this data and provide useful/actionable information and services in the human...

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Determination of simple thresholds for accelerometry-based parameters for fall detection

Cited by 167 publications

References 16 publications

Accelerometer Placement for Posture Recognition and Fall Detection

Accelerometer Placement for Posture Recognition and Fall Detection

Discrimination of Fall and Fall-like ADL Using Tri-axial Accelerometer and Bi-axial Gyroscope

Classification and suitability of sensing technologies for activity recognition

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