IJIRST (International Journal for Innovative Research in Science & Technology)ISSN (online) : 2349-6010

 International Journal for Innovative Research in Science & Technology

Human Activity Recognition on Smartphones using Machine Learning Algorithms


Print Email Cite
International Journal for Innovative Research in Science & Technology
Volume 5 Issue - 6
Year of Publication : 2018
Authors : Sandeep Kumar Polu

BibTeX:

@article{IJIRSTV5I6018,
     title={Human Activity Recognition on Smartphones using Machine Learning Algorithms},
     author={Sandeep Kumar Polu},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={5},
     number={6},
     pages={31--37},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV5I6018.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}



Abstract:

Activity Recognition is one among the most imperative period at the back of various applications like human survey system, clinical investigation and it is a functioning examination subject in smart homes and smart health. Smart mobiles are outfitted with various worked in detecting sensors like a gyroscope, accelerometer, GPS sensor and compass sensor. We can structure a device to catch the condition of the user. Activity Recognition (AR) framework takes the unrefined sensor data from compact sensors as sources of information and assessments a human movement using data mining and machine learning systems. In this paper, we examine the execution of two sort calculations i.e. Random Forest (RF) and Modified Random Forest (MRF) in an online Activity Recognition framework running on Android frameworks and this technique can underpin online training and class the utilization of the accelerometer data most successfully. For the most part, first, we utilize the Random Forest classification algorithm related next we tend to use an improvement of Modified Random Forest i.e. MRF. For the rationale of Activity Recognition, Modified Random Forest will expel the computational complexities of the Random Forest through developing decision trees (creating littler preparing units for each activity and class may be done dependent on those diminished preparing sets). We will expect the general execution of these classifiers from a movement of observations on human movements like sitting, walking, running, resting and standing in an online activity recognition contraption. On this paper, we're proposed to break down the general execution of classifiers with constrained preparing records and confined open memory on the smart devices contrasted with offline.


Keywords:

Human Activity Recognition, Smartphone sensors, Machine Learning, Random Forest, Modified Random Forest


Download Article