Analysis and decomposition of accelerometric signals of trunk and thigh obtained during the sit-to-stand movement
Medical & Biological Engineering & Computing , Volume 43 - Issue 2 p. 265- 272
Piezoresistive accelerometer signals are frequently used in movement analysis. However, their use and interpretation are complicated by the fact that the signal is composed of different acceleration components. The aim of the study was to obtain insight into the components of accelerometer signals from the trunk and thigh segments during four different sit-to-stand (STS) movements (self-selected, slow, fast and fullflexion). Nine subjects performed at least six trials of each type of STS movement. Accelerometer signals from the trunk and thigh in the sagittal direction were decomposed using kinematic data obtained from an opto-electronic device. Each acceleration signal was decomposed into gravitational and inertial components, and the inertial component of the trunk was subsequently decomposed into rotational and translational components. The accelerometer signals could be reliably reconstructed: mean normalised root mean square (RMS) trunk: 6.5% (range 3-12%), mean RMS thigh: 3% (range 2-5%). The accelerometric signals were highly characteristic and repeatable. The influence of the inertial component was significant, especially on the timing of the specific event of maximum trunk flexion in the accelerometer signal. The effect of inertia was larger in the trunk signal than in the thigh signal and increased with higher speeds. The study provides insight into the acceleration signal, its components and the influence of the type of STS movement and supports its use in STS movement analysis.
|Accelerometry, Ambulatory, Assessment, Decomposition, Sit-to-stand|
|Medical & Biological Engineering & Computing|
|Organisation||Department of Rehabilitation Medicine|
Janssen, W.G.M, Bussmann, J.B.J, Horemans, H.L.D, & Stam, H.J. (2005). Analysis and decomposition of accelerometric signals of trunk and thigh obtained during the sit-to-stand movement. Medical & Biological Engineering & Computing, 43(2), 265–272. doi:10.1007/BF02345965