Pavlos Msaouel, Theocharis Kappos, Athanasios Tasoulis, Alexandros P Apostolopoulos, Ioannis Lekkas, Elli-Sophia Tripodaki, Nikolaos C Keramaris
Assessment of cognitive biases and biostatistics knowledge of medical residents: a multicenter, cross-sectional questionnaire study
Medical education online
recent studies highlighted possibility using surface electromyographic emg signals develop humancomputer interfaces also able recognize complex motor tasks involving hand handwriting digits however automatic recognition words emg information yet studied aim study investigate feasibility using combined forearm wrist emg probes solving handwriting recognition problem 30 words consolidated machinelearning techniques aggregating stateoftheart features extracted time frequency domains six healthy subjects three females three males aged 25 40 years recruited study two tests pattern recognition conducted assess possibility classifying fine hand movements emg signals first test designed assess feasibility using consolidated myoelectric control technology shallow machinelearning methods field handwriting detection second test implemented assess specific feature extraction schemes guarantee high performances limited complexity processing pipeline among support vector machine linear discriminant analysis knearest neighbours knn last one showed best classification performances 30word classification problem mean accuracy 95 85 using features specific feature set known tdar respectively obtained results confirmed validity using combined wrist forearm emg data intelligent handwriting recognition pattern recognition approaches real scenarios