Jaromír Salamon | University of West Bohemia (original) (raw)
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Papers by Jaromír Salamon
The Internet of Things world brings to our lives many opportunities to monitor our daily activiti... more The Internet of Things world brings to our lives many opportunities to monitor our daily activities by collecting data from various devices. Complementary to it, the data expressing opinions, suggestions, interpretations, contradictions, and uncertainties are more accessible within variety of online resources. This paper deals with collection and analysis of hard data representing the number of steps and soft data representing the sentiment of participants who underwent a pilot experiment. The paper defines outlines of the problem and presents possible sources of reliable data, sentiment evaluation, sentiment extraction using machine learning methods, and links between the data collected from IoT devices and sentiment expressed by the participant in a textual form. Then the results provided by using inferential statistics are presented. The paper is concluded by discussion and summarization of results and future work proposals.
Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, 2018
Automated detection of human stress from markers is very beneficial for the development of assist... more Automated detection of human stress from markers is very beneficial for the development of assistive technologies. Blood pressure, skin temperature, galvanic skin response or heart rate are typical physiological markers that help identify human stress. However, not only the human body itself but also the human mood expressed in short text messages can be a useful source of such information about stress. This paper focuses on detection of human stress using two different but synchronized sources of information, human heart rate and sentiment extracted from tweets. During the preliminary experiment lasting for two fifty-day periods, we obtained simultaneously 481 708 heart rate data samples from two wearables and sentiment from 2049 tweets. The tweet data contain a subjective sentiment evaluation that was recorded using positive and negative hashtags. A few states of stress were identified as the result of the data processing. The final discussion provides conclusions and recommendations for future research.
The Internet of Things world brings to our lives many opportunities to monitor our daily activiti... more The Internet of Things world brings to our lives many opportunities to monitor our daily activities by collecting data from various devices. Complementary to it, the data expressing opinions, suggestions, interpretations, contradictions, and uncertainties are more accessible within variety of online resources. This paper deals with collection and analysis of hard data representing the number of steps and soft data representing the sentiment of participants who underwent a pilot experiment. The paper defines outlines of the problem and presents possible sources of reliable data, sentiment evaluation, sentiment extraction using machine learning methods, and links between the data collected from IoT devices and sentiment expressed by the participant in a textual form. Then the results provided by using inferential statistics are presented. The paper is concluded by discussion and summarization of results and future work proposals.
Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, 2018
Automated detection of human stress from markers is very beneficial for the development of assist... more Automated detection of human stress from markers is very beneficial for the development of assistive technologies. Blood pressure, skin temperature, galvanic skin response or heart rate are typical physiological markers that help identify human stress. However, not only the human body itself but also the human mood expressed in short text messages can be a useful source of such information about stress. This paper focuses on detection of human stress using two different but synchronized sources of information, human heart rate and sentiment extracted from tweets. During the preliminary experiment lasting for two fifty-day periods, we obtained simultaneously 481 708 heart rate data samples from two wearables and sentiment from 2049 tweets. The tweet data contain a subjective sentiment evaluation that was recorded using positive and negative hashtags. A few states of stress were identified as the result of the data processing. The final discussion provides conclusions and recommendations for future research.