Multi Group Based Daily Living Activity Recognition (DLAR) using Advanced Machine Learning Algorithm
Doreswamy1, Yogesh K M2
1Yogesh K M, Computer Science, Mangalore University, Mangalore, Karnataka, India.
2Doreswamy, Computer Science, Mangalore University, Mangalore, Karnataka, India.
Manuscript received on 21 September 2019 | Revised Manuscript received on 30 September 2019 | Manuscript Published on 01 October 2019 | PP: 62-69 | Volume-8 Issue-9S4 July 2019 | Retrieval Number: I11100789S419 /19©BEIESP | DOI: 10.35940/ijitee.I1110.0789S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human activity recognition (HAR) has realized more interest in several research communities given that understanding user activities and behavior help to deliver proactive and personalized services. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. Categorically, in this proposed work designs the three-level hierarchical classification structure, i.e., instance based, group and sub-group based and subject based to detect the daily activity of human body motion among activity groups. In correlation with other famous classifiers, for such as Random Forest Tree, J48, Decision Table, Multilayer Perceptron, NaïveBayes, oneR and REPTree (Reduced Error Pruning Tree), etc., thorough experiments on the mHealth dataset (Shimmer2 mHealth Data) demonstrate that group based classification achieves the best classification results, reaching RFT 99.97%. We trained classier in order to estimate accuracy classification based on (gender, age, height, and weight). We applied validation methods to the process, 10-fold cross-validation. For all three classification structure, we achieve high accuracy values for all three classification task.
Keywords: 3D-Accelerometer, Shimmer2, HAR, Wearable Sensor, Machine Learning.
Scope of the Article: Machine Learning