These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Inertial sensing-based pre-impact detection of falls involving near-fall scenarios.
    Author: Lee JK, Robinovitch SN, Park EJ.
    Journal: IEEE Trans Neural Syst Rehabil Eng; 2015 Mar; 23(2):258-66. PubMed ID: 25252283.
    Abstract:
    Although near-falls (or recoverable imbalances) are common episodes for many older adults, they have received a little attention and were not considered in the previous laboratory-based fall assessments. Hence, this paper addresses near-fall scenarios in addition to the typical falls and activities of daily living (ADLs). First, a novel vertical velocity-based pre-impact fall detection method using a wearable inertial sensor is proposed. Second, to investigate the effect of near-fall conditions on the detection performance and feasibility of the vertical velocity as a fall detection parameter, the detection performance of the proposed method (Method 1) is evaluated by comparing it to that of an acceleration-based method (Method 2) for the following two different discrimination cases: falls versus ADLs (i.e., excluding near-falls) and falls versus non-falls (i.e., including near-falls). Our experiment results show that both methods produce similar accuracies for the fall versus ADL detection case; however, Method 1 exhibits a much higher accuracy than Method 2 for the fall versus non-fall detection case. This result demonstrates the superiority of the vertical velocity over the peak acceleration as a fall detection parameter when the near-fall conditions are included in the non-fall category, in addition to its capability of detecting pre-impact falls.
    [Abstract] [Full Text] [Related] [New Search]