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 |
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Medienart: |
E-Artikel |
Erscheinungsjahr: |
2019 |
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Erschienen: |
2019 |
Enthalten in: |
Zur Gesamtaufnahme - volume:19 |
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Enthalten in: |
Sensors (Basel, Switzerland) - 19(2019), 17 vom: 28. Aug. |
Sprache: |
Englisch |
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Beteiligte Personen: |
Liu, Chengyin [VerfasserIn] |
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Links: |
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Themen: |
Benchmark problem |
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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 |
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doi: |
10.3390/s19173720 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM300756607 |
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520 | |a 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 | ||
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700 | 1 | |a Benzoni, Samuel |e verfasserin |4 aut | |
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