Publications
Journal Article
Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs
ACM Transactions on Multimedia Computing, Communications, and Applications 16, no. 2 (2020): 1-19.Status: Published
Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | ACM Transactions on Multimedia Computing, Communications, and Applications |
| Volume | 16 |
| Issue | 2 |
| Pagination | 1 - 19 |
| Date Published | Mar-06-2021 |
| Publisher | ACM |
| Place Published | New York |
| ISSN | 1551-6857 |
| URL | https://dl.acm.org/doi/10.1145/3377882 |
| DOI | 10.1145/3377882 |
Proceedings, refereed
Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros
In The ACM Multimedia Systems Conference (MMSys). The ACM Multimedia Systems Conference (MMSys): ACM, 2020.Status: Published
Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros
Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool as well as a reproducible methodology to extend on the dataset. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros, an iconic and famous video game. The sensor data is collected through an Empatica E4 wristband, which provides high-quality measurements and is graded as a medical device. In addition to the dataset and the methodology for data collection, we present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing Super Mario Bros. With the dataset and the collection methodology we aim to contribute to research on emotionally aware machine learning algorithms, focusing on reinforcement learning and multimodal data fusion. We believe that the presented dataset can be interesting for a manifold of researchers to explore exciting new interdisciplinary questions.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | The ACM Multimedia Systems Conference (MMSys) |
| Publisher | ACM |
| Place Published | The ACM Multimedia Systems Conference (MMSys) |
| URL | https://dl.acm.org/doi/10.1145/3339825.3394939 |
| DOI | 10.1145/3339825.3394939 |
Proceedings, refereed
Fusion of multiple representations extracted from a single sensor’s data for activity recognition using CNNs
In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019.Status: Published
Fusion of multiple representations extracted from a single sensor’s data for activity recognition using CNNs
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | 2019 International Joint Conference on Neural Networks (IJCNN) |
| Pagination | 1–6 |
| Publisher | IEEE |
Heart Rate Prediction from Head Movement during Virtual Reality Treatment for Social Anxiety
In 2019 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2019.Status: Published
Heart Rate Prediction from Head Movement during Virtual Reality Treatment for Social Anxiety
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | 2019 International Conference on Content-Based Multimedia Indexing (CBMI) |
| Pagination | 1–5 |
| Publisher | IEEE |
One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression
In Proceedings of the 4th International Workshop on Multimedia for Personal Health & Health Care - HealthMedia '19. New York, NY, USA: ACM Press, 2019.Status: Published
One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression
Nowadays, it has become possible to measure different human activities using wearable devices. Besides measuring the number of daily steps or calories burned, these datasets have much more potential since different activity levels are also collected. Such data would be helpful in the field of psychology because it can relate to various mental health issues such as changes in mood and stress. In this paper, we present a machine learning approach to detect depression using a dataset with motor activity recordings of one group of people with depression and one group without, i.e., the condition group includes 23 unipolar and bipolar persons, and the control group includes 32 persons without depression. We use convolutional neural networks to classify the depressed and nondepressed patients. Moreover, different levels of depression were classified. Finally, we trained a model that predicts MontgomeryÅsberg Depression Rating Scale scores. We achieved an average F1-score of 0.70 for detecting the control and condition groups. The mean squared error for score prediction was approximately 4.0.
| Afilliation | Machine Learning |
| Project(s) | Department of Holistic Systems |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | Proceedings of the 4th International Workshop on Multimedia for Personal Health & Health Care - HealthMedia '19 |
| Pagination | 9-15 |
| Date Published | 10/2019 |
| Publisher | ACM Press |
| Place Published | New York, NY, USA |
| ISBN Number | 9781450369145 |
| URL | http://dl.acm.org/citation.cfm?doid=3347444http://dl.acm.org/citation.cf... |
| DOI | 10.1145/334744410.1145/3347444.3356238 |
