Benchmark problem for human activity identification using floor vibrations

Madarshahian, Ramin; Caicedo, Juan M.; Zambrana, Diego Arocha

doi:10.1016/j.eswa.2016.06.027

Cited by 19 publications

(8 citation statements)

References 91 publications

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“…Linear link elements were adopted to connect the steel beams to the concrete slab. The fundamental frequency of the FE floor model was 16.659 Hz, which closely matches (4.11% difference) with the 16.0 Hz measured from the experimental data reported by Madarshahian et al [13]. The floor vibration responses of the FE floor model at the sensor locations in the benchmark problem under the simulated human falling loads were utilized to expand the benchmark database.…”

Section: Comparison Study With the Benchmark Problemsupporting

confidence: 78%

“…To further investigate the performance of the proposed MFSS-SVM algorithm, a benchmark study by Madarshahian et al, [13] was adopted to serve as a baseline. Since falls were not part of the experiments in the benchmark database, it needs to be first expanded.…”

Section: Comparison Study With the Benchmark Problemmentioning

confidence: 99%

“…However, the use of cameras would meet with resistance as residents have a feeling of “being watched” [12]. 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]. For example, Madarshahian et al [13] proposed a benchmark problem to promote exploration of floor vibrations to classify human activities.…”

Section: Introductionmentioning

confidence: 99%

“…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]. For example, Madarshahian et al [13] proposed a benchmark problem to promote exploration of floor vibrations to classify human activities. The benchmark problem provided 16,100 records of floor vibrations induced by human activities including bouncing a ball, jumping, and dropping a bag and included an example solution with an algorithm based on the difference of the acceleration autocorrelations.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Jiang

et al. 2019

Sensors

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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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Section: Comparison Study With the Benchmark Problemsupporting

confidence: 78%

Section: Comparison Study With the Benchmark Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liu

Jiang

et al. 2019

Sensors

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“…Current solutions to the fall detection problem can be roughly divided into two categories: non-computer vision-based methods and computer vision-based methods: 5,6 (1) Non-computer vision-based methods. There are many non-computer vision-based methods of fall detection, such as sensitive floor tiles, 7 simple sensors, 8 and wearable sensors. [9][10][11] As falls cannot be detected at locations not equipped with specialized tiles, these tiles should be installed everywhere in the living room.…”

Section: Related Workmentioning

confidence: 99%

Fall detection via human posture representation and support vector machine

Fan

Wang

et al. 2017

International Journal of Distributed Sensor Networks

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Accidental falls of elderly people are a major cause of fatal injuries, especially for those living alone. We present a novel vision-based fall detection approach that analyzes an extracted human body using described human postures. First, a human body extracted by a background subtraction technique is located by a minimum area-enclosing ellipse. Then, a normalized directional histogram is developed around the center of the ellipse to represent a human posture by multidirectional statistical analysis. After that, 12 static and 8 dynamic features are derived from the normalized directional histogram. These features are fed into a directed acyclic graph support vector machine to distinguish four closely related human postures (standing, crouching, lying, and sitting). A fall-like accident is detected by counting the occurrences of lying postures in a short temporal window. After conducting majority voting, a fall event is determined by immobility verification. From the experimental results, an overall accuracy of 97.1% is obtained for recognition of the four postures, and only 1.0% of postures are misclassified as lying postures. Our fall detection system achieves up to 95.2% fall detection accuracy on a public fall dataset.

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Modeling Human-Structure Interaction Using Control Models When Bobbing on a Flexible Structure

Alzubaidi

Caicedo

2019

Conference Proceedings of the Society for Experimental Mechanics Series

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Benchmark problem for human activity identification using floor vibrations

Cited by 19 publications

References 91 publications

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

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

Fall detection via human posture representation and support vector machine

Modeling Human-Structure Interaction Using Control Models When Bobbing on a Flexible Structure

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