Literature DB >> 24145917

A wearable inertial measurement unit for long-term monitoring in the dependency care area.

Daniel Rodríguez-Martín1, Carlos Pérez-López, Albert Samà, Joan Cabestany, Andreu Català.   

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.

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Year:  2013        PMID: 24145917      PMCID: PMC3859110          DOI: 10.3390/s131014079

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  43 in total

1.  Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions.

Authors:  M Ermes; J Pärkka; J Mantyjarvi; I Korhonen
Journal:  IEEE Trans Inf Technol Biomed       Date:  2008-01

Review 2.  Activity identification using body-mounted sensors--a review of classification techniques.

Authors:  Stephen J Preece; John Y Goulermas; Laurence P J Kenney; Dave Howard; Kenneth Meijer; Robin Crompton
Journal:  Physiol Meas       Date:  2009-04-02       Impact factor: 2.833

3.  Closed-loop insulin delivery: is the Holy Grail near?

Authors:  Eric Renard
Journal:  Lancet       Date:  2010-02-04       Impact factor: 79.321

4.  Using sensors to measure activity in people with stroke.

Authors:  George D Fulk; Edward Sazonov
Journal:  Top Stroke Rehabil       Date:  2011 Nov-Dec       Impact factor: 2.119

5.  Ambulatory human motion tracking by fusion of inertial and magnetic sensing with adaptive actuation.

Authors:  H Martin Schepers; Daniel Roetenberg; Peter H Veltink
Journal:  Med Biol Eng Comput       Date:  2009-12-17       Impact factor: 2.602

6.  Physical activity in the United States measured by accelerometer.

Authors:  Richard P Troiano; David Berrigan; Kevin W Dodd; Louise C Mâsse; Timothy Tilert; Margaret McDowell
Journal:  Med Sci Sports Exerc       Date:  2008-01       Impact factor: 5.411

7.  Dyskinesia and motor state detection in Parkinson's disease patients with a single movement sensor.

Authors:  A Samà; C Pérez-Lopez; J Romagosa; D Rodríguez-Martín; A Català; J Cabestany; D A Pérez-Martínez; A Rodríguez-Molinero
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2012

8.  Identification of sit-to-stand and stand-to-sit transitions using a single inertial sensor.

Authors:  Daniel Rodríguez-Martín; Albert Samà; Carlos Pérez-López; Andreu Català
Journal:  Stud Health Technol Inform       Date:  2012

9.  Inertial sensor-based two feet motion tracking for gait analysis.

Authors:  Tran Nhat Hung; Young Soo Suh
Journal:  Sensors (Basel)       Date:  2013-04-29       Impact factor: 3.576

10.  Ambulatory measurement of knee motion and physical activity: preliminary evaluation of a smart activity monitor.

Authors:  James Huddleston; Amer Alaiti; Dov Goldvasser; Donna Scarborough; Andrew Freiberg; Harry Rubash; Henrik Malchau; William Harris; David Krebs
Journal:  J Neuroeng Rehabil       Date:  2006-09-13       Impact factor: 4.262

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  15 in total

1.  Validation of a portable device for mapping motor and gait disturbances in Parkinson's disease.

Authors:  Alejandro Rodríguez-Molinero; Albert Samà; David A Pérez-Martínez; Carlos Pérez López; Jaume Romagosa; Àngels Bayés; Pilar Sanz; Matilde Calopa; César Gálvez-Barrón; Eva de Mingo; Daniel Rodríguez Martín; Natalia Gonzalo; Francesc Formiga; Joan Cabestany; Andreu Catalá
Journal:  JMIR Mhealth Uhealth       Date:  2015-02-02       Impact factor: 4.773

2.  Assessing Motor Fluctuations in Parkinson's Disease Patients Based on a Single Inertial Sensor.

Authors:  Carlos Pérez-López; Albert Samà; Daniel Rodríguez-Martín; Andreu Català; Joan Cabestany; Juan Manuel Moreno-Arostegui; Eva de Mingo; Alejandro Rodríguez-Molinero
Journal:  Sensors (Basel)       Date:  2016-12-15       Impact factor: 3.576

3.  Analysis of Correlation between an Accelerometer-Based Algorithm for Detecting Parkinsonian Gait and UPDRS Subscales.

Authors:  Alejandro Rodríguez-Molinero; Albert Samà; Carlos Pérez-López; Daniel Rodríguez-Martín; Leo R Quinlan; Sheila Alcaine; Berta Mestre; Paola Quispe; Benedetta Giuliani; Gabriel Vainstein; Patrick Browne; Dean Sweeney; J Manuel Moreno Arostegui; Àngels Bayes; Hadas Lewy; Alberto Costa; Roberta Annicchiarico; Timothy Counihan; Gearòid Ò Laighin; Joan Cabestany
Journal:  Front Neurol       Date:  2017-09-01       Impact factor: 4.003

4.  A Waist-Worn Inertial Measurement Unit for Long-Term Monitoring of Parkinson's Disease Patients.

Authors:  Daniel Rodríguez-Martín; Carlos Pérez-López; Albert Samà; Andreu Català; Joan Manuel Moreno Arostegui; Joan Cabestany; Berta Mestre; Sheila Alcaine; Anna Prats; María de la Cruz Crespo; Àngels Bayés
Journal:  Sensors (Basel)       Date:  2017-04-11       Impact factor: 3.576

Review 5.  A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry.

Authors:  Salvatore Tedesco; John Barton; Brendan O'Flynn
Journal:  Sensors (Basel)       Date:  2017-06-03       Impact factor: 3.576

6.  Detecting falls with wearable sensors using machine learning techniques.

Authors:  Ahmet Turan Özdemir; Billur Barshan
Journal:  Sensors (Basel)       Date:  2014-06-18       Impact factor: 3.576

7.  Synchronous wearable wireless body sensor network composed of autonomous textile nodes.

Authors:  Peter Vanveerdeghem; Patrick Van Torre; Christiaan Stevens; Jos Knockaert; Hendrik Rogier
Journal:  Sensors (Basel)       Date:  2014-10-09       Impact factor: 3.576

8.  Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People.

Authors:  Roberto Luis Shinmoto Torres; Renuka Visvanathan; Stephen Hoskins; Anton van den Hengel; Damith C Ranasinghe
Journal:  Sensors (Basel)       Date:  2016-04-15       Impact factor: 3.576

Review 9.  Using Portable Transducers to Measure Tremor Severity.

Authors:  Rodger J Elble; James McNames
Journal:  Tremor Other Hyperkinet Mov (N Y)       Date:  2016-05-17

10.  Configurable, wearable sensing and vibrotactile feedback system for real-time postural balance and gait training: proof-of-concept.

Authors:  Junkai Xu; Tian Bao; Ung Hee Lee; Catherine Kinnaird; Wendy Carender; Yangjian Huang; Kathleen H Sienko; Peter B Shull
Journal:  J Neuroeng Rehabil       Date:  2017-10-11       Impact factor: 4.262

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