Exploring a Fuzzy Rule Inferred ConvLSTM for Discovering and Adjusting the Optimal Posture of Patients with a Smart Medical Bed

Francis Joseph Costello, Min Gyeong Kim, Cheong Kim and Kun Chang Lee
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Francis Joseph Costello: SKK Business School, Sungkyunkwan University, Seoul 03063, Korea
Min Gyeong Kim: SKK Business School, Sungkyunkwan University, Seoul 03063, Korea
Cheong Kim: SKK Business School, Sungkyunkwan University, Seoul 03063, Korea
Kun Chang Lee: SKK Business School, Sungkyunkwan University, Seoul 03063, Korea

IJERPH, 2021, vol. 18, issue 12, 1-14

Abstract: Several countries nowadays are facing a tough social challenge caused by the aging population. This public health issue continues to impose strain on clinical healthcare, such as the need to prevent terminal patients’ pressure ulcers. Provocative approaches to resolve this issue include health information technology (HIT). In this regard, this paper explores one technological solution based on a smart medical bed (SMB). By integrating a convolutional neural network (CNN) and long-short term memory (LSTM) model, we found this model enhanced performance compared to prior solutions. Further, we provide a fuzzy inferred solution that can control our proposed proprietary automated SMB layout to optimize patients’ posture and mitigate pressure ulcers. Therefore, our proposed SMB can allow autonomous care to be given, helping prevent medical complications when lying down for a long time. Our proposed SMB also helps reduce the burden on primary caregivers in fighting against staff shortages due to public health issues such as the increasing aging population.

Keywords: smart medical bed; health information technology; ConvLSTM; fuzzy inference; clinical healthcare; public health (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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