TY - JOUR
T1 - AIoT-Based Ergometer for Physical Training in Frail Elderly with Cognitive Decline
T2 - A Pilot Randomized Control Trial
AU - Lin, Chih Chun
AU - Kuo, Li Chieh
AU - Lin, Yu Sheng
AU - Chang, Chia Ming
AU - Hu, Fang Wen
AU - Chen, Yi Jing
AU - Lin, Chun Tse
AU - Su, Fong Chin
N1 - Funding Information:
We are grateful to Professor Chung-Yi Lee and Wan-Ni Chen for providing statistical consulting services from the Biostatistics Consulting Center, National Cheng Kung University Hospital.
Funding Information:
This study was supported by the Ministry of Science and Technology of Taiwan (Grant Number: MOST 110-2627-M-006-002-) and by the Medical Device Innovation Center (MDIC), National Cheng Kung University (NCKU) from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MoE) in Taiwan.
Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Purpose: Reduced physical activity is reported in the elderly, especially in institutional residents. Institutionalized older adults exhibit a high prevalence of frailty. In this work, we developed an artificial intelligence of things (AIoT)-based feedback assistive strengthening ergometer (AIFASE), for the physical strengthening of the elderly with intelligent assistance. Methods: We conducted a 12-week intervention in a long-term care facility. In total, 16 participants (84.38 ± 6.0 years; 4 males and 12 females) were recruited with 1:1 randomization of exercise to control groups. The muscle strength of the lower extremities, timed up and go test (TUG), and Short-form Physical Performance Battery (SPPB) of the participants were measured. The AIFASE system allows the clinical staff to record the personal physical performance of the elderly and generates personalized exercise prescriptions accordingly. AIFASE also displays the current usage status of all ergometers and the users’ physiological conditions. The algorithms were developed to generate warning alerts when the training workload was too large by personal physiological detection. AIFASE automatically customized the exercise prescription according to the user’s exercise performance. Results: After a 12-week AIFASE intervention, the intervention group exhibited significant improvements in the strength of the hip flexor, Semi-Tandem Stand, and Tandem Stand. Conclusion: In this study, we developed an AIoT ergometer that delivered customized physical training prescriptions to improve the physical performance of long-term care facility residents. We believe that the application of AIFASE will help improve the quality of institutional care.
AB - Purpose: Reduced physical activity is reported in the elderly, especially in institutional residents. Institutionalized older adults exhibit a high prevalence of frailty. In this work, we developed an artificial intelligence of things (AIoT)-based feedback assistive strengthening ergometer (AIFASE), for the physical strengthening of the elderly with intelligent assistance. Methods: We conducted a 12-week intervention in a long-term care facility. In total, 16 participants (84.38 ± 6.0 years; 4 males and 12 females) were recruited with 1:1 randomization of exercise to control groups. The muscle strength of the lower extremities, timed up and go test (TUG), and Short-form Physical Performance Battery (SPPB) of the participants were measured. The AIFASE system allows the clinical staff to record the personal physical performance of the elderly and generates personalized exercise prescriptions accordingly. AIFASE also displays the current usage status of all ergometers and the users’ physiological conditions. The algorithms were developed to generate warning alerts when the training workload was too large by personal physiological detection. AIFASE automatically customized the exercise prescription according to the user’s exercise performance. Results: After a 12-week AIFASE intervention, the intervention group exhibited significant improvements in the strength of the hip flexor, Semi-Tandem Stand, and Tandem Stand. Conclusion: In this study, we developed an AIoT ergometer that delivered customized physical training prescriptions to improve the physical performance of long-term care facility residents. We believe that the application of AIFASE will help improve the quality of institutional care.
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U2 - 10.1007/s40846-022-00759-8
DO - 10.1007/s40846-022-00759-8
M3 - Article
AN - SCOPUS:85141861323
SN - 1609-0985
VL - 42
SP - 909
EP - 921
JO - Journal of Medical and Biological Engineering
JF - Journal of Medical and Biological Engineering
IS - 6
ER -