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

accelerations combination). However, according to the post-hoc tests, at least one intraactivity
HD (HDw, HDr, HDc) has not been significantly smaller (p>0.05) than at least
one inter-activity HD (HDwr, HDwc, HDrc) for 219 accelerations combinations.
The percentage of intra-activity HDs inferior to inter-activity HDs defined which
combination of accelerations clearly differentiated each PA. Table 2 shows the five best
n accelerations combinations with the highest mean rating. The best combination is the
combinations of the vertical accelerations of the knees.
4.1 PA classification
To test the robustness of the algorithm and the validity of the parameters, the algorithm
was tested on the experimental data previously described. The thresholds were set
according to the results of a previous study. The threshold T1 for the acceleration
detecting activity was set to 0.5m.s -2 , the threshold T2 for the autocorrelation
determining cyclicity to 0.4, and the threshold T3 for the HD determining known
activity was set to 0.8. The orientation of the trunk and thighs was defined by the
sacrum and that of the midpoint of the clavicula and the two knee markers. The previous
results were used to set Cacc, namely the combination of accelerations used to
differentiate the PAs. According to these results, the PAs “walking”, “running”, and
“cycling” were identified by the vertical acceleration of the knees markers. The specific
reference Caccwalk, Caccrun, Cacccycle were extracted for each subject, and for each PA
category randomly among the trials performed at a moderate pace.
To test the algorithm, it has been computed how many trials (in %) were correctly
classified for each posture and PA. Except for the “running”, for all 16 subjects at all
levels of activity, our algorithm classified posture and PA (Table 2) correctly. For the
activity “running”, only 80% of the trials were correctly classified. The errors were
made for trials “running” performed at maximum pace.
5. DISCUSSION
The present study proposed a complete algorithm for physical activity (PA)
classification. It included the demonstration of the choice of the inputs parameters and
the test on a heterogeneous population that performed PA at difference levels of
activity.
The proposed algorithm presents several originalities. First, for the postures and PAs, it
is based on biomechanical foundations. For the postures, literal definitions were used to
define the mean they could be characterised. PAs were classified according to n-space
curves of accelerations (2≤n≤3) chosen to represent body-segments coordination, which
are known to be typical to each PA performed. It did not use post-processing from
classical signal processing theory e.g. frequencies analysis and entropy calculation.
As the reference curves of accelerations can easily be recorded for each subject, not
only the algorithm can catch specificities of the subject but also be updated to take into
account his evolution, making it appropriated for all type of subjects and applications.
This algorithm is also flexible as a library of PAs can easily be added, under the
condition to identify the set of accelerations according the methodology presented in
this study. Inside the proposed algorithm, the Hausdorff Distance (HD) [7] was used to
characterise the similarity between the body segments accelerations combination. Other
similarity measures exist as proposed in the field of shape classification but HD has the
advantage of having no dimension restriction on the curves to compare which avoids the