Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly

A new method of physical activity monitoring is presented, which is able to detect body postures (sifting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking." 8.2% for sifting, and 1.3% for lying.

[1]  J Lubitz,et al.  The effect of longevity on spending for acute and long-term care. , 2000, The New England journal of medicine.

[2]  K H Mauritz,et al.  Control mechanisms for restoring posture and movements in paraplegics. , 1989, Progress in brain research.

[3]  Kamiar Aminian,et al.  Measurement of stand-sit and sit-stand transitions using a miniature gyroscope and its application in fall risk evaluation in the elderly , 2002, IEEE Transactions on Biomedical Engineering.

[4]  David S. Krantz,et al.  Automated physical activity monitoring: validation and comparison with physiological and self-report measures. , 1993, Psychophysiology.

[5]  M Honl,et al.  Duration and frequency of every day activities in total hip patients. , 2001, Journal of biomechanics.

[6]  Bijan Najafi,et al.  Falling risk evaluation in elderly using miniature gyroscope , 2000, 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.00EX451).

[7]  John G. Webster,et al.  Portable Accelerometer Device for Measuring Human Energy Expenditure , 1981, IEEE Transactions on Biomedical Engineering.

[8]  I. Daubechies Ten Lectures on Wavelets , 1992 .

[9]  J. Webster,et al.  Estimation of energy expenditure by a portable accelerometer. , 1983, Medicine and science in sports and exercise.

[10]  Friedrich Foerster,et al.  Detection of posture and motion by accelerometry : a validation study in ambulatory monitoring , 1999 .

[11]  W P James,et al.  Approaches to estimating physical activity in the community: calorimetric validation of actometers and heart rate monitoring. , 1988, European journal of clinical nutrition.

[12]  A. Grossmann,et al.  DECOMPOSITION OF HARDY FUNCTIONS INTO SQUARE INTEGRABLE WAVELETS OF CONSTANT SHAPE , 1984 .

[13]  M. Sun,et al.  Improving energy expenditure estimation by using a triaxial accelerometer. , 1997, Journal of applied physiology.

[14]  T Togawa,et al.  A solid-state ambulatory physical activity monitor and its application to measuring daily activity of the elderly. , 1997, Journal of medical engineering & technology.

[15]  M. Chamberlain,et al.  An investigation into the problems of easy chairs used by the arthritic and the elderly. , 1981, Rheumatology and rehabilitation.

[16]  T Chau,et al.  A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. , 2001, Gait & posture.

[17]  J. D. Janssen,et al.  A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity , 1997, IEEE Transactions on Biomedical Engineering.

[18]  G. A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .

[19]  S. Asfour,et al.  Discrete wavelet transform: a tool in smoothing kinematic data. , 1999, Journal of biomechanics.

[20]  Nan Li,et al.  A universal pattern of mortality decline in the G7 countries , 2000, Nature.

[21]  P. Seed,et al.  Do Hospital Fall Prevention Programs Work? A Systematic Review , 2000, Journal of the American Geriatrics Society.

[22]  H. Busser,et al.  Ambulatory monitoring of children's activity. , 1997, Medical Engineering and Physics.

[23]  P H Veltink,et al.  Detection of static and dynamic activities using uniaxial accelerometers. , 1996, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[24]  J A Balogun,et al.  Factors affecting Caltrac and Calcount accelerometer output. , 1988, Physical therapy.

[25]  J. Kent‐Braun,et al.  Quantitation of lower physical activity in persons with multiple sclerosis. , 1997, Medicine and science in sports and exercise.

[26]  Peter M. Quesada,et al.  Wavelet-based noise removal for biomechanical signals: a comparative study , 2000, IEEE Transactions on Biomedical Engineering.

[27]  R Riener,et al.  Biomechanical analysis of sit-to-stand transfer in healthy and paraplegic subjects. , 2000, Clinical biomechanics.

[28]  R E LaPorte,et al.  Assessment of physical activity in epidemiologic research: problems and prospects. , 1985, Public health reports.

[29]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

[30]  E. Diczfalusy,et al.  The demographic revolution and our common future. , 2001, Maturitas.

[31]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[32]  Toshiyo Tamura,et al.  Classification of acceleration waveform in a continuous walking record , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[33]  G.A.L. Meijer,et al.  Methods to assess physical activity with special reference to motion sensors and accelerometers , 1991, IEEE Transactions on Biomedical Engineering.

[34]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[35]  E. Crimmins,et al.  Further evidence on recent trends in the prevalence and incidence of disability among older Americans from two sources: the LSOA and the NHIS. , 1997, The journals of gerontology. Series B, Psychological sciences and social sciences.

[36]  Kamiar Aminian,et al.  An ambulatory system for physical activity monitoring in elderly , 2000, 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.00EX451).

[37]  J C Barbenel,et al.  A new ambulatory monitoring instrument of posture and mobility related activities. , 1997, Biomedical sciences instrumentation.

[38]  K. Manton Epidemiological, demographic, and social correlates of disability among the elderly. , 1989, The Milbank quarterly.

[39]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.