@inproceedings{10.1145/3539494.3542755,
author = {Orfanidis, Charalampos and Darwich, Adam S. and Cheong, Rachel and Fafoutis, Xenofon},
title = {Monitoring Neurological Disorders with AI-Enabled Wearable Systems},
year = {2022},
isbn = {9781450394062},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi-org.proxy.findit.cvt.dk/10.1145/3539494.3542755},
doi = {10.1145/3539494.3542755},
abstract = {The age distribution has changed in Europe over the last decade. The group of 45 year-olds and above has increased and the median age in the EU is estimated to increase by 4.5 years during the next 3 decades reaching a median age of approximately 48.2 years according to Eurostat. A similar trend is noticeable in the United States, where the median age increased by 3.3 years from 2000 to 2020 according to Statista. Neurological diseases, such as Huntington disease, have a highly variable onset of 30 - 50 years but they are more prevalent to the older population. One of the first observable physical symptoms is chorea which includes random, uncontrollable and involuntary movements. Internet of Things and wearable systems can assist long-term monitoring of digital biomarkers such as plantar pressure and gait pattern which are associated with the aforementioned neurological disease. Emerging artificial intelligence models can be utilized to monitor the related digital biomarkers and check if these demonstrate a potential pattern denoting the presence or the development of a neurological disease. Enabling long-term monitoring by utilizing a unobtrusive wearable will increase the possibilities of early diagnosis, a longer life expectancy, and an improved quality of life for the patient.},
booktitle = {Proceedings of the 2022 Workshop on Emerging Devices for Digital Biomarkers},
pages = {24–28},
numpages = {5},
keywords = {wearable systems, smart health, embedded mobile learning, digital biomarkers},
location = {Portland, Oregon},
series = {DigiBiom '22}
}