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Sensors 2013, 13(10), 14079-14104; doi:10.3390/s131014079

A Wearable Inertial Measurement Unit for Long-Term Monitoring in the Dependency Care Area

Technical Research Centre for Dependency Care and Autonomous Living–CETPD, Universitat Politècnica de Catalunya–Barcelona Tech, Rambla de l'Exposició 59-69, Vilanova i la Geltrú 08800, Barcelona, Spain
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Received: 12 July 2013 / Revised: 27 September 2013 / Accepted: 29 September 2013 / Published: 18 October 2013
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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Abstract

Human movement analysis is a field of wide interest since it enables the assessment of a large variety of variables related to quality of life. Human movement can be accurately evaluated through Inertial Measurement Units (IMU), which are wearable and comfortable devices with long battery life. The IMU’s movement signals might be, on the one hand, stored in a digital support, in which an analysis is performed a posteriori. On the other hand, the signal analysis might take place in the same IMU at the same time as the signal acquisition through online classifiers. The new sensor system presented in this paper is designed for both collecting movement signals and analyzing them in real-time. This system is a flexible platform useful for collecting data via a triaxial accelerometer, a gyroscope and a magnetometer, with the possibility to incorporate other information sources in real-time. A µSD card can store all inertial data and a Bluetooth module is able to send information to other external devices and receive data from other sources. The system presented is being used in the real-time detection and analysis of Parkinson’s disease symptoms, in gait analysis, and in a fall detection system. View Full-Text
Keywords: inertial sensors; hardware; firmware; autonomy; accelerometry; Parkinson’s disease inertial sensors; hardware; firmware; autonomy; accelerometry; Parkinson’s disease
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Rodríguez-Martín, D.; Pérez-López, C.; Samà, A.; Cabestany, J.; Català, A. A Wearable Inertial Measurement Unit for Long-Term Monitoring in the Dependency Care Area. Sensors 2013, 13, 14079-14104.

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