Gender Classification Using Under Floor Vibration Measurements

Bales, Dustin; Tarazaga, Pablo A.; Kasarda, Mary; Batra, Dhruv

doi:10.1007/978-3-319-29763-7_37

Cited by 7 publications

(1 citation statement)

References 11 publications

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“…A large amount of research has been focusing on feature extraction and information learning for different vibration-based applications (Dobbler et al, 2014;Mirshekari et al, 2015Mirshekari et al, , 2016Pan et al, 2015Pan et al, , 2016Bales et al, 2016b). However, if the raw signals acquired are already distorted (signal clipping) or of low resolution, the learning can hardly compensate for such information loss.…”

Section: Introductionmentioning

confidence: 99%

Collaboratively Adaptive Vibration Sensing System for High-fidelity Monitoring of Structural Responses Induced by Pedestrians

Pan

Mirshekari

et al. 2017

Front. Built Environ.

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This paper presents a collaboratively adaptive vibration monitoring system that captures high-fidelity structural vibration signals induced by pedestrians. These signals can be used for various human activities' monitoring by inferring information about the impact sources, such as pedestrian footsteps, door opening and closing, and dragging objects. Such applications often require high-fidelity (high resolution and low distortion) signals. Traditionally, expensive high resolution and high dynamic range sensors are adopted to ensure sufficient resolution. However, for sensing systems that use low-cost sensing devices, the resolution and dynamic range are often limited; hence this type of sensing methods is not well explored ubiquitously. We propose a low-cost sensing system that utilizes (1) a heuristic model of the investigating excitations and (2) shared information through networked devices to adapt hardware configurations and obtain high-fidelity structural vibration signals. To further explain the system, we use indoor pedestrian footstep sensing through ambient structural vibration as an example to demonstrate the system performance. We evaluate the application with three metrics that measure the signal quality from different aspects: the sufficient resolution rate to present signal resolution improvement without clipping, the clipping rate to measure the distortion of the footstep signal, and the signal magnitude to quantify the detailed resolution of the detected footstep signal. In experiments conducted in a school building, our system demonstrated up to 2× increase on the sufficient resolution rate and 2× less error rate when used to locate the pedestrians as they walk along the hallway, compared to a fixed sensing setting.

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