Human Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review.

TitreHuman Fall Detection Using Passive Infrared Sensors with Low Resolution: A Systematic Review.
Publication TypeJournal Article
Year of Publication2022
AuthorsBen-Sadoun G, Michel E, Annweiler C, Sacco G
JournalClin Interv Aging
Date Published2022
Mots-clésAccidental Falls, Aged, Algorithms, Humans

Systems using passive infrared sensors with a low resolution were recently proposed to answer the dilemma effectiveness-ethical considerations for human fall detection by Information and Communication Technologies (ICTs) in older adults. How effective is this type of system? We performed a systematic review to identify studies that investigated the metrological qualities of passive infrared sensors with a maximum resolution of 16×16 pixels to identify falls. The search was conducted on PubMed, ScienceDirect, SpringerLink, IEEE Xplore Digital Library, and MDPI until November 26-28, 2020. We focused on studies testing only these types of sensor. Thirteen articles were "conference papers", five were "original articles" and one was a found in (an open access repository of scientific research). Since four authors "duplicated" their study in two different journals, our review finally analyzed 15 studies. The studies were very heterogeneous with regard to experimental procedures and detection methods, which made it difficult to draw formal conclusions. All studies tested their systems in controlled conditions, mostly in empty rooms. Except for two studies, the overall performance reported for the detection of falls exceeded 85-90% of accuracy, precision, sensitivity or specificity. Systems using two or more sensors and particular detection methods (eg, 3D CNN, CNN with 10-fold cross-validation, LSTM with CNN, LSTM and Voting algorithms) seemed to give the highest levels of performance (> 90%). Future studies should test more this type of system in real-life conditions.

Alternate JournalClin Interv Aging
PubMed ID35046646
PubMed Central IDPMC8763199