Human Activity Recognition Using Smartphones

By Erhan Bulbul, Aydın Çetin, Ibrahim Alper Dogru. 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2018

295 citations recorded by the source corpus.

Human Activity Recognition(HAR) is classifying activity of a person using responsive sensors that are affected from human movement. Both users and capabilities(sensors) of smartphones increase and users usually carry their smartphone with them. These facts makes HAR more important and popular. This work focuses on recognition of human activity using smartphone sensors using different machine learning classification approaches. Data retrieved from smart phones' accelerometer and gyroscope sensors are classified in order to recognise human activity. Results of the approaches used are compared in terms of efficiency and presicion.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/4c70cc7f44bfd0105168acb8d85f0c71718a579f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Parisa Rashidi

In Computer Vision and Pattern Recognition

1. Parisa Rashidi
2. Graziano Pravadelli
3. Azra Bihorac
4. Florenc Demrozi
5. Diane Joyce Cook

In Transportation

1. Parisa Rashidi
2. Diane Joyce Cook

Citations in the corpus

• Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “was the Support Vector Machine (SVM) model [4], [6], [58], [60], [69], [78], [79], [82], [85], [92], [95], [109], [118], [127], [131], [132], [136], [138], [142], [145], [155], [168], [169], [171], [173], [182], [184], [186], [187], [189], [190], [209]–[212] which was used in 35 papers, achieving an”