Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets
Numerous laboratory-based studies have been reported on the use of accelerometry for gait and activity analysis particularly among cohorts of elderly subjects. A drawback of such studies is the use of custom hardware platforms worn by subjects. This paper introduces a system solely utilizing acceler...
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| Published in | Personal and ubiquitous computing Vol. 15; no. 7; pp. 667 - 678 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer-Verlag
01.10.2011
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1617-4909 1617-4917 |
| DOI | 10.1007/s00779-010-0345-1 |
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| Abstract | Numerous laboratory-based studies have been reported on the use of accelerometry for gait and activity analysis particularly among cohorts of elderly subjects. A drawback of such studies is the use of custom hardware platforms worn by subjects. This paper introduces a system solely utilizing accelerometers embedded in off-the-shelf cellular handsets that allow medical professionals and caregivers to remotely monitor the activity characteristics of elderly patients in the home or in the community. The use of ubiquitous cellular handsets makes the system far more acceptable to patients and enables the use of the system to be extended beyond healthcare facilities into the home environment. Mobile handset power consumption issues and other relevant handset and mobile handset application characteristics have been investigated in the context of the deployment of the proposed system. |
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| AbstractList | Numerous laboratory-based studies have been reported on the use of accelerometry for gait and activity analysis particularly among cohorts of elderly subjects. A drawback of such studies is the use of custom hardware platforms worn by subjects. This paper introduces a system solely utilizing accelerometers embedded in off-the-shelf cellular handsets that allow medical professionals and caregivers to remotely monitor the activity characteristics of elderly patients in the home or in the community. The use of ubiquitous cellular handsets makes the system far more acceptable to patients and enables the use of the system to be extended beyond healthcare facilities into the home environment. Mobile handset power consumption issues and other relevant handset and mobile handset application characteristics have been investigated in the context of the deployment of the proposed system.[PUBLICATION ABSTRACT] Numerous laboratory-based studies have been reported on the use of accelerometry for gait and activity analysis particularly among cohorts of elderly subjects. A drawback of such studies is the use of custom hardware platforms worn by subjects. This paper introduces a system solely utilizing accelerometers embedded in off-the-shelf cellular handsets that allow medical professionals and caregivers to remotely monitor the activity characteristics of elderly patients in the home or in the community. The use of ubiquitous cellular handsets makes the system far more acceptable to patients and enables the use of the system to be extended beyond healthcare facilities into the home environment. Mobile handset power consumption issues and other relevant handset and mobile handset application characteristics have been investigated in the context of the deployment of the proposed system. |
| Author | Hynes, Martin McCarrick, Eleanor Kilmartin, Liam Wang, Han |
| Author_xml | – sequence: 1 givenname: Martin surname: Hynes fullname: Hynes, Martin organization: School of Engineering and Informatics, NUI – sequence: 2 givenname: Han surname: Wang fullname: Wang, Han organization: School of Engineering and Informatics, NUI – sequence: 3 givenname: Eleanor surname: McCarrick fullname: McCarrick, Eleanor organization: School of Medicine, NUI – sequence: 4 givenname: Liam surname: Kilmartin fullname: Kilmartin, Liam email: liam.kilmartin@nuigalway.ie organization: School of Engineering and Informatics, NUI |
