TY - JOUR
T1 - Pronation and supination analysis based on biomechanical signals from Parkinson's disease patients
AU - Garza-Rodríguez, Alejandro
AU - Sánchez-Fernández, Luis Pastor
AU - Sánchez-Pérez, Luis Alejandro
AU - Ornelas-Vences, Christopher
AU - Ehrenberg-Inzunza, Mariane
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2018/1
Y1 - 2018/1
N2 - In this work, a fuzzy inference model to evaluate hands pronation/supination exercises during the MDS-UPDRS motor examination is proposed to analyze different extracted features from the bio-mechanical signals acquired from patients with Parkinson's disease (PD) in different stages of severity. Expert examiners perform visual assessments to evaluate several aspects of the disease. Some previous work on this subject does not contemplate the MDS-UPDRS guidelines. The method proposed in this work quantifies the biomechanical features examiners evaluate. The extracted characteristics are used as inputs of a fuzzy inference model to perform an assessment strictly attached to the MDS-UPDRS. The acquired signals are processed by techniques of digital signal processing and statistical analysis. The experiments were performed in collaboration with clinicians and patients from different PD associations and institutions. In total, 210 different measurements of patients with PD, plus 20 different measurements of healthy control subjects were performed. With objective values rated by several feature extraction procedures there is the possibility to track down the disease evolution in a patient, and to detect subtle changes in the patient's condition.
AB - In this work, a fuzzy inference model to evaluate hands pronation/supination exercises during the MDS-UPDRS motor examination is proposed to analyze different extracted features from the bio-mechanical signals acquired from patients with Parkinson's disease (PD) in different stages of severity. Expert examiners perform visual assessments to evaluate several aspects of the disease. Some previous work on this subject does not contemplate the MDS-UPDRS guidelines. The method proposed in this work quantifies the biomechanical features examiners evaluate. The extracted characteristics are used as inputs of a fuzzy inference model to perform an assessment strictly attached to the MDS-UPDRS. The acquired signals are processed by techniques of digital signal processing and statistical analysis. The experiments were performed in collaboration with clinicians and patients from different PD associations and institutions. In total, 210 different measurements of patients with PD, plus 20 different measurements of healthy control subjects were performed. With objective values rated by several feature extraction procedures there is the possibility to track down the disease evolution in a patient, and to detect subtle changes in the patient's condition.
KW - Biomechanical signals
KW - Feature extraction
KW - Fuzzy logic
KW - MDS-UPDRS
KW - Parkinson
KW - Pronation-supination
UR - http://www.scopus.com/inward/record.url?scp=85031329120&partnerID=8YFLogxK
U2 - 10.1016/j.artmed.2017.10.001
DO - 10.1016/j.artmed.2017.10.001
M3 - Artículo
C2 - 29042162
SN - 0933-3657
VL - 84
SP - 7
EP - 22
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
ER -