Sensor data and social networks
Our sensor data-driven approach, ultimately, yields a detailed, precise picture of children’s interactions with peers and their direct environment, complementary to field observation and questionnaires. This sensor data-driven approach includes GPS loggers to obtain children’s geographical locations, Bluetooth-based proximity tags to examine face-to-face contacts of individuals, and multi-motion receivers (MMR) to obtain physical activity levels. Multimodal analyses of sensor data enables monitoring of children's movements within the environment, their contacts with peers and their activities in the schoolyard during unstructured breaks at school. Data and results obtained through this novel method are validated using video observations of these schoolyard events. In computer science, we design algorithms that extract spatiotemporal patterns representing social behaviors of children. Overall, this research line focuses on two main areas, automated monitoring, and social network analyses.
1. automated monitoring
How can machine learning algorithms automatically identify different social behavioral patterns from spatiotemporal data? Recent developments in artificial intelligence enabled us to design an end-to-end machine learning framework to automatically learn and distinguish group behavior from spatiotemporal data. You could read more about this research in the paper under Key Publications.
2. social network analyses
How does the physical space influence our social network? Using spatiotemporal social network, we understand where children are spatially and socially positioned in their network, how strong their connections to the network is, how schoolyard features influence individuals’ social network and finally how such spatiotemporal network changes over time.
Collaboration with eScience
In collaboration with eScience center, we now further enhance our sensing system and build a fully-automated method for measuring individual differences during play, by developing software for smartwatches that simultaneously records social behavior, movements, locations, and heart rate via the built-in sensors in smartwatches, and which can also collect children's subjective experiences during play in real time. Read more about this project.
People involved
Computer Science - Maedeh Nasri, Mitra Baratchi (Leiden University).
Psychology - Carolien Rieffe, Adva Eichengreen, Brenda Sousa da Silva, Yung-Ting Tsou, Jiayin Zhao (University of Twente / Leiden University), Guida Veiga (University of Evora).
Architecture - Alexander Koutamanis (TU Delft).
Governance - Sarah Giest (Governance, Leiden University), Ellen Starke (School Alliances Amsterdam).
Key publications
- Nasri, M., Tsou, Y.T., Koutamanis, A., Baratchi, M., Giest, S., Reidsma, D., & Rieffe, C. (2022). A novel data-driven approach to examine children’s movements and social behaviour in schoolyard environments Children, 9, 1177 (Special Issue: Child Psychomotricity: Development, Assessment, and Intervention). Doi: 10.3390/children9081177.
- Nasri, M., Fang, Z., Baratchi, M., Englebienne, G., Wang, S., Koutamanis, A., & Rieffe, C. (2023). A GNN-Based Architecture for Group Detection from Spatio-Temporal Trajectory Data In Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings (pp. 327-339). Cham: Springer Nature Switzerland.
- Veiga, G., Ketelaar, L., de Leng, W., Cachucho, R., Kok, J. N., Knobbe, A., Neto, C., & Rieffe, C. (2017). Alone at the playground European Journal of Developmental Psychology, 14, 44-61. doi: 10.1080/17405629.2016.1145111.
- Laanen, R., Nasri, M., van Dijk, R., Baratchi, M., Koutamanis, A., & Rieffe, C. (2023). Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology. arXiv preprint arXiv:2303.07107 doi: 10.48550/arXiv.2303.07107.
