Acceptance and Quality of Service Evaluation of the Internet of Medical Things (IoMT)

“物联网（IoT）是一种将设备与互联网以及彼此连接起来的技术。内置传感器的设备连接到物联网平台，该平台充当交换和连接来自各种设备及其周围环境的数据的集线器。通过分析和人工智能（AI）的应用，该技术擅长解决特定需求，分析有价值的信息，识别趋势，提出建议，甚至预测潜在问题。医疗物联网（IoMT）是物联网的一个子集，包括有效收集生理信号的传感器和电子元件、用于信号分析的处理单元、用于数据传输的网络设备、存储单元以及通过人工智能技术增强的可视化平台。这是在可穿戴设备上完成的，允许实时远程监控，并提供传统测量无法提供的有价值的见解。由于对医疗服务的需求不断增加，COVID-19大流行凸显了物联网在医疗保健领域的关键作用。此外，老龄化人口正在迅速增加。伴随的老年疾病增加了照顾者的压力，对额外医疗护理的要求使老年人更难独处，这增加了入住护理机构的人数，因为老年人发现越来越难以独立生活。在医疗保健中结合人工智能和物联网可以提高运营效率，从而实现以患者为中心的方法。物联网还可以通过提供远程监测、援助和保护，增强老年人，特别是独居老年人的权能，从而促进他们更大程度的独立，提高生活质量。廉价的消费级可穿戴设备已经成为医疗保健研究的一个兴趣点。这些设备最大的问题是它们的可用性、可接受性、互操作性、可伸缩性和安全性。虽然这一领域的研究正在增长，但其中大部分都特别关注这些设备的技术方面。研究表明，与其他物联网应用相比；医疗保健行业对IoMT的采用率较慢。虽然现代系统是用户友好的，但老年人可能难以适应复杂的技术，需要更多的时间来适应。设计考虑行为分析的基于物联网的医疗保健系统可以帮助解决这一挑战，但衡量用户的接受程度仍然很困难。此外，有关健康监测的可用性和可接受性的问题，特别是在为生物信号监测而设计的iomt中，数据丢失会严重影响系统质量和服务质量（QoS），尚未得到广泛探讨。本研究的主要目标是提高老年人对可穿戴设备的接受度和可用性，以实时监测活动或提供帮助。最初，将分析现有的兼容可穿戴设备，以评估增强功能以提高可用性和接受度的可能性。护理机构或其他机构的参与者将被邀请参加初步研究。可穿戴设备的使用频率和个人偏好将被记录下来供分析。第一阶段的分析结果将应用于第二阶段的设计，以提高可穿戴设备的可用性和接受度。还将开发人工智能/机器学习模型，用于实时分析和检测所获取数据中的任何异常情况。这一领域的研究非常重要，因为它可以为设计和实施适合需要先进辅助技术的老年人需求的IoMT解决方案提供信息。这些进步有可能改善老年人的医疗保健和生活质量，并有助于确定在老年人护理中应用IoMT解决方案的潜在障碍和机会。

"The Internet of Things (IoT) is a technology that interconnects devices both with the internet and each other. Devices with built-in sensors are linked to an IoT platform, which acts as a hub for exchanging and connecting data from various devices and their surroundings. Through the application of analytics and artificial intelligence (AI), this technology is adept at addressing specific needs, analysing valuable information, identifying trends, making recommendations, and even predicting potential issues.Internet of Medical Things (IoMT) is a subset of IoMT and comprises sensors and electronic components that efficiently collect physiological signals, processing units for signal analysis, network devices for data transmission, storage units, and visualization platforms enhanced with AI techniques. This is done on wearable devices allowing for real-time remote monitoring and assistance with valuable insights that traditional measures cannot offer. The COVID-19 pandemic underscored the pivotal role of IoMT in healthcare due to increased demand for medical services. Furthermore, the ageing population is increasing quickly. The accompanying diseases of ageing increase the strain on carers and the requirement for additional medical care makes it more difficult for older individuals to be left alone, which increases the number of admissions to care facilities as the elderly are finding it increasingly difficult to live independently.Combining AI and IoMT in healthcare can enhance operational efficiency leading to a patient-centric approach. IoMT can also empower elderly individuals, especially those living alone, by offering remote monitoring, assistance, and protection to promote greater independence and an improved quality of life.Inexpensive consumer-grade wearable devices have become a point of interest in healthcare research. The biggest concerns of these devices are their usability, acceptance, interoperability, scalability, and security. While research in this domain is growing, much of it specifically focuses on advancing the technological aspects of these devices. Studies have revealed that compared to other IoT applications; the healthcare sector has exhibited a slower adoption rate of IoMT.While modern systems are user-friendly, older individuals may struggle with complex technologies and require more time to adapt. Designing IoT-based healthcare systems with consideration for behavioural analysis can help address this challenge, but gauging user acceptance remains difficult.Additionally, issues concerning the usability and acceptability of health monitoring, especially in IoMTs designed for biosignal monitoring, where data losses can significantly affect system quality and the quality of service (QoS), have not been explored extensively.The primary objective of this research centres on improving the acceptance and usability of the wearable devices in the elderly population to monitor activity or provide assistance in real-time. Initially, the existing compatible wearable devices will be analysed to evaluate the possibilities of enhancing the functionality to improve usability and acceptance. Participants in care facilities or other facilities will be invited for the initial study. Wearable usage frequency and individual preferences will be recorded for analysis. The analysis findings from phase-I will be applied in phase-II design to improve the usability and acceptance of the wearable. AI/ML models would also be developed for real-time analysis and to detect any anomalies in the acquired data.Research in this field holds immense importance, as it can inform the design and implementation of IoMT solutions tailored to the needs of the elderly who require advanced assistive technologies. Such advancements have the potential to improve healthcare and the quality of life for elderly individuals and aid in identifying potential obstacles and opportunity for the application of IoMT solutions in elderly care.