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• W4313307056 abstract "Revolutions in physical locomotion detection have aided in living a protected and effortless life. Physical locomotion can be referred to as both indoor and outdoor human motion. However, the applications of physical locomotion are not limited to smart homes or smart societies and expand over the security systems, healthcare gadgets, sensing devices, and motion detection tactics. For this purpose, multiple sensors support attaining comprehensive learning of the physical locomotion in order to avoid confidentiality violations. In this paper, a model has been recommended to contribute toward muscles monitoring during exercising for locomotion detection. The objective of this research is to assist in recognizing locomotion based on a pre-classification technique for patterns identification, which includes static and kinematic patterns cognition. The proposed system consists of different phases including data acquisition, filtering, windowing, pre- classification, cues extraction, cues optimization, and locomotion detection. Experimental outcomes of the research performed indicated that the proposed locomotion detection model has performed reliably for both simple and complex physical locomotion patterns by achieving 92.33% accuracy." @default.
• W4313307056 created "2023-01-06" @default.
• W4313307056 creator A5021881763 @default.
• W4313307056 creator A5089511340 @default.
• W4313307056 date "2022-08-16" @default.
• W4313307056 modified "2023-09-28" @default.
• W4313307056 title "Physical Locomotion Detection using pre- classification for Muscles monitoring" @default.
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• W4313307056 doi "https://doi.org/10.1109/ibcast54850.2022.9990142" @default.
• W4313307056 hasPublicationYear "2022" @default.
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