ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

computed from acceleration frequencies obtained with one or more sensors placed on
various locations have been proposed. However, neither the choice of the parameters,
neither the positions of the sensors have been justified. Now, as demonstrated by
biomechanical studies, each PA can be defined by specific body-segment movements
and organisations. For instance, walking and running consist both in moving by lifting
alternately the feet forwards but during walking, conversely to running, one part of a
foot is always on the ground, resulting in distinctive coordination patterns of the lowerleg
kinematics data [Erreur ! Source du renvoi introuvable.,6]. These observations
could be used for a new generation of algorithms for PA classification.
Moreover, the traditional algorithms proposed are implemented, trained -in the case of
neural networks- and tested with the use of healthy young subjects. In the context of
rehabilitation, the physical abilities of the subjects to monitor are not only
heterogeneous but are expected to evolve throughout the rehabilitation process. A
methodology that can easily take into account the specificities of the subject and their
evolutions has then to be proposed.
The present study proposes first an algorithm that can classify PAs based on the bodysegment
coordination but also a methodology that can be used to determine the inputs of
the algorithm, namely, the kinematics parameters that can be utilized to differentiate the
different physical activities.
3. MATERIAL AND METHOD
Figure 1: Algorithm for the PA classification; acc represents
accelerations of the activity/posture to classify; Caccwalk,
Caccrun, Cacccycle represent n-space reference curves
corresponding to n accelerations for subject specific reference
activities “walking”, “running” and “cycling”.
3.1 Architecture of algorithm
Figure 2: Markers positioning
The algorithm (Fig.1) classifies the postures sitting/standing/lying and the PAs
walking/running/cycling, which are typical PAs recommended during a rehabilitation
program. Only body-segment accelerations and orientations were considered as inputs
for the algorithm since they are provided by inertial sensors that can record over a long
period of time.
With this algorithm, the subject is considered as being immobile when the accelerations
are above a threshold T1. In that case, postures are classified based on their literal
definition. “Standing” means indeed “to be in an upright position on the feet”, which