PlumX Metrics
Embed PlumX Metrics

Comparative Study of Computational Techniques for Smartphone Based Human Activity Recognition

Lecture Notes in Electrical Engineering, ISSN: 1876-1119, Vol: 778, Page: 427-439
2021
  • 0
    Citations
  • 0
    Usage
  • 4
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

Conference Paper Description

Human activity recognition (HAR) has been popular because of its diverse applications in the field of health care, geriatrics care, the security of women and children, and many more. With the advancement in technology, the traditional sensors are replaced by smartphones. The mobile inbuild accelerometer detects the orientation or acceleration, and the gyroscope detects the angular rotational velocity. In this study, computational techniques-based comparative analysis has been carried out on publicly available dataset on human activity recognition using smartphone dataset. Traditional and contemporary computational techniques (support vector machine, decision tree, random forest, multi-layer perceptron, CNN, LSTM, and CNN-LSTM) for HAR are explored in this study to compare each model’s accuracy to classify a particular human activity. Support vector machine outperforms in most of the activity recognition tasks.

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know