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Intelligent Recognition of the Smartphone User's Activity
Pustka, Michal ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
This thesis deals with real-time human activity recognition (eg, running, walking, driving, etc.) using sensors which are available on current mobile devices. The final product of this thesis consists of multiple parts. First, an application for collecting sensor data from mobile devices. Followed by a tool for preprocessing of collected data and creation of a data set. The main part of the thesis is the design of convolutional neural network for activity classification and subsequent use of this network in an Android mobile application. The combination of previous parts creates a comprehensive framework for detection of user activities. Finally, some interesting experiments were made and evaluated (eg, the influence of specific sensors on detection precision).
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Utilization of Motion Sensor Data for User Activity Analysis
Eršek, Martin ; Zemčík, Pavel (referee) ; Beran, Vítězslav (advisor)
This bachelor thesis aims to provide a design and implementation of an algorithm for analysis of user activity based on data from motion sensors. The thesis explores possibilities of classification and counting repetitions of 7 basic body-weight exercises, namely: push ups, squats, planks, sit-ups, seated knee raises, tricep dips and lunges. Data from motion sensors are collected by a mobile device located in top pocket of exercising user's trousers. Selection of used methods and their parameters as well as number and type of extracted features is chosen with regard to low computational complexity. When designing a solution, emphasis was put on the fact that it is irrelevant how the device is positioned in the user's pocket. For the thesis, a dataset containing 7 training sessions from 4 different users was created. Designed method was implemented as a desktop application with Command Line Interface and consequently validated on the created dataset. The solution was able to reach metrics of F1-score in range 45.3 % - 74.9 % for analysis and counting repetitions of an unseen user's training session. For an unseen training session of a known user, the metrics of F1-score was up to 94 %.
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Human Activity Recognition Using Smartphone
Novák, Andrej ; Červenák, Rastislav (referee) ; Burget, Radim (advisor)
The increase of mobile smartphones continues to grow and with it the demand for automation and use of the most offered aspects of the phone, whether in medicine (health care and surveillance) or in user applications (automatic recognition of position, etc.). As part of this work has been created the designs and implementation of the system for the recognition of human activity on the basis of data processing from sensors of smartphones, along with the determination of the optimal parameters, recovery success rate and comparison of individual evaluation. Other benefits include a draft format and displaying numerous training set consisting of real contributions and their manual evaluation. In addition to the main benefits, the software tool was created to allow the validation of the elements of the training set and acquisition of features from this set and software, that is able with the help of deep learning to train models and then test them.
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|
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Utilization of Motion Sensor Data for User Activity Analysis
Eršek, Martin ; Zemčík, Pavel (referee) ; Beran, Vítězslav (advisor)
This bachelor thesis aims to provide a design and implementation of an algorithm for analysis of user activity based on data from motion sensors. The thesis explores possibilities of classification and counting repetitions of 7 basic body-weight exercises, namely: push ups, squats, planks, sit-ups, seated knee raises, tricep dips and lunges. Data from motion sensors are collected by a mobile device located in top pocket of exercising user's trousers. Selection of used methods and their parameters as well as number and type of extracted features is chosen with regard to low computational complexity. When designing a solution, emphasis was put on the fact that it is irrelevant how the device is positioned in the user's pocket. For the thesis, a dataset containing 7 training sessions from 4 different users was created. Designed method was implemented as a desktop application with Command Line Interface and consequently validated on the created dataset. The solution was able to reach metrics of F1-score in range 45.3 % - 74.9 % for analysis and counting repetitions of an unseen user's training session. For an unseen training session of a known user, the metrics of F1-score was up to 94 %.
|
|
|
Intelligent Recognition of the Smartphone User's Activity
Pustka, Michal ; Goldmann, Tomáš (referee) ; Drahanský, Martin (advisor)
This thesis deals with real-time human activity recognition (eg, running, walking, driving, etc.) using sensors which are available on current mobile devices. The final product of this thesis consists of multiple parts. First, an application for collecting sensor data from mobile devices. Followed by a tool for preprocessing of collected data and creation of a data set. The main part of the thesis is the design of convolutional neural network for activity classification and subsequent use of this network in an Android mobile application. The combination of previous parts creates a comprehensive framework for detection of user activities. Finally, some interesting experiments were made and evaluated (eg, the influence of specific sensors on detection precision).
|
|
|
Human Activity Recognition Using Smartphone
Novák, Andrej ; Červenák, Rastislav (referee) ; Burget, Radim (advisor)
The increase of mobile smartphones continues to grow and with it the demand for automation and use of the most offered aspects of the phone, whether in medicine (health care and surveillance) or in user applications (automatic recognition of position, etc.). As part of this work has been created the designs and implementation of the system for the recognition of human activity on the basis of data processing from sensors of smartphones, along with the determination of the optimal parameters, recovery success rate and comparison of individual evaluation. Other benefits include a draft format and displaying numerous training set consisting of real contributions and their manual evaluation. In addition to the main benefits, the software tool was created to allow the validation of the elements of the training set and acquisition of features from this set and software, that is able with the help of deep learning to train models and then test them.
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