Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

Li, Qiang; Stankovic, John A.; Hanson, Mark; Barth, Adam T.; Lach, John; Zhou, Gang

doi:10.1109/bsn.2009.46

Cited by 451 publications

(265 citation statements)

References 13 publications

(23 reference statements)

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“…Of particular interest to us is the work in [5]. They used two 3-axis accelerometers and gyroscopes worn on the chest and thigh; by applying thresholds to accelerations, angular velocities and angles, they detected a potential fall and the activity/posture after the fall, resulting in 90.1% accurate fall detection.…”

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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“…In the case of falls, it could give false positives if the device is thrown on a surface or when the user performs actions involving high accelerations followed by an inactivity period (let yourself fall on a chair or a sofa). This approach is not really bad because it reduces the number of the false negatives especially when the most critical activity is the falling [56,57].…”

Section: Discussionmentioning

confidence: 99%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

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a b s t r a c tCurrently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.

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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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Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

Cited by 451 publications

References 13 publications

Accelerometer Placement for Posture Recognition and Fall Detection

Accelerometer Placement for Posture Recognition and Fall Detection

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Classification and suitability of sensing technologies for activity recognition

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