IoT 18: e1

Research Article

Hybrid Machine Learning Techniques to detect Real Time Human Activity using UCI Dataset

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  • @ARTICLE{10.4108/eai.26-5-2021.170006,
        author={Muhammad Arshad and Fawwad Hassan Jaskani and Muhammad Ayub Sabri and Fatima Ashraf and Muhammad Farhan and Maria Sadiq and Hammad Raza},
        title={Hybrid Machine Learning Techniques to detect Real Time Human Activity using UCI Dataset},
        journal={EAI Endorsed Transactions on Internet of Things: Online First},
        keywords={Machine Learning, KNN, SVM, Human Activity Recognition},
  • Muhammad Arshad
    Fawwad Hassan Jaskani
    Muhammad Ayub Sabri
    Fatima Ashraf
    Muhammad Farhan
    Maria Sadiq
    Hammad Raza
    Year: 2021
    Hybrid Machine Learning Techniques to detect Real Time Human Activity using UCI Dataset
    DOI: 10.4108/eai.26-5-2021.170006
Muhammad Arshad1, Fawwad Hassan Jaskani2,*, Muhammad Ayub Sabri1, Fatima Ashraf1, Muhammad Farhan3, Maria Sadiq3, Hammad Raza2
  • 1: Department of Computer Science & IT, Government College University, Faisalabad, Pakistan
  • 2: Department of Computer Systems Engineering, Islamia University of Bahawalpur, Pakistan
  • 3: Department of Computer Science & IT, Islamia University of Bahawalpur, Pakista
*Contact email:


The cell phone is assuming a crucial job in present day life. It offers types of assistance and applications, for example, location tracking, medical applications, and human activity examination. All android smartphones have motion sensors i.e. Accelerometer, gyroscope, in order to detect motion of a user in a very precise way. In early conditions, committed sensors were utilized for activity acknowledgment. Different techniques are developed for distinguishing normal or human activities scenes in the crowd by processing the video or an image. A novel KNN-SVM human activity detection method is proposed to detect human activities in the UCI dataset for complex multi-process physical activities. Model trained with machine learning algorithms to capture the temporal dependency, normal sequences with high dimension is uniformly utilized to train the model to discriminate each activity. In the classification process, 2 different efficient classifiers are applied to identify the types of human activities in the UCI dataset. Support Vector Machine and K-Nearest Neighbour are applied in the proposed method for the classification. The efficiency of each classifiers is about 85% to 87%. The classification efficiency is comparable with existing literature after applying the majority decision in these classification techniques.