Universiti Teknologi Malaysia Institutional Repository

Human activity and posture classification using smartphone sensors and matlab mobile

Jamian, Syahirah and Gunawan, Teddy Surya and Kartiwi, Mira and Ahmad, Robiah and Abdul Kadir, Kushsairy and Nordin, Muhammad Noor (2022) Human activity and posture classification using smartphone sensors and matlab mobile. In: 2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022, 16 - 19 May 2022, Ottawa, Canada.

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Official URL: http://dx.doi.org/10.1109/I2MTC48687.2022.9806551


Human Activity Recognition (HAR) is significant, especially in the medical field. Activity recognition has been used in various ways as technology has advanced, particularly using a smartphone-based approach. This work aims to evaluate the accuracy of the triaxial accelerometer in the Matlab Mobile and examine the development and performance of the algorithms in identifying human motions on individuals of similar ages and physical appearances. Motion signals from three subjects are measured, data is preprocessed using a filtering technique, features are extracted, feature normalization is used to reduce bias in data measurement, and activities are classified. Confusion matrix, precision, recall, accuracy, F1-score, and Kappa score are performance indicators used to determine this classification approach. As a result, this research discovered that the Quadratic Support Vector Machine (SVM) produces the best results, with a 99.22 % accuracy rate, proving the efficacy of its activity identification method.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:human activity recognition, posture recognition, smartphone sensors, SVM classifier
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:98854
Deposited By: Widya Wahid
Deposited On:02 Feb 2023 17:44
Last Modified:02 Feb 2023 17:44

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