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

CLASSIFICATION OF PHYSICAL ACTIVITY BASED ON A
BIOMECHANICAL APPROACH
1. ABSTRACT
L. Fradet 1 and F. Marin 2
Recent developments in inertial sensors enable henceforth an ambulatory
characterisation of movement. This recent development offers new opportunities for
rehabilitation purpose such as patients’ physical activity (PA) monitoring. This typical
application requires algorithms for the recognition of the PA recommended by the
clinical staff. Currently, the algorithms proposed are based on decisional trees and
neural networks built on data of young healthy subjects. The relevance of such
algorithms is questionable for pathological or elderly subjects. The aim of the present
study is to propose a new method adapted to all kind of subjects using biomechanical
characteristics of the PAs/postures to classify.
Firstly, an algorithm for PA classification was proposed based on the biomechanical
definition of the postures to classify and on the hypothesis that to each PA corresponded
a specific body-segments coordination. To determine this specific body-segments
coordination, a motion capture was performed on 16 subjects of heterogeneous age and
physical condition taking successively different posture (sitting/standing/lying) and
performing different PAs (walking/running/cycling) at different pace
(slow/normal/fast). Body-segments orientation and 16 vertical and horizontal
accelerations were afterwards computed. All the combinations from 2 to 3 accelerations
out of the 16 accelerations were tested to define which one was the most appropriate to
distinguish the PAs by the mean of the Hausdorff Distance.
The knees vertical acceleration showed the best ability for distinguishing the PAs. This
result was then tested on the algorithm for PA classification. 94% of the trials of the
experimental data were correctly classified.
2. INTRODUCTION
Recent advances have resulted in the development of light-weight and wireless inertial
sensors enabling thus measures of motion kinematics over a long period of time. These
developments can be especially exploited for rehabilitation purposes dealing with
physical activity monitoring [1]. In that case, the aim is to detect the physical activities
(PA) performed by the user in order to compare their PA practice with that
recommended by the clinical staff [2]. For this, the activities performed by the
individual are recognized and classified by interpreting signals provided by sensors,
usually accelerometers. Many algorithms based on decisional trees [3], neural networks
[Erreur ! Source du renvoi introuvable.] taking as inputs diverse parameters
1 PhD, UMR7338 Biomécanique et Bioingénérie, Université de Technologie de Compiègne, Centre de
Recherche de Royallieu - Rue du Dr Schweitzer, BP 20529, 60205 Compiègne cedex , France
2 Professor, UMR7338 Biomécanique et Bioingénérie, Université de Technologie de Compiègne, Centre
de Recherche de Royallieu - Rue du Dr Schweitzer, BP 20529, 60205 Compiègne cedex , France