Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat 382481, India.
Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, 248002, India.
Artif Intell Med. 2024 Nov;157:103000. doi: 10.1016/j.artmed.2024.103000. Epub 2024 Oct 22.
The latest evolution of the healthcare industry from Industry 1.0 to 5.0, incorporating smart wearable devices and digital technologies, has revolutionized healthcare delivery and improved patient treatment. Integrating smart wearables such as fitness trackers, smartwatches, and biosensors has endowed healthcare Industry 5.0 with numerous advantages, including remote patient monitoring, personalized healthcare, patient empowerment and engagement, telemedicine, and virtual care. This digital healthcare paradigm embraces promising technologies like Machine Learning (ML) and the Internet of Medical Things (IoMT) to enhance patient care. The key contribution of digital healthcare Industry 5.0 lies in its ability to revolutionize patient care by leveraging smart wearables and digital technologies to provide personalized, proactive, and patient-centric healthcare solutions. Despite the remarkable growth of smart wearables, the exploration of ML-based applications still needs to be expanded. Motivated by this gap, our paper conducts a comprehensive examination and evaluation of advanced ML techniques pertinent to the digital healthcare Industry 5.0 and wearable technology. We propose a detailed taxonomy for digital healthcare Industry 5.0, transforming it into an innovative process model highlighting key research challenges such as wearable modes for data collection, health tracking, security, and privacy issues. The proposed ML-based process comprises data collection from wearables like smartwatches and performs data pre-processing. Several ML models are applied, such as Support Vector Machine (SVM), Decision Tree (DT), and Random Forest(RF), to predict and classify the activity of the person. ML algorithms are capable of analyzing extensive healthcare data encompassing electronic health records (EHR) from sensors to offer valuable insights to improve decision-making processes. A comparative study of the existing work is discussed in detail. Lastly, a case study is presented to render the process model, where the RF-based model shows its efficacy by obtaining the lowest RMSE of 0.94, MSE of 0.88, and MAE of 0.27 for the prediction of activity.
医疗行业从 1.0 到 5.0 的最新演变，融入了智能可穿戴设备和数字技术，彻底改变了医疗服务的提供方式，改善了患者的治疗效果。将健身追踪器、智能手表和生物传感器等智能可穿戴设备整合到医疗行业 5.0 中，带来了许多优势，包括远程患者监测、个性化医疗、患者赋权和参与、远程医疗和虚拟护理。这种数字医疗模式采用了有前途的技术，如机器学习（ML）和医疗物联网（IoMT），以增强患者护理。数字医疗 5.0 的主要贡献在于它能够利用智能可穿戴设备和数字技术为患者提供个性化、主动和以患者为中心的医疗解决方案，从而彻底改变患者护理。尽管智能可穿戴设备取得了显著增长，但基于 ML 的应用探索仍有待扩展。受此差距的启发，我们的论文对与数字医疗 5.0 和可穿戴技术相关的先进 ML 技术进行了全面的检查和评估。我们为数字医疗 5.0 提出了详细的分类法，将其转化为一个创新的过程模型，突出了关键的研究挑战，如用于数据收集、健康跟踪、安全性和隐私问题的可穿戴模式。所提出的基于 ML 的过程包括从智能手表等可穿戴设备收集数据，并执行数据预处理。应用了几种 ML 模型，如支持向量机（SVM）、决策树（DT）和随机森林（RF），以预测和分类人员的活动。ML 算法能够分析来自传感器的广泛医疗保健数据，包括电子健康记录（EHR），以提供有价值的见解，从而改善决策过程。详细讨论了对现有工作的比较研究。最后，提出了一个案例研究来呈现该过程模型，其中基于 RF 的模型通过获得活动预测的最低 RMSE 为 0.94、MSE 为 0.88 和 MAE 为 0.27 来展示其功效。
