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.

中文翻译：

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探索模糊规则推断的 ConvLSTM 以发现和调整智能医疗床患者的最佳姿势

当前，一些国家正面临着人口老龄化带来的严峻社会挑战。这一公共卫生问题继续给临床医疗保健带来压力，例如需要预防晚期患者的压疮。解决此问题的积极方法包括健康信息技术 (HIT)。对此，本文探讨了一种基于智能医疗床（SMB）的技术解决方案。通过集成卷积神经网络 (CNN) 和长短期记忆 (LSTM) 模型，我们发现与之前的解决方案相比，该模型提高了性能。此外，我们提供了一种模糊推断解决方案，可以控制我们提出的专有自动化 SMB 布局，以优化患者的姿势并减轻压疮。因此，我们提议的 SMB 可以提供自主护理，有助于防止长时间躺下时出现医疗并发症。我们提议的 SMB 还有助于减轻主要护理人员的负担，以应对由于人口老龄化等公共卫生问题导致的人员短缺。