An acoustic fall detector system that uses sound height information to reduce the false alarm rate

Popescu, Mihail; Li, Yun; Skubic, Marjorie; Rantz, Marilyn

doi:10.1109/iembs.2008.4650244

Cited by 149 publications

(72 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, many studies consider fall with limited fall tasks, limited fall actions and limited volunteers while against to a specific fall scenario for evaluation, which always brings out many phenomena of false alarms or alarm omission. In addition, some of the previous studies use various sensor arrays such as vibration [6] and acoustic [7], as well as different processing technologies such as image processing [8] and pattern recognition [9] to detect fall, but all of these methods have high cost, high power consumption and large computation. Furthermore, few systems refer to the energy-efficiency issue, which affects the usage of the system.…”

Section: Introductionmentioning

confidence: 99%

Low-power fall detection in home-based environments

Ren

Zhang

Shi

2012

Proceedings of the 2nd ACM International Workshop on Pervasive Wireless Healthcare

View full text Add to dashboard Cite

Fall detection of the elderly becomes more critical in an aging society. However, how to put forward fall detection with reliability and high accuracy while maintaining real-time and energy-efficiency is an important issue. To this end, we design and implement an energy-efficient prototype called Asgard, in which a fall detection algorithm and a hybrid energy-efficient strategy are proposed. The algorithm, which can flexibly track the body change by recovery angle detection, helps to reduce the false positive phenomenon as well as detection time (DT). Results of comprehensive evaluations show the accuracy rate of 96.25%, which is higher than AMD (Advanced Magnitude Detection). More notably, the prototype still has low DT with the aforementioned accuracy. More precisely, with the proposed hybrid energy-efficient algorithm, Asgard functions well for approximately one month using only two AA batteries (1500mAH each).

show abstract

Section: Introductionmentioning

confidence: 99%

Low-power fall detection in home-based environments

Ren

Zhang

Shi

2012

Proceedings of the 2nd ACM International Workshop on Pervasive Wireless Healthcare

View full text Add to dashboard Cite

show abstract

“…[15,16] A system consisting of a circular microphone array detects falls by capturing the sound, generated during falls. It correctly locates the source of falling sound in attempt to reduce false alarms, which also makes the system robust under noisy situations.…”

Section: Rationalementioning

confidence: 99%

P1‐429: Adding Intelligence to a Floor‐Based Array Personnel Detector

Tyrer

Muheidat

2016

Alzheimer's & Dementia

View full text Add to dashboard Cite

“…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

View full text Add to dashboard Cite

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...

show abstract

An acoustic fall detector system that uses sound height information to reduce the false alarm rate

Cited by 149 publications

References 13 publications

Low-power fall detection in home-based environments

Low-power fall detection in home-based environments

P1‐429: Adding Intelligence to a Floor‐Based Array Personnel Detector

Classification and suitability of sensing technologies for activity recognition

Contact Info

Product

Resources

About