Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices

Jaakko Lähteenmäki, John Puentes, Laurent Lecornu, Julien Montagner

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleProfessional

4 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationPervasive health knowledge management
EditorsRajeev Bali
Place of PublicationNew York
PublisherSpringer
Pages103-133
ISBN (Electronic)9781461445142
ISBN (Print)978-1-4614-4513-5
Publication statusPublished - 2013
MoE publication typeD2 Article in professional manuals or guides or professional information systems or text book material

Fingerprint

Mobile devices
Health
Sensors
Thermometers
Blood pressure
Monitoring

Cite this

Lähteenmäki, J., Puentes, J., Lecornu, L., & Montagner, J. (2013). Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices. In R. Bali (Ed.), Pervasive health knowledge management (pp. 103-133). New York: Springer.
Lähteenmäki, Jaakko ; Puentes, John ; Lecornu, Laurent ; Montagner, Julien. / Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices. Pervasive health knowledge management. editor / Rajeev Bali. New York : Springer, 2013. pp. 103-133
@inbook{6ced769dbd79489f8c8f963e81a5c5f2,
title = "Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices",
abstract = "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.",
author = "Jaakko L{\"a}hteenm{\"a}ki and John Puentes and Laurent Lecornu and Julien Montagner",
note = "Project code: 29016",
year = "2013",
language = "English",
isbn = "978-1-4614-4513-5",
pages = "103--133",
editor = "Rajeev Bali",
booktitle = "Pervasive health knowledge management",
publisher = "Springer",
address = "Germany",

}

Lähteenmäki, J, Puentes, J, Lecornu, L & Montagner, J 2013, Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices. in R Bali (ed.), Pervasive health knowledge management. Springer, New York, pp. 103-133.

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 proceedingChapter or book articleProfessional

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 -

Lähteenmäki J, Puentes J, Lecornu L, Montagner J. Quality Analysis of Sensors Data for Personal Health Records on Mobile Devices. In Bali R, editor, Pervasive health knowledge management. New York: Springer. 2013. p. 103-133