Literature DB >> 8973963

Detection of static and dynamic activities using uniaxial accelerometers.

P H Veltink1, H B Bussmann, W de Vries, W L Martens, R C Van Lummel.   

Abstract

Rehabilitation treatment may be improved by objective analysis of activities of daily living. For this reason, the feasibility of distinguishing several static and dynamic activities (standing, sitting, lying, walking, ascending stairs, descending stairs, cycling) using a small set of two or three uniaxial accelerometers signals can be measured with a portable data acquisition system, which potentially makes it possible to perform online detection of static and dynamic activities in the home environment. However, the procedures described in this paper have yet to be evaluated in the home environment. Experiments were conducted on ten healthy subjects, with accelerometers mounted on several positions and orientations on the body, performing static and dynamic activities according to a fixed protocol. Specifically, accelerometers on the sternum and thigh were evaluated. These accelerometers were oriented in the sagittal plane, perpendicular to the long axis of the segment (tangential), or along this axis (radial). First, discrimination between the static or dynamic character of activities was investigated. This appeared to be feasible using an rms-detector applied on the signal of one sensor tangentially mounted on the thigh. Second, the distinction between static activities was investigated. Standing, sitting, lying supine, on a side and prone could be distinguished by observing the static signals of two accelerometers, one mounted tangentially on the thigh, and the second mounted radially on the sternum. Third, the distinction between the cyclical dynamic activities walking, stair ascent, stair descent and cycling was investigated. The discriminating potentials of several features of the accelerometer signals were assessed: the mean value, the standard deviation, the cycle time and the morphology. Signal morphology was expressed by the maximal cross-correlation coefficients with template signals for the different dynamic activities. The mean signal values and signal morphology of accelerometers mounted tangentially on the thigh and the sternum appeared to contribute to the discrimination of dynamic activities with varying detection performances. The standard deviation of the signal and the cycle time were primarily related to the speed of the dynamic activities, and did not contribute to the discrimination of the activities. Therefore, discrimination of dynamic activities on the basis of the combined evaluation of the mean signal value and signal morphology is proposed.

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Mesh:

Year:  1996        PMID: 8973963     DOI: 10.1109/86.547939

Source DB:  PubMed          Journal:  IEEE Trans Rehabil Eng        ISSN: 1063-6528


  58 in total

1.  Physical activity monitoring based on accelerometry: validation and comparison with video observation.

Authors:  K Aminian; P Robert; E E Buchser; B Rutschmann; D Hayoz; M Depairon
Journal:  Med Biol Eng Comput       Date:  1999-05       Impact factor: 2.602

2.  Analysis and decomposition of signals obtained by thigh-fixed uni-axial accelerometry during normal walking.

Authors:  J B Bussmann; L Damen; H J Stam
Journal:  Med Biol Eng Comput       Date:  2000-11       Impact factor: 2.602

3.  Ambulatory measurement of upper limb usage and mobility-related activities during normal daily life with an upper limb-activity monitor: a feasibility study.

Authors:  F C Schasfoort; J B J Bussmann; H J Stam
Journal:  Med Biol Eng Comput       Date:  2002-03       Impact factor: 2.602

4.  Detection of daily physical activities using a triaxial accelerometer.

Authors:  M J Mathie; A C F Coster; N H Lovell; B G Celler
Journal:  Med Biol Eng Comput       Date:  2003-05       Impact factor: 2.602

5.  Assessment of spatio-temporal parameters during unconstrained walking.

Authors:  Wiebren Zijlstra
Journal:  Eur J Appl Physiol       Date:  2004-02-17       Impact factor: 3.078

6.  Investigating body motion patterns in patients with Parkinson's disease using matching pursuit algorithm.

Authors:  M Sekine; M Akay; T Tamura; Y Higashi; T Fujimoto
Journal:  Med Biol Eng Comput       Date:  2004-01       Impact factor: 2.602

7.  Quaternion-based strap-down integration method for applications of inertial sensing to gait analysis.

Authors:  A M Sabatini
Journal:  Med Biol Eng Comput       Date:  2005-01       Impact factor: 2.602

8.  Classification of basic daily movements using a triaxial accelerometer.

Authors:  M J Mathie; B G Celler; N H Lovell; A C F Coster
Journal:  Med Biol Eng Comput       Date:  2004-09       Impact factor: 2.602

9.  Gait characteristics in people with type 2 diabetes mellitus.

Authors:  Jerrold Petrofsky; Scott Lee; Salameh Bweir
Journal:  Eur J Appl Physiol       Date:  2004-12-01       Impact factor: 3.078

10.  A combined sEMG and accelerometer system for monitoring functional activity in stroke.

Authors:  Serge H Roy; M Samuel Cheng; Shey-Sheen Chang; John Moore; Gianluca De Luca; S Hamid Nawab; Carlo J De Luca
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2009-12       Impact factor: 3.802

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