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.
Highlights
The reinforced concrete (RC) beams in the floor were explicitly simulation and the experimental testing of the typical floor vibration time-history are shown in Figure modeled with 1D elements, while the walls and columns were represented by constraints
An algorithm based on the difference of the acceleration autocorrelations was used to classify the various events
The sensitivity criterion intends to measure the percentage of actual falling that are correctly identified as falling events while the specificity evaluates the percentage of non-falling events that are correctly identified as daily activities
Summary
The user-dependent methods either use wearable devices (e.g., pendant, watch, clip, bracelet, or ring) to track rapid changes in its orientation or rely on users themselves to report the emergency of fall by interacting (e.g., pressing) the wearable device [4,5,6,7,8,9] These options suffer from limitations such as getting the person to wear the device or false-positive detection (e.g., a fall is incorrectly identified when no fall occurs) due to user’s abrupt movement. Progress toward new methods without cameras has been made through the use of accelerometer sensors mounted on the structure to capture floor vibration for human activities detection [13,14,15]. A multi-features semi-supervised support vector machines (MFSS-SVM) algorithm with a radial basis function kernel is proposed to identify falling events through the use of accelerometer measurements from floor vibration. The performance of the proposed algorithm was investigated with laboratory testing and through an expanded benchmark database
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