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. 2019 Aug 28;19(17):3720.
doi: 10.3390/s19173720.

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

Chengyin Liu  1 , Zhaoshuo Jiang  2 , Xiangxiang Su  1 , Samuel Benzoni  3 , Alec Maxwell  3
Affiliations

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

Chengyin Liu et al. Sensors (Basel). .

Erratum in

Abstract

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.

Keywords: benchmark problem; fall loading model; falling detection; floor vibration; multi-features semi-supervised support vector machines.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The proposed multi-features semi-supervised support vector machines (MFSS-SVM) framework.
Figure 2
Figure 2
Intelligent structural hazard mitigation laboratory (iSHM) Lab at the San Francisco State University (Left: Picture; Right: 3D Model).
Figure 3
Figure 3
Sensor and excitation position arrangement in experiment (unit: m).
Figure 4
Figure 4
Experiment Setup.
Figure 5
Figure 5
Dummy weighing 75 kg falls.
Figure 6
Figure 6
Dummy weighing 48 kg falls.
Figure 7
Figure 7
Volunteer weights 69 kg jumping.
Figure 8
Figure 8
Volunteers (78 kg and 69 kg) walking.
Figure 9
Figure 9
Ball (0.6 kg) drops.
Figure 10
Figure 10
Bag (5 kg and 10 kg) drops.
Figure 11
Figure 11
Missing report rate and misreporting rate of falling events.
Figure 12
Figure 12
Misreporting rate of SVM-D-R method for non-fall events.
Figure 13
Figure 13
Sensor and impact locations in benchmark experiment (unit: m) [12].
Figure 14
Figure 14
Floor force during the process of falling [46].
Figure 15
Figure 15
Model of a human body under vertical vibration [46].
Figure 16
Figure 16
Floor force during the process of human falling (weight 71 kg).
Figure 17
Figure 17
Finite element (FE) model of experimental laboratory floor.
Figure 18
Figure 18
Comparison of simulated acceleration and experimental acceleration time history.
Figure 19
Figure 19
FE model of benchmark laboratory floor.
See this image and copyright information in PMC

References

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