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

Tarafdar, Pratik and Bose, Indranil (2021) Recognition of human activities for wellness management using a smartphone and a smartwatch: A boosting approach. Decision Support Systems, 140: 113426. ISSN 1679236

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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.

Item Type: Article
Keywords: Activity-based wellness management | Boosting algorithms | Human activity recognition | Machine learning | Mobile health | Multimodal sensors
Subjects: Social Sciences and humanities > Business, Management and Accounting > Strategy and Management
JGU School/Centre: Jindal Global Business School
Depositing User: Mr. Syed Anas
Date Deposited: 17 Dec 2021 18:27
Last Modified: 17 Dec 2021 18:27
Official URL:
Funders: Indian Institute of Management Calcutta, India


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