This paper explores a method for deducing the affective state of runners using his/her movements. The movements are measured on the arm using a smartphone’s built-in accelerometer. Multiple features are derived from the measured data. We studied which features are most predictive for the affective state by looking at the correlations between the features and the reported affect. We found that changes in runners’ movement can be used to predict change in affective state
Knowledge of users’ affective states can improve their interaction with smartphones by providing mor...
One of the most interesting applications of mobile sensing is monitoring of individual behavior, esp...
Most of the research in multi-modal affect detection has been done in laboratory environment. Little...
Background Although people generally feel more positive and more energetic in the aftermath of exerc...
Wearable technology is playing an increasing role in the development of user-centric applications. I...
The fluctuation of affective states is a contributing factor to sport performance variability. The c...
The need to monitor patients after they leave the hospital or clinics is of growing concern and doct...
Recent research indicates that affective responses during exercise are an important determinant of f...
Mobile sensing technologies and machine learning techniques have been successfully exploited to buil...
Over the years a lot of research efforts have been put into recognizing human emotions from facial e...
Automatic emotion recognition is of great value in many applications, however, to fully display the ...
There is some evidence for a positive relationship between affective responses to exercise and futur...
Extracting information about emotion from heart rate in real life is challenged by the concurrent ef...
Extracting information about emotion from heart rate in real life is challenged by the concurrent ef...
Most of the existing studies focus on physical activities recognition, such as running, cycling, swi...
Knowledge of users’ affective states can improve their interaction with smartphones by providing mor...
One of the most interesting applications of mobile sensing is monitoring of individual behavior, esp...
Most of the research in multi-modal affect detection has been done in laboratory environment. Little...
Background Although people generally feel more positive and more energetic in the aftermath of exerc...
Wearable technology is playing an increasing role in the development of user-centric applications. I...
The fluctuation of affective states is a contributing factor to sport performance variability. The c...
The need to monitor patients after they leave the hospital or clinics is of growing concern and doct...
Recent research indicates that affective responses during exercise are an important determinant of f...
Mobile sensing technologies and machine learning techniques have been successfully exploited to buil...
Over the years a lot of research efforts have been put into recognizing human emotions from facial e...
Automatic emotion recognition is of great value in many applications, however, to fully display the ...
There is some evidence for a positive relationship between affective responses to exercise and futur...
Extracting information about emotion from heart rate in real life is challenged by the concurrent ef...
Extracting information about emotion from heart rate in real life is challenged by the concurrent ef...
Most of the existing studies focus on physical activities recognition, such as running, cycling, swi...
Knowledge of users’ affective states can improve their interaction with smartphones by providing mor...
One of the most interesting applications of mobile sensing is monitoring of individual behavior, esp...
Most of the research in multi-modal affect detection has been done in laboratory environment. Little...