Abstract
| Original language | English |
|---|---|
| Title of host publication | Pervasive health knowledge management |
| Editors | Rajeev Bali |
| Place of Publication | New York |
| Publisher | Springer |
| Pages | 103-133 |
| ISBN (Electronic) | 9781461445142 |
| ISBN (Print) | 978-1-4614-4513-5 |
| Publication status | Published - 2013 |
| MoE publication type | D2 Article in professional manuals or guides or professional information systems or text book material |
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Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices. / Lähteenmäki, Jaakko; Puentes, John; Lecornu, Laurent; Montagner, Julien.
Pervasive health knowledge management. ed. / Rajeev Bali. New York : Springer, 2013. p. 103-133.Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Professional
TY - CHAP
T1 - Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices
AU - Lähteenmäki, Jaakko
AU - Puentes, John
AU - Lecornu, Laurent
AU - Montagner, Julien
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.
M3 - Chapter or book article
SN - 978-1-4614-4513-5
SP - 103
EP - 133
BT - Pervasive health knowledge management
A2 - Bali, Rajeev
PB - Springer
CY - New York
ER -