TY - GEN
T1 - MultiGRehab
T2 - 2022 IEEE International Conference on Digital Health, ICDH 2022
AU - Dias, Sofia
AU - Hadjileontiadis, Leontios J.
AU - Jelinek, Herbert F.
N1 - Funding Information:
This work has been supported by Khalifa University-FSU funding (ref. FSU-847400341).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Rehabilitation programs for post stroke recovery or following a heart attack are always stressful for patients, who have been spending time in hospital, an unaccustomed environment, experiencing surgery burden, irregular sleep, and undergoing general rehabilitation exercise programs. In the latter, the exercise intensity and difficulty are often more than what a patient can manage, and usually subjective decisions on the level of exercise intensity and difficulty are followed. To address this issue in a more personalized way, the development of a new rehabilitation framework, namely MultiGRehab (multi-sensed biosignals combined with serious games), is proposed here. In fact, MultiGRehab captures multimodal biosignals in a real-time fashion during a patient's rehabilitation session that includes serious gaming. Through biosignals swarm decomposition and deep learning, the emotional state of the patient is estimated and used as a controlling factor for the serious game adaptation, in terms of exercise type, duration and intensity level. In this way, MultiGRehab is expected to increase a patient's motivation, adherence to the exercise protocol and personalization of rehabilitation targets and outcomes.
AB - Rehabilitation programs for post stroke recovery or following a heart attack are always stressful for patients, who have been spending time in hospital, an unaccustomed environment, experiencing surgery burden, irregular sleep, and undergoing general rehabilitation exercise programs. In the latter, the exercise intensity and difficulty are often more than what a patient can manage, and usually subjective decisions on the level of exercise intensity and difficulty are followed. To address this issue in a more personalized way, the development of a new rehabilitation framework, namely MultiGRehab (multi-sensed biosignals combined with serious games), is proposed here. In fact, MultiGRehab captures multimodal biosignals in a real-time fashion during a patient's rehabilitation session that includes serious gaming. Through biosignals swarm decomposition and deep learning, the emotional state of the patient is estimated and used as a controlling factor for the serious game adaptation, in terms of exercise type, duration and intensity level. In this way, MultiGRehab is expected to increase a patient's motivation, adherence to the exercise protocol and personalization of rehabilitation targets and outcomes.
KW - cardiovascular health
KW - deep learning
KW - MultiGRehab
KW - multimodal biosignals acquisition headset
KW - patient's emotional state
KW - post-stroke rehabilitation
KW - swarm decomposition
UR - http://www.scopus.com/inward/record.url?scp=85138075915&partnerID=8YFLogxK
U2 - 10.1109/ICDH55609.2022.00035
DO - 10.1109/ICDH55609.2022.00035
M3 - Conference contribution
AN - SCOPUS:85138075915
T3 - Proceedings - 2022 IEEE International Conference on Digital Health, ICDH 2022
SP - 175
EP - 177
BT - Proceedings - 2022 IEEE International Conference on Digital Health, ICDH 2022
A2 - Ahamed, Sheikh Iqbal
A2 - Ardagna, Claudio Augistino
A2 - Bian, Hongyi
A2 - Bochicchio, Mario
A2 - Chang, Carl K.
A2 - Chang, Rong N.
A2 - Damiani, Ernesto
A2 - Liu, Lin
A2 - Pavel, Misha
A2 - Priami, Corrado
A2 - Shahriar, Hossain
A2 - Ward, Robert
A2 - Xhafa, Fatos
A2 - Zhang, Jia
A2 - Zulkernine, Farhana
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 July 2022 through 16 July 2022
ER -