Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM

Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection will dramatically improve the health outcomes of the older population and reduce the associated healthcare cost after a fall. To achieve the goal, a multi-features semi-supervised support vector machines (MFSS-SVM) algorithm utilizing measurements from structural floor vibration obtained through accelerometers is proposed in this study to detect falling events with limited labeled samples. In this MFSS-SVM algorithm, the peak value, energy, and correlation coefficient of the accelerometer signal are used as classification features. The performance of the proposed algorithm was validated with laboratory experiments among activities including falling, walking, free jumping, rhythmic jumping, bag dropping, and ball dropping. To further illustrate the performance of the algorithm, a benchmark database was adopted and expanded to test its ability to accurately identify falling, compared with the algorithm used in the benchmark study. Results show that by using the proposed algorithm, the falling events can be identified with high accuracy and confidence, even with small training datasets and test nodes.

Errataetall:

ErratumIn: Sensors (Basel). 2021 Jun 02;21(11):. - PMID 34199685

Medienart:

E-Artikel

Erscheinungsjahr:

2019

Erschienen:

2019

Enthalten in:

Zur Gesamtaufnahme - volume:19

Enthalten in:

Sensors (Basel, Switzerland) - 19(2019), 17 vom: 28. Aug.

Sprache:

Englisch

Beteiligte Personen:

Liu, Chengyin [VerfasserIn]
Jiang, Zhaoshuo [VerfasserIn]
Su, Xiangxiang [VerfasserIn]
Benzoni, Samuel [VerfasserIn]
Maxwell, Alec [VerfasserIn]

Links:

Volltext

Themen:

Benchmark problem
Fall loading model
Falling detection
Floor vibration
Journal Article
Multi-features semi-supervised support vector machines

Anmerkungen:

Date Completed 21.01.2020

Date Revised 02.07.2021

published: Electronic

ErratumIn: Sensors (Basel). 2021 Jun 02;21(11):. - PMID 34199685

Citation Status MEDLINE

doi:

10.3390/s19173720

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM300756607