A dataset for the development and optimization of fall detection algorithms based on wearable sensors

This paper describes a dataset acquired on 8 subjects while simulating 13 types of falls and 5 types of Activities of Daily Living (ADL), each repeated 3 times. In details, data includes 4 simulated falls forward (falling on knees ending up lying, ending in lateral position, ending up lying, ending...

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Vydané v:Data in brief Ročník 23; s. 103839
Hlavní autori: Cotechini, Valentina, Belli, Alberto, Palma, Lorenzo, Morettini, Micaela, Burattini, Laura, Pierleoni, Paola
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Netherlands Elsevier Inc 01.04.2019
Elsevier
Predmet:
data collection
Engineering
Fall detection
Human fall
MARG (Magnetic Angular Rate and Gravity) sensor
syncope
waist
Wearable device
Wearable device
Human fall
Fall detection
MARG (Magnetic Angular Rate and Gravity) sensor
ISSN:2352-3409, 2352-3409
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Shrnutí:This paper describes a dataset acquired on 8 subjects while simulating 13 types of falls and 5 types of Activities of Daily Living (ADL), each repeated 3 times. In details, data includes 4 simulated falls forward (falling on knees ending up lying, ending in lateral position, ending up lying, ending up lying with recovery), 4 backward (falling sitting ending up lying, ending in lateral position, ending up lying, ending up lying with recovery), 2 lateral right (ending up lying, ending up lying with recovery), 2 lateral left (ending up lying, ending up lying with recovery), and 1 syncope. Simulated ADL are: lying on a bed then standing; walking a few meters; sitting on a chair then standing; go up or down three steps; and standing after picking something. Data were acquired using a MARG sensor, a wearable multisensory device tied to the subject's waist, that recorded time-variations of the subject's acceleration and orientation (expressed through the yaw, pitch and roll angles). These data can be useful in the development and test of algorithms to automatically identify and classify fall events. Fall detection systems are particularly useful when a subject is alone and not able to stand up after a fall, since an automatic alarm can be sent remotely to receive proper help.
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content type line 23
ISSN:2352-3409
2352-3409
DOI:10.1016/j.dib.2019.103839