TY - CHAP
T1 - Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices
AU - Puentes, John
AU - Montagner, Julien
AU - Lecornu, Laurent
AU - Lähteenmäki, Jaakko
N1 - Project code: 29016
PY - 2013
Y1 - 2013
N2 - Data collected by multiple physiological sensors are being increasingly used for wellness monitoring or disease management, within a pervasiveness context facilitated by the massive use of mobile devices. These abundant complementary raw data are challenging to understand and process, because of their voluminous and heterogeneous nature, as well as the data quality issues that could impede their utilization. This chapter examines the main data quality questions concerning six frequently used physiological sensors - glucometer, scale, blood pressure meter, heart rate meter, pedometer, and thermometer -, as well as patient observations that may be associated to a given set of measurements. We discuss specific details that are either overlooked in the literature or avoided by data exploration and information extraction algorithms, but have significant importance to properly preprocess these data. Making use of different types of formalized knowledge, according to the characteristics of physiological measurement devices, relevant data handled by a Personal Health Record on a mobile device, are evaluated from a data quality perspective, considering data deficiencies factors, consequences and reasons. We propose a general scheme for sensors data quality characterization adapted to a pervasive scenario.
AB - Data collected by multiple physiological sensors are being increasingly used for wellness monitoring or disease management, within a pervasiveness context facilitated by the massive use of mobile devices. These abundant complementary raw data are challenging to understand and process, because of their voluminous and heterogeneous nature, as well as the data quality issues that could impede their utilization. This chapter examines the main data quality questions concerning six frequently used physiological sensors - glucometer, scale, blood pressure meter, heart rate meter, pedometer, and thermometer -, as well as patient observations that may be associated to a given set of measurements. We discuss specific details that are either overlooked in the literature or avoided by data exploration and information extraction algorithms, but have significant importance to properly preprocess these data. Making use of different types of formalized knowledge, according to the characteristics of physiological measurement devices, relevant data handled by a Personal Health Record on a mobile device, are evaluated from a data quality perspective, considering data deficiencies factors, consequences and reasons. We propose a general scheme for sensors data quality characterization adapted to a pervasive scenario.
U2 - 10.1007/978-1-4614-4514-2_10
DO - 10.1007/978-1-4614-4514-2_10
M3 - Chapter or book article
SN - 978-1-4614-4513-5
SN - 978-1-4899-8782-2
T3 - Healthcare Delivery in the Information Age
SP - 103
EP - 133
BT - Pervasive health knowledge management
A2 - Bali, Rajeev
A2 - Troshani, Indrit
A2 - Goldberg, Steve
A2 - Wickramasinghe, Nilmini
PB - Springer
CY - New York
ER -