Literature DB >> 16438228

Numerical validation of linear accelerometer systems for the measurement of head kinematics.

Paolo Cappa1, Lorenzo Masia, Fabrizio Patanè.   

Abstract

The purpose of this study was to analytically exploit the capabilities of head-mounted systems instrumented with linear accelerometers (ACs) for field use in redundant configurations. We simulated different headsets equipped with uni-, bi- or triaxial sensors with a number of axes that lie in the range of 12-24; the ACs were located on a hemispherical surface by adopting a priori criterion while their orientation was randomized. In addition, for a comparative purpose the nine accelerometer scheme (one triaxial AC and three biaxial ACs addressed in the following as "3-2-2-2 configuration") was also analyzed in the present paper. We simulated and statistically assessed the performances of hemispherical headsets in the test case of a healthy subject walking freely at normal pace over level ground. The numerical results indicated that a well designed instrumented headset can retrieve the angular acceleration and (a0-g) component with rms errors of about 2% and 0.5%, respectively, and angular velocity with a drift error of about 20% in a 6 s trial. On the contrary, the pose of the headset cannot be evaluated because of the drift induced by the integration process. In general, we can state that headsets with uni-, bi- or triaxial ACs have comparable performances. The main implications of the above-mentioned observations are (a) neither expensive triaxial ACs nor assembling procedure based on the use of orthogonal mounting blocks are needed; (b) redundant arrays of low-cost uni- or biaxial ACs can effectively be used to reach adequate performances in biomechanical studies where head acceleration and velocity are investigated; (c) while estimates of angular acceleration with accelerometers are accurate, estimations of angular velocities, linear velocities and pose are not.

Mesh:

Year:  2005        PMID: 16438228     DOI: 10.1115/1.2049329

Source DB:  PubMed          Journal:  J Biomech Eng        ISSN: 0148-0731            Impact factor:   2.097


  2 in total

1.  Machine learning methods for classifying human physical activity from on-body accelerometers.

Authors:  Andrea Mannini; Angelo Maria Sabatini
Journal:  Sensors (Basel)       Date:  2010-02-01       Impact factor: 3.576

2.  In Memoriam: Paolo Cappa.

Authors:  Eduardo Palermo; Stefano Rossi; Fabrizio Patanè; Jeffrey Laut; Maurizio Porfiri
Journal:  Sensors (Basel)       Date:  2017-11-18       Impact factor: 3.576

  2 in total

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