Ceron Bravo JD (2016)
Publication Status: Published
Publication Type: Conference contribution, Original article
Publication year: 2016
Book Volume: 228
Pages Range: 804-6
Sedentary behavior has been associated to the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Accelerometers and inclinometers have been used to estimate sedentary behaviors, however a major limitation is that these devices do not provide contextual information such as the activity performed, e.g., TV viewing, sitting at work, driving, etc.\nThe main objective of the thesis is to propose and evaluate a Personal Health Record System to support the assessment and monitoring of sedentary behaviors.\nUntil now, we have implemented a system, which identifies individual's sedentary behaviors and location based on accelerometer data obtained from a smartwatch, and symbolic location data obtained from Bluetooth beacons. The system infers sedentary behaviors by means of a supervised Machine Learning Classifier. The precision in the classification of the six studied sedentary behaviors exceeded 90%, being the Random Forest algorithm the most precise.\nThe proposed system allows the recognition of specific sedentary behaviors and their location with very high precision.\nBACKGROUND\nOBJECTIVE\nRESULTS\nCONCLUSION
APA:
Ceron Bravo, J.D. (2016). Towards a Personal Health Record System for the Assesment and Monitoring of Sedentary Behavior in Indoor Locations. (pp. 804-6).
MLA:
Ceron Bravo, Jesus David. "Towards a Personal Health Record System for the Assesment and Monitoring of Sedentary Behavior in Indoor Locations." 2016. 804-6.
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