Opportunistic Activity Recognition in IoT Sensor Ecosystems via Multimodal Transfer Learning Baños Legrán, Oresti Calatroni, Alberto Damas Hermoso, Miguel Pomares Cintas, Héctor Emilio Roggen, Daniel Rojas Ruiz, Ignacio Villalonga Palliser, Claudia Transfer learning Multimodal sensors Wearable sensors Ambient sensors Activity recognition Human–computer Interaction This work has been partially supported by the Spanish Ministry of Science, Innovation and Universities (MICINN) Projects PGC2018-098813-B-C31 and RTI2018-101674-B-I00 together with the European Fund for Regional Development (FEDER). Recognizing human activities seamlessly and ubiquitously is now closer than ever given the myriad of sensors readily deployed on and around users. However, the training of recognition systems continues to be both time and resource-consuming, as datasets must be collected ad-hoc for each specific sensor setup a person may encounter in their daily life. This work presents an alternate approach based on transfer learning to opportunistically train new unseen or target sensor systems from existing or source sensor systems. The approach uses system identification techniques to learn a mapping function that automatically translates the signals from the source sensor domain to the target sensor domain, and vice versa. This can be done for sensor signals of the same or cross modality. Two transfer models are proposed to translate recognition systems based on either activity templates or activity models, depending on the characteristics of both source and target sensor systems. The proposed transfer methods are evaluated in a human–computer interaction scenario, where the transfer is performed in between wearable sensors placed at different body locations, and in between wearable sensors and an ambient depth camera sensor. Results show that a good transfer is possible with just a few seconds of data, irrespective of the direction of the transfer and for similar and cross sensor modalities 2026-01-28T11:18:30Z 2026-01-28T11:18:30Z 2021-03-31 journal article Baños, O., Calatroni, A., Damas, M. et al. Opportunistic Activity Recognition in IoT Sensor Ecosystems via Multimodal Transfer Learning. Neural Process Lett 53, 3169–3197 (2021). https://doi.org/10.1007/s11063-021-10468-z https://hdl.handle.net/10481/110419 10.1007/s11063-021-10468-z eng http://creativecommons.org/licenses/by-nc-nd/3.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Springer Nature