Tiago Almeida
Position: Doctoral Student School/office: School of Science and TechnologyEmail: tiago.almeida@oru.se
Phone: +46 19 303725
Room: T2226
About Tiago Almeida
Tiago Rodrigues de Almeida holds an MSc in Mechanical Engineering from the University of Aveiro, Portugal. In October 2020, he started his Ph.D. in Computer Science at Örebro University. Moreover, he is enrolled in the Wallenberg AI, Autonomous Systems program (WASP).
His main research focus is human motion forecasting and its possible connections with mental well-being. For this, he is using Deep Learning to leverage novel frameworks for human motion prediction and analysis.
Publications
Articles in journals
- Gutiérrez Maestro, E. , Almeida, T. R. d. , Schaffernicht, E. & Martinez Mozos, O. (2023). Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities. Applied Sciences, 13 (9).
- Almeida, T. , Santos, V. , Martinez Mozos, O. & Lourenco, B. (2021). Comparative Analysis of Deep Neural Networks for the Detection and Decoding of Data Matrix Landmarks in Cluttered Indoor Environments. Journal of Intelligent and Robotic Systems, 103 (1).
Conference papers
- Rodrigues de Almeida, T. & Martinez Mozos, O. (2023). Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction. Paper presented at 26th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2023), Bilbao, Bizkaia, Spain, September 24-28, 2023.
- Almeida, T. , Rudenko, A. , Schreiter, T. , Zhu, Y. , Gutiérrez Maestro, E. , Morillo-Mendez, L. , Kucner, T. P. , Martinez Mozos, O. & et al. (2023). THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction. In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW). Paper presented at IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Paris, France, October 2-6, 2023. (pp. 2192-2201). IEEE.
- Almeida, T. R. d. , Gutiérrez Maestro, E. & Martinez Mozos, O. (2022). Context-free Self-Conditioned GAN for Trajectory Forecasting. In: Wani, MA; Kantardzic, M; Palade, V; Neagu, D; Yang, L; Chan, KY, 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022 Proceedings. Paper presented at 21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), Nassau, Bahamas, December 12-14, 2022. (pp. 1218-1223). IEEE.
- Schreiter, T. , Almeida, T. R. d. , Zhu, Y. , Gutiérrez Maestro, E. , Morillo-Mendez, L. , Rudenko, A. , Kucner, T. P. , Martinez Mozos, O. & et al. (2022). The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized. Paper presented at 31st IEEE International Conference on Robot & Human Interactive Communication, Naples, Italy, August 29 - September 2, 2022.