TY - JOUR
T1 - Non-Invasive Wearable Optical Sensors for Full Gait Analysis in E-Health Architecture
AU - Domingues, M. Fatima
AU - Tavares, Catia
AU - Nepomuceno, Ana Catarina
AU - Alberto, Nelia
AU - Andre, Paulo
AU - Antunes, Paulo
AU - Chi, Hao Ran
AU - Radwan, Ayman
N1 - Funding Information:
Acknowledgments This work is funded by FCT/MCTES through national funds and, when applicable, co-funded by EU funds under project UID-B/50008/2020-UIDP/50008/2020, and the internal project X-0005-AV-20-NICE-HOME. M. Fátima Domingues and Nélia Alberto acknowledge the REACT and PREDICT (FCT-IT-LA) scientific actions, respectively, under the support of FCT/ MEC through national funds and, when applicable, co-funded by FEDER PT2020 partnership agreement under the project UID/EEA/50008/2019 FCT. Cátia Tavares acknowledges FCT grant PD/ BD/142787/2018. The work of Ayman Radwan was supported by FCT/MEC through Programa Operacional Regional do Centro and by the European Union through the European Social Fund (ESF) under Investigador FCT Grant (5G-AHEAD IF/FCT-IF/01393/2015/CP1310/CT0002). This work was also supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Lisbon (POR LISBOA 2020) and the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework [Project Safe-Home with Nr. 072082 (CENTRO-01-0247-FEDER-072082)];
Funding Information:
This work is funded by FCT/MCTES through national funds and, when applicable, co-funded by EU funds under project UIDB/50008/2020-UIDP/50008/2020, and the internal project X-0005-AV-20- NICE-HOME. M. F?tima Domingues and N?lia Alberto acknowledge the REACT and PREDICT (FCT-IT-LA) scientific actions, respectively, under the support of FCT/MEC through national funds and, when applicable, co-funded by FEDER PT2020 partnership agreement under the project UID/EEA/50008/2019 FCT. C?tia Tavares acknowledges FCT grant PD/BD/142787/2018. The work of Ayman Radwan was supported by FCT/MEC through Programa Operacional Regional do Centro and by the European Union through the European Social Fund (ESF) under Investigador FCT Grant (5G-AHEAD IF/FCT- IF/01393/2015/CP1310/CT0002). This work was also supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Lisbon (POR LISBOA 2020) and the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework [Project Safe-Home with Nr. 072082 (CENTRO-01-0247-FEDER-072082)];
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - With the current sedentary lifestyle we are living, along with an aging population, there is a noticeable rise in the number of impaired and physically debilitated citizens. Such debilitations require new solutions to aid in the rehabilitation process and lift the burden on healthcare systems, which are already overwhelmed in many developed countries. In this article, we target this problem by introducing the design and implementation of a non-invasive wearable architecture for full gait analysis, enabled by optical fiber Bragg grating sensors. The proposed sensing network provides the necessary tools to simultaneously monitor parameters, including foot plantar pressure, peak muscle activity, and the ankle and knee sagittal range of motion, with high precision, without the need for any synchronization or delay adjustments, between multiple sensors. The integration of the sensor network within an edge-cloud networking architecture is also discussed, which enables remote live monitoring of patients during exercise, in addition to secured storing and further analysis of data, with efficient scheduling of different tasks between the edge and the cloud, based on different levels of privacy and required latency.
AB - With the current sedentary lifestyle we are living, along with an aging population, there is a noticeable rise in the number of impaired and physically debilitated citizens. Such debilitations require new solutions to aid in the rehabilitation process and lift the burden on healthcare systems, which are already overwhelmed in many developed countries. In this article, we target this problem by introducing the design and implementation of a non-invasive wearable architecture for full gait analysis, enabled by optical fiber Bragg grating sensors. The proposed sensing network provides the necessary tools to simultaneously monitor parameters, including foot plantar pressure, peak muscle activity, and the ankle and knee sagittal range of motion, with high precision, without the need for any synchronization or delay adjustments, between multiple sensors. The integration of the sensor network within an edge-cloud networking architecture is also discussed, which enables remote live monitoring of patients during exercise, in addition to secured storing and further analysis of data, with efficient scheduling of different tasks between the edge and the cloud, based on different levels of privacy and required latency.
UR - http://www.scopus.com/inward/record.url?scp=85111135578&partnerID=8YFLogxK
U2 - 10.1109/MWC.001.2000405
DO - 10.1109/MWC.001.2000405
M3 - Article
AN - SCOPUS:85111135578
SN - 1536-1284
VL - 28
SP - 28
EP - 35
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
IS - 3
M1 - 9490941
ER -