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| CitedBy_id | crossref_primary_10_1007_s00779_017_1003_7 crossref_primary_10_1007_s12668_013_0088_3 crossref_primary_10_1109_MMM_2014_2356148 crossref_primary_10_3233_AIS_170446 crossref_primary_10_3390_s19143213 crossref_primary_10_1007_s11301_015_0118_z crossref_primary_10_1007_s00779_012_0559_5 crossref_primary_10_1109_ACCESS_2023_3311261 crossref_primary_10_1007_s00779_015_0856_x crossref_primary_10_1016_j_jbi_2018_09_002 crossref_primary_10_1007_s00779_012_0560_z crossref_primary_10_1007_s11042_013_1725_0 crossref_primary_10_1007_s00779_013_0668_9 |
| Cites_doi | 10.1049/cp.2009.1680 10.1007/978-2-287-09411-8_10 10.1089/152091503322641088 10.1049/cp:20080645 10.1145/1554233.1554235 10.1145/1152215.1152244 |
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| References | Arensman WL, Whipple JG, Boler MS (2009) A public safety application of GPS-enabled smartphones and the android operating system. In: Proceedings of systems, man and cybernetics (SMC 2009), Oct 2009 DeLisa JA, Casey K (1998) Gait analysis in the science of rehabilitation. DIANE Publishing, Darby Gimeno J, Morillo P, Coma I, Fernández M (2009) A Device-independent 3D user interface for mobile phones based on motion and tracking. In: Proceedings of the 2009 International conference on image processing, computer vision and pattern recognition (IPCV 2009), July 2009 Niezen G, Hancke GP (2008) Gesture recognition as ubiquitous input for mobile phones. In: Proceedings of ubiquitous computing (UbiComp 2008), Sep 2008 Ibrahim RK, Ambikairajah E, Celler B, Lovell NH, Kilmartin L (2008) Gait patterns classification using spectral features. In: Proceedings of the IET Irish signals and systems conference, June 2008, pp 98–102 Annavaram M, Medvidovic N, Mitra U, Narayanan S, Sukhatme G, Meng Z, Qiu S, Kumar R, Thatte G, Spruijt-Metz D (2008) Multimodal Sensing of pediatric obesity applications. In: Proceedings of urbansense 2008, Nov 2008, pp 21–25 Mladenov M, Mock M (2009) A step counter service for java-enabled devices using a built-in accelerometer. In: Proceedings of the 1st international workshop on context-aware middleware and services: affiliated with the 4th international conference on communication system software and middleware (COMSWARE 2009), June 2009 Han JH, Jeon HS, Park KS (2008) Gait detection from three dimensional acceleration signals of ankles for patients with Parkinson’s disease. In: Proceedings of the international conference on technology and applications in biomedicine, ITAB 2008, May 2006, pp 349–352 Slawson SE, Justham LM, West PP, Conway MP, Caine MP, Harrison R (2008) Accelerometer profile recognition of swimming strokes. The engineering of sport 7. Springer, Paris, pp 81–87 Vajk T, Bamford W, Coulton P, Edwards R (2007) Using a mobile phone as a ‘Wii like’ controller. In: Proceedings of cybergames 2007, Sep 2007 Kilmartin L, Ibrahim RK, Ambikairajah E, Celler B (2009) Optimising recognition rates for subject independent gait pattern classification. In: Proceedings of the IET Irish signals and systems conference 2009, June 2009 ChenKYAcraSAMajchrzakKDonahueCLBakerLClemensLSumMBuchowskiMSPredicting energy expenditure of physical activity using hip- and wrist-worn accelerometersDiabetes Technol Ther20035610231033 Iso T, Yamazaki K (2006) Gait analyzer based on a cell phone with a single three-axis accelerometer. In: MobileHCI '06: Proceedings of the 8th conference on Human-computer interaction with mobile devices and services, pp 141–144 Yoshida T, Mizuno F, Hayasaka T, Tsubota K, Wada S, Yamaguchi T (2006) Gait analysis for detecting a leg a accident with an accelerometer. In: Proceedings of the transdisciplinary conference on distributed diagnosis and home healthcare 345_CR1 345_CR2 345_CR3 345_CR4 345_CR5 345_CR6 345_CR7 345_CR8 345_CR9 345_CR12 345_CR11 345_CR10 345_CR14 345_CR13 |
