| Barometric pressure and triaxial accelerometry-based falls event detection. | |
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MedLine Citation:
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PMID: 20805056 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a barometric pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population. |
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Authors:
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Federico Bianchi; Stephen J Redmond; Michael R Narayanan; Sergio Cerutti; Nigel H Lovell |
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Publication Detail:
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Type: Journal Article Date: 2010-08-30 |
Journal Detail:
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Title: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Volume: 18 ISSN: 1558-0210 ISO Abbreviation: IEEE Trans Neural Syst Rehabil Eng Publication Date: 2010 Dec |
Date Detail:
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Created Date: 2010-12-08 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101097023 Medline TA: IEEE Trans Neural Syst Rehabil Eng Country: United States |
Other Details:
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Languages: eng Pagination: 619-27 Citation Subset: IM |
Affiliation:
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Department of Biomedical Engineering,Politecnico di Milano, 20133 Milan, Italy. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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