Estimation of Human Body Vital Signs Based on 60 GHz Doppler Radar Using a Bound-Constrained Optimization Algorithm

In this study, a bound-constrained optimization algorithm is applied for estimating physiological data (pulse and breathing rate) of human body using 60 GHz Doppler radar, by detecting displacements induced by breathing and the heartbeat of a human subject. The influence of mutual phasing between the two movements is analyzed in a theoretical framework and the application of optimization algorithms is proved to be able to accurately detect both breathing and heartbeat rates, despite intermodulation effects between them. Different optimization procedures are compared and shown to be more robust to receiver noise and artifacts of random body motion than a direct spectrum analysis. In case of a large-scale constrained bound, a parallel optimization procedure executed in subranges is proposed to realize accurate detection in a reduced span of time.

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Large-scale bound constrained optimization based on hybrid teaching learning optimization algorithm

Alexandria Engineering Journal ◽

10.1016/j.aej.2021.04.002 ◽

2021 ◽

Vol 60 (6) ◽

pp. 6013-6033

Author(s):

Wali Khan Mashwani ◽

Habib Shah ◽

Manjit Kaur ◽

Maharani Abu Bakar ◽

Miftahuddin Miftahuddin

Keyword(s):

Constrained Optimization ◽

Optimization Algorithm ◽

Large Scale ◽

Hybrid Teaching ◽

Bound Constrained Optimization ◽

Teaching Learning

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UCF-STAR: A Large Scale Still Image Dataset for Understanding Human Actions

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i03.5653 ◽

2020 ◽

Vol 34 (03) ◽

pp. 2677-2684

Author(s):

Marjaneh Safaei ◽

Pooyan Balouchian ◽

Hassan Foroosh

Keyword(s):

Action Recognition ◽

Human Body ◽

Large Scale ◽

Temporal Information ◽

Training Data ◽

Body Motion ◽

Human Actions ◽

Still Images ◽

Still Image ◽

Novel Approach

Action recognition in still images poses a great challenge due to (i) fewer available training data, (ii) absence of temporal information. To address the first challenge, we introduce a dataset for STill image Action Recognition (STAR), containing over $1M$ images across 50 different human body-motion action categories. UCF-STAR is the largest dataset in the literature for action recognition in still images. The key characteristics of UCF-STAR include (1) focusing on human body-motion rather than relatively static human-object interaction categories, (2) collecting images from the wild to benefit from a varied set of action representations, (3) appending multiple human-annotated labels per image rather than just the action label, and (4) inclusion of rich, structured and multi-modal set of metadata for each image. This departs from existing datasets, which typically provide single annotation in a smaller number of images and categories, with no metadata. UCF-STAR exposes the intrinsic difficulty of action recognition through its realistic scene and action complexity. To benchmark and demonstrate the benefits of UCF-STAR as a large-scale dataset, and to show the role of “latent” motion information in recognizing human actions in still images, we present a novel approach relying on predicting temporal information, yielding higher accuracy on 5 widely-used datasets.

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