| References_xml | – reference: Gimeno J, Morillo P, Coma I, Fernández M (2009) A Device-independent 3D user interface for mobile phones based on motion and tracking. In: Proceedings of the 2009 International conference on image processing, computer vision and pattern recognition (IPCV 2009), July 2009 – reference: Arensman WL, Whipple JG, Boler MS (2009) A public safety application of GPS-enabled smartphones and the android operating system. In: Proceedings of systems, man and cybernetics (SMC 2009), Oct 2009 – reference: Kilmartin L, Ibrahim RK, Ambikairajah E, Celler B (2009) Optimising recognition rates for subject independent gait pattern classification. In: Proceedings of the IET Irish signals and systems conference 2009, June 2009 – reference: Yoshida T, Mizuno F, Hayasaka T, Tsubota K, Wada S, Yamaguchi T (2006) Gait analysis for detecting a leg a accident with an accelerometer. In: Proceedings of the transdisciplinary conference on distributed diagnosis and home healthcare – reference: DeLisa JA, Casey K (1998) Gait analysis in the science of rehabilitation. DIANE Publishing, Darby – reference: Slawson SE, Justham LM, West PP, Conway MP, Caine MP, Harrison R (2008) Accelerometer profile recognition of swimming strokes. The engineering of sport 7. Springer, Paris, pp 81–87 – reference: Iso T, Yamazaki K (2006) Gait analyzer based on a cell phone with a single three-axis accelerometer. In: MobileHCI '06: Proceedings of the 8th conference on Human-computer interaction with mobile devices and services, pp 141–144 – reference: Mladenov M, Mock M (2009) A step counter service for java-enabled devices using a built-in accelerometer. In: Proceedings of the 1st international workshop on context-aware middleware and services: affiliated with the 4th international conference on communication system software and middleware (COMSWARE 2009), June 2009 – reference: ChenKYAcraSAMajchrzakKDonahueCLBakerLClemensLSumMBuchowskiMSPredicting energy expenditure of physical activity using hip- and wrist-worn accelerometersDiabetes Technol Ther20035610231033 – reference: Ibrahim RK, Ambikairajah E, Celler B, Lovell NH, Kilmartin L (2008) Gait patterns classification using spectral features. In: Proceedings of the IET Irish signals and systems conference, June 2008, pp 98–102 – reference: Annavaram M, Medvidovic N, Mitra U, Narayanan S, Sukhatme G, Meng Z, Qiu S, Kumar R, Thatte G, Spruijt-Metz D (2008) Multimodal Sensing of pediatric obesity applications. In: Proceedings of urbansense 2008, Nov 2008, pp 21–25 – reference: Han JH, Jeon HS, Park KS (2008) Gait detection from three dimensional acceleration signals of ankles for patients with Parkinson’s disease. In: Proceedings of the international conference on technology and applications in biomedicine, ITAB 2008, May 2006, pp 349–352 – reference: Niezen G, Hancke GP (2008) Gesture recognition as ubiquitous input for mobile phones. In: Proceedings of ubiquitous computing (UbiComp 2008), Sep 2008 – reference: Vajk T, Bamford W, Coulton P, Edwards R (2007) Using a mobile phone as a ‘Wii like’ controller. In: Proceedings of cybergames 2007, Sep 2007 – ident: 345_CR14 doi: 10.1049/cp.2009.1680 – ident: 345_CR10 – ident: 345_CR4 doi: 10.1007/978-2-287-09411-8_10 – ident: 345_CR13 – ident: 345_CR12 – ident: 345_CR11 doi: 10.1089/152091503322641088 – ident: 345_CR1 doi: 10.1049/cp:20080645 – ident: 345_CR7 – ident: 345_CR2 – ident: 345_CR5 – ident: 345_CR3 – ident: 345_CR9 – ident: 345_CR8 doi: 10.1145/1554233.1554235 – ident: 345_CR6 doi: 10.1145/1152215.1152244 |
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| Title | Accurate monitoring of human physical activity levels for medical diagnosis and monitoring using off-the-shelf cellular handsets |
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