首页|Researchers’ from Department of Computer Science and Engineering Report Details of New Studies and Findings in the Area of Machine Learning (CAGSI: A Classifica tion Approach towards Gait Speed Identification)
Researchers’ from Department of Computer Science and Engineering Report Details of New Studies and Findings in the Area of Machine Learning (CAGSI: A Classifica tion Approach towards Gait Speed Identification)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting originating from the Departme nt of Computer Science and Engineering by NewsRx correspondents, research stated , “The last few decades have witnessed a remarkable amount of research addressin g numerous challenges in the domain of human activity recognition. One popular p roblem in this domain has been that of gait analysis.” Our news editors obtained a quote from the research from Department of Computer Science and Engineering: “A subproblem in this domain is to identify the speed o f a mobile object through gait analysis. Apart from clinical diagnostic applicat ions, the detection of the speed of a person is also important in remote health monitoring, tracking of the mentally incompetent, and determining proper ambulat ory assistive devices for the orthopaedically impaired. Gait analysis-related pr oblems commonly deal with large volumes of interrelated data for which machine-l earning techniques have been proven effective. However, the size of the feature set used in such problems is a crucial factor. The choice of a large feature set may complicate the approach for long-term analysis. The present work addresses the problem of human walking speed classification through the machine learning a pproach. Data was experimentally collected with the mobile phone sensors carried by volunteers of different physiques. Only the acceleration readings along the three axes of the accelerometer are considered for further experimentation. Alth ough walking speed is a personal trait, four classes of data have been curated, namely, slow walking, moderate walking, fast walking, and sitting. The speeds of the walks were not pre-defined so the volunteers performed the walks as per the ir own comfort, which enhances the challenge of distinguishing between sensor si gnals of varying speed. Experiments have been performed using different supervis ed learning algorithms with only acceleration data. The performance of the learn ing models has been analyzed with the help of accuracy, precision, recall, f1-sc ore, and the ROC curve in a One-vs-Rest approach.”
Department of Computer Science and Engin eeringCyborgsEmerging TechnologiesMachine Learning
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