Tese
Avaliação da marcha humana utilizando palmilhas sensorizadas e algoritmo de aprendizado de máquina
Fecha
2022-12-16Autor
Diego Henrique Antunes Nascimento
Institución
Resumen
Human gait analysis can provide an excellent source for identifying and predicting pathologies and injuries. In this respect, instrumented insoles also have a great potential for
extracting gait information. However, there are technical and commercial difficulties that
can limit the diffusion of this technology. The insoles available on the market have a high
cost and closed software. On the other hand, the low-cost academic prototypes do not
present enough information about the design parameters, manufacturing techniques, and
guidelines for developing. In addition, data processing is highly complex and requires a
high degree of user knowledge. The present study proposes a proof-of-concept of a system
based on vertical ground reaction force (vGRF) acquisition with a sensorized insole that
uses a machine learning algorithm to identify different patterns of vGRF and extract biomechanical characteristics that can help during clinical evaluation. For this, a low-cost
instrumented insole was developed, with customized sensors that was validated using a
double-belt instrumented treadmill (Bertec, 1000 Hz, USA) as the “gold standard”. A
new calibration methodology was developed, which increased by 12% the correlation with
the force plate in relation to the usual calibration method. The study had the participation of 32 volunteers (18 men and 14 women). Each volunteer walked on the instrumented
treadmill while wearing an experimental resistive sensorized insole. The acquired data are
processed using algorithms based on machine learning responsible to to identify different
patterns of vGRF and extract biomechanical characteristics that can help during clinical
evaluation. The data was clustered by an Immunological Algorithm (IA) based on vGRF
during gait. These clusters underwent a data mining process using the Classification and
Regression Tree algorithm (CART), where the main characteristics of each group were
extracted, and some rules for gait classification were created. As a result, the system
proposed was able to collect and process the biomechanical behavior of gait. After the
application of IA and CART algorithms, six groups were found. The characteristics of
each of these groups were extracted and verified the capability of the system to collect
and process the biomechanical behavior of gait, offering verification points that can help
focus during a clinical evaluation.