RESUMO
Transfer is a key ability and allows greater interact with the environment and social participation. Conversely, paraplegics have great risk of pain and injury in the upper limbs due to joint overloads during activities of daily living, like transfer. The main goal of this study is to verify if the use of functional electrical stimulation (FES) in the lower limbs of paraplegic individuals can assist the sitting pivot transfer (SPT). The secondary objective is to verify if there is a greater participation of the lower limbs during lift pivot phase. A preliminary study was done with one complete paraplegic individual. Temporal parameters were calculated and a kinetic assessment was done during the SPT. The preliminary results showed the feasibility of FES for assisting the SPT.
RESUMO
OBJECTIVE: To propose a semi-automated method for pattern classification to predict individuals' stage of growth based on morphologic characteristics that are described in the modified cervical vertebral maturation (CVM) method of Baccetti et al. MATERIALS AND METHODS: A total of 188 lateral cephalograms were collected, digitized, evaluated manually, and grouped into cervical stages by two expert examiners. Landmarks were located on each image and measured. Three pattern classifiers based on the Naïve Bayes algorithm were built and assessed using a software program. The classifier with the greatest accuracy according to the weighted kappa test was considered best. RESULTS: The classifier showed a weighted kappa coefficient of 0.861 ± 0.020. If an adjacent estimated pre-stage or poststage value was taken to be acceptable, the classifier would show a weighted kappa coefficient of 0.992 ± 0.019. CONCLUSION: Results from this study show that the proposed semi-automated pattern classification method can help orthodontists identify the stage of CVM. However, additional studies are needed before this semi-automated classification method for CVM assessment can be implemented in clinical practice.