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Open Dataset for the Automatic Recognition of Sedentary Behaviors
William Possos1, Robinson Cruz1, Jesús D Cerón1
1Telematics Engineering Research Group, University of Cauca, Colombia.
Studies in Health Technology and Informatics
|May 9, 2017
Summary
This study developed a dataset to automatically classify sedentary behaviors, crucial for preventing noncommunicable diseases (NCDs). The Random Forest algorithm with data from a hip-worn phone and beacons achieved the best classification accuracy.
Area of Science:
- Health Informatics
- Biomedical Engineering
- Data Science
Background:
- Sedentary behavior is a significant risk factor for noncommunicable diseases (NCDs), including cardiovascular diseases (CVD), type 2 diabetes, and cancer.
- Personalized prevention programs require accurate identification of specific sedentary activities like TV viewing, sitting at work, and driving.
Purpose of the Study:
- To create and assess a public dataset for the automated recognition and classification of diverse sedentary behaviors.
- To enhance the accuracy of sedentary behavior classification through advanced data mining techniques and sensor integration.
Main Methods:
- Collected data from 30 participants performing 23 distinct sedentary behaviors using wrist-worn wearables, and smartphones placed on the hip and thigh.
- Integrated Bluetooth Low Energy (BLE) beacons to provide symbolic location references, aiming to improve classification precision.
- Evaluated six established data mining classification algorithms to determine the most effective method for distinguishing between the 23 sedentary behaviors.
Main Results:
- The Random Forest algorithm demonstrated superior performance in classifying sedentary behaviors.
- Classification accuracy was highest when data was collected from the smartphone positioned on the hip.
- The incorporation of BLE beacons significantly enhanced the precision of sedentary behavior classification.
Conclusions:
- The Random Forest algorithm, combined with data from a hip-worn smartphone and BLE beacons, offers a highly accurate method for classifying sedentary behaviors.
- This approach provides a foundation for developing more effective, personalized interventions to mitigate risks associated with sedentary lifestyles.
