title: Towards a Personal Health Record System for the Assesment and Monitoring of Sedentary Behavior in Indoor Locations.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  The main objective of the thesis is to propose and evaluate a Personal Health Record System to support the assessment and monitoring of sedentary behaviors.
  
  
  RESULTS
  Until 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.
  
  
  CONCLUSIONS
  The proposed system allows the recognition of specific sedentary behaviors and their location with very high precision.
authors: Telematics Engineering Research Group, University of Cauca, Colombia.
authors: Jesus D
authors: JD
authors: Ceron
authors: Telematics Engineering Research Group, University of Cauca, Colombia.
authors: Diego M
authors: DM
authors: Lopez
mesh_terms: D061725:Accelerometry; D000465:Algorithms; D055991:Health Records, Personal; D006801:Humans; D011187:Posture; D057185:Sedentary Behavior; D000069553:Supervised Machine Learning; D059015:Wireless Technology