Falling monitoring system based on multi axial accelerometer

Li, Wentao; Luo, Yang; Yan, Junze; Tao, Chunjing; Ma, Lifang

doi:10.1109/wcica.2014.7052678

Cited by 1 publication

(1 citation statement)

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“…Some research studies have used wearable sensors [ 1 – 3 ], mobile phones [ 4 – 7 ], and smart watches [ 8 ] for recognizing different actions. In some research, the focus was on detection of abnormal actions, such as falling [ 9 – 11 ], or on reporting status for both normal and abnormal situations [ 12 ]. To recognize complex actions, moreover, several sensors must be embedded at different positions on the body.…”

Section: Introductionmentioning

confidence: 99%

Multiview Layer Fusion Model for Action Recognition Using RGBD Images

Chalearnnetkul

Suvonvorn

2018

Computational Intelligence and Neuroscience

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Vision-based action recognition encounters different challenges in practice, including recognition of the subject from any viewpoint, processing of data in real time, and offering privacy in a real-world setting. Even recognizing profile-based human actions, a subset of vision-based action recognition, is a considerable challenge in computer vision which forms the basis for an understanding of complex actions, activities, and behaviors, especially in healthcare applications and video surveillance systems. Accordingly, we introduce a novel method to construct a layer feature model for a profile-based solution that allows the fusion of features for multiview depth images. This model enables recognition from several viewpoints with low complexity at a real-time running speed of 63 fps for four profile-based actions: standing/walking, sitting, stooping, and lying. The experiment using the Northwestern-UCLA 3D dataset resulted in an average precision of 86.40%. With the i3DPost dataset, the experiment achieved an average precision of 93.00%. With the PSU multiview profile-based action dataset, a new dataset for multiple viewpoints which provides profile-based action RGBD images built by our group, we achieved an average precision of 99.31%.

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