首页|Ensemble of deep learning techniques to human activity recognition using smart phone signals
Ensemble of deep learning techniques to human activity recognition using smart phone signals
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Human Activity Recognition (HAR) has become a signifcant area of study in the felds of health, human behavior analysis, the Internet of Things, and human-machine interaction in recent years. Smartphones are a popular choice for HAR as they are common devices used in daily life. However, most available HAR datasets are gathered in laboratory settings, which do not refect real-world scenarios. To address this issue, a real-world dataset using smartphone inertial sensors, involving 62 individuals, is collected. The collected dataset is noisy, small, and has variable frequency. On the other hand, in the context of HAR, algo- rithms face additional challenges due to intra-class diversity (which refers to diferences in the characteristics of performing an activity by diferent people or by the same individual under diferent conditions) and inter-class similarity (which refers to diferent activities that are highly similar). Consequently, it is essential to extract features accurately from the dataset. Ensemble learning, which combines multiple models, is an efective approach to improve generalization performance. In this paper, a weighted ensemble of hybrid deep models for HAR using smartphone sensors is proposed. The proposed ensemble approach demonstrates superior performance compared to current methods, achieving impressive results across multiple evaluation metrics. Specifcally, the experimental analysis demon- strates an accuracy of 97.15%, precision of 96.41%, recall of 95.62%, and an F1-score of 96.01%. These results demonstrate the efectiveness of our ensemble approach in address- ing the challenges of HAR in real-world scenarios.
Human Activity RecognitionEnsemble learningDeep LearningTime series classifcationReal-world datasetSmartphone inertial sensors
Soodabeh Imanzadeh、Jafar Tanha、Mahdi Jalili
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Electrical and Computer Engineering Department,University of Tabriz,Tabriz,Iran
School of Engineering,RMIT University,Melbourne,Australia
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