Quantified-self and remote patient monitoring
Collecting biological data (e.g. heart rate, blood pressure) for diagnosis and for monitoring efficacy of treatments can be time-consuming and expensive in terms of staff resources. With growing health needs globally, there needs to be a more sustainable and participatory approach to the collection of biological data so that medical staff can focus their time on helping patients.
We are developing a quantified-self approach within the context of a ‘carer network’, an online social network that includes patients, healthcare professionals, and informal caregivers for gathering biological data for diagnosis and treatment.
Ye, J, Dobson, SA & Zambonelli, F 2020, ‘XLearn: learning activity labels across heterogeneous datasets‘, ACM Transactions on Intelligent Systems and Technology, vol. 11, no. 2, 17. https://doi.org/10.1145/3368272
Fang, L, Ye, J & Dobson, SA 2019, Distributed self-monitoring sensor networks via Markov switching Dynamic Linear Models. in Proceedings 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019)., 8780572, IEEE Computer Society, pp. 33-42, 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019), Umeå, Sweden, 16/06/19. https://doi.org/10.1109/SASO.2019.00014