Recognition of human activities for wellness management using a smartphone and a smartwatch: A boosting approach

Mobile health applications are considered to be powerful tools for activity-based wellness management. With the availability of multimodal sensors in smart devices used in our daily lives, it is possible to track human activity and deliver context-aware wellness services. The embedded sensors in nat...

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Bibliographic Details
Published in:Decision Support Systems Vol. 140; p. 113426
Main Authors: Tarafdar, Pratik, Bose, Indranil
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2021
Subjects:
Activity-based wellness management
Boosting algorithms
Human activity recognition
Machine learning
Mobile health
Multimodal sensors
Activity-based wellness management
Multimodal sensors
Human activity recognition
Boosting algorithms
Machine learning
Mobile health
ISSN:0167-9236
Online Access:Get full text
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Summary:Mobile health applications are considered to be powerful tools for activity-based wellness management. With the availability of multimodal sensors in smart devices used in our daily lives, it is possible to track human activity and deliver context-aware wellness services. The embedded sensors in naturally used devices such as smartphones, smartwatches, and wearables contain rich information that can be integrated for human activity recognition. Our research demonstrates how powerful boosting algorithms can extract knowledge for human activity classification in a real-life setting. Our results show that boosting classifiers outperform traditional machine learning classifiers in the detection of basic human activities such as walking, standing, sitting, exercise, and sleeping. Further, we perform feature engineering to compare the potential of a smartphone and a smartwatch in activity detection. Our feature engineering strategy provides directions about the selection of sensor features for improvement in classification of basic human activities. The theoretical and practical implications of this research for activity-based wellness management are also discussed. •Human activity recognition is an important constituent for wellness management.•Machine learning can be used for predicting human activities based on sensor data.•Boosting methods show high predictive accuracy for human activity recognition.•Feature engineering determines the importance of specific sensor data for different activities.
ISSN:0167-9236
DOI:10.1016/j.dss.2020.113426