The recognition of motion patterns belongs to very important research areas related to neurology, rehabilitation, and robotics. It is based on modern sensor technologies and general mathematical methods, multidimensional signal processing, and machine learning. The present paper is devoted to the detection of features associated with accelerometric data acquired by 31 time-synchronized sensors located at different parts of the body. Experimental data sets were acquired from 25 individuals diagnosed as healthy controls and ataxic patients. The proposed method includes the application of the discrete Fourier transform for the estimation of the mean power in selected frequency bands and the use of these features for data segments classification. The study includes a comparison of results obtained from signals recorded at different positions. Evaluations are based on classification accuracy and cross-validation errors estimated by support vector machine, Bayesian, nearest neighbours (k-NN], and neural network (NN) methods. Results show that highest accuracies of 77.1%, 78.9%, 89.9%, 98.0%, and 98.5% were achieved by NN method for signals acquired from the sensors on the feet, legs, uplegs, shoulders, and head/spine, respectively, recorded in 201 signal segments. The entire study is based on observations in the clinical environment and suggests the importance of augmented reality to decisions and diagnosis in neurology.
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This research was supported by grant projects of the Ministry of Health of the Czech Republic (FN HK 00179906) and of the Charles University in Prague, Czech Republic (PROGRES Q40), as well as by the project PERSONMED—Centre for the Development of Personalized Medicine in Age-Related Diseases, Reg. No. CZ.02.1.01-0.0-0.0-17_048-0007441, co-financed by the European Regional Development Fund (ERDF), the governmental budget of the Czech Republic, and grant INTER-ACTION LTAIN19007.
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Dostál, O., Procházka, A., Vyšata, O. et al. Recognition of motion patterns using accelerometers for ataxic gait assessment. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-05103-2
- Multidimensional signal analysis
- Computational intelligence
- Machine learning
- Ataxic gait
- Motion classification