TY - GEN
T1 - AI Techniques to Automatically Detect Standardized Functional Test Patterns from Wearable Sensor Data
AU - Vijayan, Vini
AU - Connolly, James
AU - Condell, Joan
AU - Gardiner, Philip
AU - McKelvey, Nigel
AU - O'Shea, Finbar Barry
AU - O'Dwyer, Tom
AU - O'Grady, Megan
AU - Wilson, Fiona
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Wearable devices are often utilized for the diagnosis, treatment, and rehabilitation of various diseases. Data acquired from them may be used to monitor patient recovery during rehabilitation or recovery. Wearable devices allow for the collection of data from various standardized functional assessments that quantify activities of daily living (ADL). The volume of data collected by the sensors from detailed recordings of long-term movement data can become enormous, making it challenging to manually extract movement information with any degree of certainty. The goal of this research was to create an automated system capable of extracting standardized functional test (SFT) patterns from wearable devices worn in an ambulatory environment. The datasets used in this study contained movement data from 40 people living with axial spondylo arthritis. Each participant in this study completed a series of SFTs. The first session was completed in a clinical setting, and the second session at each participant's home. An artificial intelligence (AI) system was developed to automatically extract SFT movement patterns from the long-term datasets. The resultant model demonstrated an accuracy of 97.37%.
AB - Wearable devices are often utilized for the diagnosis, treatment, and rehabilitation of various diseases. Data acquired from them may be used to monitor patient recovery during rehabilitation or recovery. Wearable devices allow for the collection of data from various standardized functional assessments that quantify activities of daily living (ADL). The volume of data collected by the sensors from detailed recordings of long-term movement data can become enormous, making it challenging to manually extract movement information with any degree of certainty. The goal of this research was to create an automated system capable of extracting standardized functional test (SFT) patterns from wearable devices worn in an ambulatory environment. The datasets used in this study contained movement data from 40 people living with axial spondylo arthritis. Each participant in this study completed a series of SFTs. The first session was completed in a clinical setting, and the second session at each participant's home. An artificial intelligence (AI) system was developed to automatically extract SFT movement patterns from the long-term datasets. The resultant model demonstrated an accuracy of 97.37%.
KW - Activities of Daily Living (ADL)
KW - Artificial Intelligence (AI)
KW - Axial Spondylo Arthritis (axSpA)
KW - Neural Network (NN)
KW - Standardised Functional Test (SFT)
KW - Wearable technology
UR - http://www.scopus.com/inward/record.url?scp=85135950166&partnerID=8YFLogxK
U2 - 10.1109/ISSC55427.2022.9826154
DO - 10.1109/ISSC55427.2022.9826154
M3 - Conference contribution
AN - SCOPUS:85135950166
T3 - 2022 33rd Irish Signals and Systems Conference, ISSC 2022
BT - 2022 33rd Irish Signals and Systems Conference, ISSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Irish Signals and Systems Conference, ISSC 2022
Y2 - 9 June 2022 through 10 June 2022
ER -