An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones †
Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to...
Main Authors: | Jayita Saha, Chandreyee Chowdhury, Ishan Roy Chowdhury, Suparna Biswas, Nauman Aslam |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-04-01
|
Series: | Information |
Subjects: | |
Online Access: | http://www.mdpi.com/2078-2489/9/4/94 |
Similar Items
-
PACP: A Position-Independent Activity Recognition Method Using Smartphone Sensors
by: Rong Yang, et al.
Published: (2016-12-01) -
Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors
by: Ku Nurhanim Ku Abd. Rahim, et al.
Published: (2018-11-01) -
A Comprehensive Study of Smartphone-Based Indoor Activity Recognition via Xgboost
by: Wenting Zhang, et al.
Published: (2019-01-01) -
DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier
by: Imran Ashraf, et al.
Published: (2019-12-01) -
A Robust Deep Learning Approach for Position-Independent Smartphone-Based Human Activity Recognition
by: Bandar Almaslukh, et al.
Published: (2018-11-01)