# IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
## 2 Related Work
IoT could support the healthcare system, allowing people to reside and be supervised at home instead of being sent to clinics or hospitals. A recent article surveyed the significance of healthcare IoT from clinical perspectives by discussing its current trends, application demands, and challenges [30]. Moreover, many novel healthcare monitoring systems using machine learning techniques have been proposed and researched for advancing all healthcare applications [31]. The authors in [32] proposed a novel healthcare monitoring system framework based on ontologies and bidirectional long-term short memory (Bi-LSTM), which can precisely analyze and store healthcare data and improve recognition accuracy. This novel approach is being applied to healthcare data related to BP, diabetes, and mental health. The model has been proven to be quite effective in enhancing the performance of heterogenous data handling and improving classification accuracy using various sources of patient data. A smart healthcare monitoring system for the prediction of heart disease using ensemble and deep learning and sensor fusion techniques has been proposed and implemented by the authors in [33]. The experimental results show 98.5% precision in terms of recognizing the disease, which is higher than that of existing state-of-the-art systems. Another recent article [34] used the machine learning technique to predict circulatory failure in an intensive care unit. The proposed approach predicts 90% of circulatory failure events in test sets, among which 82% were recognized 2 h in advance. This shows that the implementation of machine learning techniques could make the system more seamless and precise in classification in handling the large amount of unstructured healthcare data. In addition, many scholars have proposed and implemented IoT-based architectural system solution in applications including stroke and knee rehabilitation [35], bed egress [36], fall detection [22] as well as sleep [37], respiratory [38], cardiac [39], and glucose monitoring [40]. However, there has been less of a focus on certain other applications such as hip fracture rehabilitation [6]. The details of a typical solution to the follow-up of a process such as post-operative hip fracture patient rehabilitation will be explored and addressed in this paper. Different multi-layer IoT-based architectures that involve wireless sensing, data processing, communication, edge computing and Cloud computation have been proposed [1,8]. Wearable IoT-based three-layered architectures for personalized [41], home-based healthcare services [42] have also been proposed. This section discusses the functionalities of various architecture layers and their benefit for clinical healthcare monitoring applications. The SPHERE project offered an architecture for the identification and administration of healthcare conditions and aimed to integrate different sensing modalities into an IoT solution for AAL [43]. Home health hub IoT (H3 IoT) designed a simple layered architecture for monitoring of the health of aging occupants and could be extended and modified to suit clinical and emergency-based healthcare monitoring systems [44]. In addition to the proposed design, the focus is now shifting from centralized to decentralized IoT architectural approaches. In the centralized approach, IoT devices directly forward data to the Cloud before any decision making takes place. This means that all the computational resources are placed within the Cloud. As a result, this could lead to challenges in handling the overhead on the used devices, as well as the latency and increased size of data traffic. In contrast, in the decentralized approach, the resources are utilized across all layers, i.e., from IoT wearable devices to the gateway/edge and Cloud layers [8]. In doing so, the computational and decision-making capabilities are distributed, reducing the data packet transmission size and communication delay time.