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Human activity classification
Müller, Jakub ; Smital, Lukáš (referee) ; Smíšek, Radovan (advisor)
This bachelor's thesis describes daily activity classification using accelerometric data. The first theoretical part summarizes the basics about daily activity and benefits that we get from monitoring it. In the next part of theory the principles of accelerometer inner workings are described. The last part of theory is dedicated to explaining the basics of neural networks and SVM. The aim of the practical part was to find a suitable dataset from a publicaly shared database, containing daily activity accelerometric data and also to collect our own data. Then performing classification using our own algorithm, optimizing it and finally evaluating the results.
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Classification of free living data sensed with Faros
Šalamoun, Jan ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
Topic of this master thesis is classification of free living data sensed with Faros. Faros is small compatible device which measure ECG and 3-axes accelerometric data. The first part of master thesis is find out how automatically measure free living activities by accelerometer and ECG. In next part was measured data of 8 activities from 10 probands. Automatic algorithms are made for this data in Matlab. This algorithms were used for this datasets and compare with manually recorded references. In the end of master thesis data were statistically evaluated.
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Human activity classification
Müller, Jakub ; Smital, Lukáš (referee) ; Smíšek, Radovan (advisor)
This bachelor's thesis describes daily activity classification using accelerometric data. The first theoretical part summarizes the basics about daily activity and benefits that we get from monitoring it. In the next part of theory the principles of accelerometer inner workings are described. The last part of theory is dedicated to explaining the basics of neural networks and SVM. The aim of the practical part was to find a suitable dataset from a publicaly shared database, containing daily activity accelerometric data and also to collect our own data. Then performing classification using our own algorithm, optimizing it and finally evaluating the results.
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Classification of free living data sensed with Faros
Šalamoun, Jan ; Vítek, Martin (referee) ; Smíšek, Radovan (advisor)
Topic of this master thesis is classification of free living data sensed with Faros. Faros is small compatible device which measure ECG and 3-axes accelerometric data. The first part of master thesis is find out how automatically measure free living activities by accelerometer and ECG. In next part was measured data of 8 activities from 10 probands. Automatic algorithms are made for this data in Matlab. This algorithms were used for this datasets and compare with manually recorded references. In the end of master thesis data were statistically evaluated.
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