scispace - formally typeset
Journal ArticleDOI

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

TLDR
The ambulatory system 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, in both first and second studies.
Abstract
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.

read more

Citations
More filters
Journal ArticleDOI

A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring

TL;DR: This paper reviews and compares existing commercial products to provide a comprehensive outlook of current development status and possible emerging technologies of wearable accelerometry-based motion detectors.
Journal ArticleDOI

Review: Ambient intelligence: Technologies, applications, and opportunities

TL;DR: This paper provides a survey of the technologies that comprise ambient intelligence and of the applications that are dramatically affected by it and specifically focuses on the research that makes AmI technologies ''intelligent''.
Journal ArticleDOI

Stretchable, Transparent, Ultrasensitive, and Patchable Strain Sensor for Human-Machine Interfaces Comprising a Nanohybrid of Carbon Nanotubes and Conductive Elastomers.

TL;DR: A stretchable, transparent, ultrasensitive, and patchable strain sensor that is made of a novel sandwich-like stacked piezoresisitive nanohybrid film of single-wall carbon nanotubes and a conductive elastomeric composite of polyurethane (PU)-poly(3,4-ethylenedioxythiophene) polystyrenesulfonate is described.
Journal ArticleDOI

Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

TL;DR: How human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose, is discussed.
Journal ArticleDOI

Eye Movement Analysis for Activity Recognition Using Electrooculography

TL;DR: The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.
References
More filters
Journal ArticleDOI

A theory for multiresolution signal decomposition: the wavelet representation

TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book

Ten lectures on wavelets

TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI

Ten Lectures on Wavelets

TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Journal ArticleDOI

Singularity detection and processing with wavelets

TL;DR: It is proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents.
Journal ArticleDOI

Decomposition of Hardy functions into square integrable wavelets of constant shape

TL;DR: In this article, the authors studied square integrable coefficients of an irreducible representation of the non-unimodular $ax + b$-group and obtained explicit expressions in the case of a particular analyzing family that plays a role analogous to coherent states (Gabor wavelets) in the usual $L_2 $ -theory.
Related Papers (5)