Literature DB >> 19163747

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

Mihail Popescu1, Yun Li, Marjorie Skubic, Marilyn Rantz.   

Abstract

More than one third of about 38 million adults 65 and older fall each year in the United States. To address the above problem we propose to develop an acoustic fall detection system (FADE) that will automatically signal a fall to the monitoring caregiver. As opposed to many existent fall detection systems that require the monitored person to wear devices such as accelerometers or gyroscopes at all times, our system is completely unobtrusive by not requiring any wearable devices. To reduce the false alarm rate we employ an array of acoustic sensors to obtain sound source height information. The sound is considered a false alarm if it comes from a source located at a height higher than 2 feet. We tested our system in a pilot study that consisted of a set of 23 falls performed by a stunt actor during six sessions of about 15 minutes each (1.3 hours in total). The actor was previously trained by our nursing collaborators to fall like an elderly person. The use of height information reduced the false alarm hourly rate from 32 to 5 at a 100% fall detection rate.

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Year:  2008        PMID: 19163747     DOI: 10.1109/IEMBS.2008.4650244

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  10 in total

1.  Smartphone-based solutions for fall detection and prevention: the FARSEEING approach.

Authors:  S Mellone; C Tacconi; L Schwickert; J Klenk; C Becker; L Chiari
Journal:  Z Gerontol Geriatr       Date:  2012-12       Impact factor: 1.281

Review 2.  Fall detection devices and their use with older adults: a systematic review.

Authors:  Shomir Chaudhuri; Hilaire Thompson; George Demiris
Journal:  J Geriatr Phys Ther       Date:  2014 Oct-Dec       Impact factor: 3.381

3.  A Behaviour Monitoring System (BMS) for Ambient Assisted Living.

Authors:  Samih Eisa; Adriano Moreira
Journal:  Sensors (Basel)       Date:  2017-08-24       Impact factor: 3.576

Review 4.  Automatic fall monitoring: a review.

Authors:  Natthapon Pannurat; Surapa Thiemjarus; Ekawit Nantajeewarawat
Journal:  Sensors (Basel)       Date:  2014-07-18       Impact factor: 3.576

Review 5.  Smart Homes for Elderly Healthcare-Recent Advances and Research Challenges.

Authors:  Sumit Majumder; Emad Aghayi; Moein Noferesti; Hamidreza Memarzadeh-Tehran; Tapas Mondal; Zhibo Pang; M Jamal Deen
Journal:  Sensors (Basel)       Date:  2017-10-31       Impact factor: 3.576

6.  An Energy-Efficient Multi-Tier Architecture for Fall Detection Using Smartphones.

Authors:  M Amac Guvensan; A Oguz Kansiz; N Cihan Camgoz; H Irem Turkmen; A Gokhan Yavuz; M Elif Karsligil
Journal:  Sensors (Basel)       Date:  2017-06-23       Impact factor: 3.576

Review 7.  Ambient Sensors for Elderly Care and Independent Living: A Survey.

Authors:  Md Zia Uddin; Weria Khaksar; Jim Torresen
Journal:  Sensors (Basel)       Date:  2018-06-25       Impact factor: 3.576

8.  Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality?

Authors:  Marion Pech; Helene Sauzeon; Helene Amieva; Thinhinane Yebda; Jenny Benois-Pineau
Journal:  JMIR Aging       Date:  2021-12-09

9.  Fall Detection Using Multiple Bioradars and Convolutional Neural Networks.

Authors:  Lesya Anishchenko; Andrey Zhuravlev; Margarita Chizh
Journal:  Sensors (Basel)       Date:  2019-12-17       Impact factor: 3.576

10.  NT-FDS-A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices.

Authors:  Marvi Waheed; Hammad Afzal; Khawir Mehmood
Journal:  Sensors (Basel)       Date:  2021-03-12       Impact factor: 3.576

  10 in total

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