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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| Published in: | Decision Support Systems Vol. 140; p. 113426 |
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| Language: | English |
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01.01.2021
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| ISSN: | 0167-9236 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 113426 |
| Author | Tarafdar, Pratik Bose, Indranil |
| Author_xml | – sequence: 1 givenname: Pratik surname: Tarafdar fullname: Tarafdar, Pratik organization: Information Systems & Analytics, Jindal Global Business School, O.P. Jindal Global University, Haryana 131001, India – sequence: 2 givenname: Indranil surname: Bose fullname: Bose, Indranil email: bose@iimcal.ac.in organization: Indian Institute of Management Calcutta, Diamond Harbour Road, Kolkata 700104, India |
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| Keywords | Activity-based wellness management Multimodal sensors Human activity recognition Boosting algorithms Machine learning Mobile health |
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| SubjectTerms | Activity-based wellness management Boosting algorithms Human activity recognition Machine learning Mobile health Multimodal sensors |
| Title | Recognition of human activities for wellness management using a smartphone and a smartwatch: A boosting approach |
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