Small Motion Detection and Non-Contact Vital Signs Monitoring with Continuous Wave Doppler Radars

Seflek, Ibrahim; Acar, Yunus Emre; Yaldiz, Ercan

doi:10.5755/j01.eie.26.3.25810

Cited by 12 publications

(2 citation statements)

References 16 publications

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“…Here, crw 1 and crw 2 , crt 1 and crr 2 are the classification success of walking and turning movements in the first and second steps, respectively, and sw and sr are the sample numbers in walking and turning. In addition to the approximately 6% improvement in the classification success of this method, the most important point is its effect on the classification of rotational movements with little data.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The precise monitoring and classification of human movements have garnered immense importance across an array of critical domains, ranging from child and elderly care to the detection of dangerous situations and the tracking of sequential tasks [1][2][3]. In these spheres, the meticulous analysis of people's movements, coupled with continuous observation, facilitates invaluable follow-ups such as health status assessments, organ functionality evaluations, and the identification of potentially harmful behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Human Movement Classification in Imbalanced Data with a Hybrid AI Approach

2023

IJANSER

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The monitoring and classification of human movements have garnered significant importance across various domains, including hazardous situation detection and elderly care. In recent years, the application of Artificial Intelligence (AI) has emerged as a promising approach to predicting and analyzing human movements in such contexts. However, as the complexity and diversity of target movements increase, obtaining sufficient and high-quality data for AI methods becomes increasingly challenging. A particular obstacle researchers and practitioners encounter is the uneven distribution of movement data, where certain movements are frequently repeated and well-represented in the dataset, while others occur infrequently, leading to an imbalanced dataset. This data imbalance poses a critical issue as it hampers the effectiveness of AI models, particularly for those movements with limited available data. The scarcity of samples for certain classes can lead to biased learning, resulting in reduced accuracy and poor generalization of the model. To address this critical challenge, this research proposes a hybrid AI methodology specifically designed to enhance the classification accuracy of two important human movements: turning and walking. The experimental results demonstrate the efficacy of the proposed method. It significantly improves the overall classification accuracy compared to only one AI technique, especially for those movements characterized by limited data availability. By effectively addressing the data imbalance problem, the hybrid AI methodology ensures that the model's performance is not disproportionately skewed towards the overrepresented classes, yielding a more balanced and reliable human movement monitoring and classification system.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%