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

need for temporal normalisation.
In the present study only accelerations were considered to define the kinematics
signature of body-segments coordination. This choice was driven by the fact that inertial
markers and accelerometers are a mature technology for a daily and ambulatory
measurement of human motion. Moreover, due to the focus on the PAs “walking”,
“running”, and “cycling”, fewer markers localisations were considered for the arms than
for the legs because the present PAs involved more lower limbs than upper limbs. The
body segment medio-lateral acceleration has also not been considered since the
movement of the segments was scarcely produced along that direction for the PAs to
classify. These medio-lateral accelerations, other markers, but also other kinematics
parameters e.g 3D trajectories, and velocities could be considered. Nevertheless, it
would increase dramatically the computation time.
According to our results, each PA had particular patterns of body segments coordination
as demonstrated by significantly smaller intra-activity HDs than inter-activity HDs of
body segments accelerations. The results also show that the vertical accelerations of the
knee markers were the best to distinguish the three PAs walking/running/cycling.
For posture classification, we identified the needs of orientation for thighs and trunk.
Now, as methods using extended Kalman filters are able to compute these orientations
from inertial sensors, the PAs walking/running/ walking but also the postures
standing/lying/sitting can be classified by the use of only three inertial sensors located
on the thighs and on the sacrum,.
The present algorithm has been 100% successful to classify the different
postures/physical activities “walking” and “cycling”, but only 80% for the PA
“running” at maximal pace. This result can be explained by the fact that due to the use
of an optoelectronical motion capture device, the measure volume was limited. Thus for
some subjects we did not always obtain a complete cycle of “running” at maximal pace
as subjects often began to decelerate in order to stop their run. This result is due to a
bias of our motion capture protocol. However, the good results with the subjects for
which at least a complete full pace running cycle had been obtained convinced us about
the robustness of the algorithm even for the “running” classification.
5. REFERENCES
1. Gebruers, N., Vanroy, C., Truijen, S., Engelborghs, S. and De Deyn, P.P.,
Monitoring of physical activity after stroke: a systematic review of accelerometrybased
measures. Arch Phys Med Rehabil., 2010, Vol. 91, 288-297.
2. Mattila, J., Ding, H., Mattila, E. and Sarela, A., Mobile tools for home-based cardiac
rehabilitation based on heart rate and movement activity analysis. Conf Proc IEEE
Eng Med Biol Soc 2009, 2009, 6448-6452.
3. Bonomi, A.G., Goris, A.H., Yin, B. and Westerterp, K.R. Detection of type, duration,
and intensity of physical activity using an accelerometer. Med Sci Sports Exerc.,
2009, Vol. 41, 1770-1777.
4. Liu, S.H. and Chang, Y.J., Using accelerometers for physical actions recognition by
a neural fuzzy network. Telemed J E Health. 2009, Vol. 15, 867-876.
5. Zatsiorsky, V.M., Kinematics of human motion. In: Human Kinetics (Eds.),
Champaign, IL, 1998.
6. Gregor, R.J., Broker, J.P. and Ryan, M.M., The biomechanics of cycling. Exerc
Sport Sci Rev., 1991, Vol. 19, 127-169.
7. Rote, G., Computing the minimum Hausdorff distance between two point sets on a