# IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
## 1 Introduction  
Hip fracture is a critical life-threatening injury and has a serious, long-term, and devastating impact on a person's physical functional performance. It is a common event among members of the older population (aged 60 and above) and causes substantial problems with a person's ability to live independently, movement restrictions, a reduction in well-being, and other health-related concerns [1; 2; 3]. Rehabilitation is a form of therapy whereby patients perform different types of movements, activities, and physical exercises. It plays a pivotal role in restoring physical functionality, healing the injured hip, and supporting muscle strength. Evidence shows that intensive rehabilitation plays a role in functional performance, quality of life, and achieving optimal rehabilitation outcomes [2; 3; 4; 5; 6]. Several rehabilitation programs are available that aid in the improvement and recovery of physical function and mobility. However, each program's efficacy is ambiguous as rehabilitation mostly occurs while the patient is living independently and unsupervised [2; 5]. Furthermore, to avoid improper exercise, continuous long-term monitoring of movements during activities by a physical therapist and the rectification of improper movements are essential [4]. Therefore, it constitutes a critical global challenge as healthcare professionals lack the historical data on a patient's short-term movement that are needed to help the patient succeed in achieving their personalized recovery goals. To address such challenges, weurgently need to develop a rehabilitation movement monitoring system that can provide a comprehensive rehabilitation care plan program, recognize movement during an activity both in near-real time and the long term, assist healthcare professionals with interacting with important events, assess the improvement in a patient, provide emergency care, and perform a timely follow-up [7]. Advancements in technologies such as the IoT, enabled by wearables and digital health, could be leveraged to transform the existing conventional system into a smart rehabilitation movement monitoring system [1; 8; 9]. Drastic transformation has taken place with the integration of the IoT into healthcare due to the integration of heterogenous types of physical hardware and software and using them to collect data, perform analyses, and facilitate services and various user interactions with the targeted process. Moreover, it can significantly reduce the cost, enrich a user's interaction experience, and boost their quality of life. Findings show that wearables based on IoT technology can bring about endless opportunities, especially for healthcare monitoring applications [10; 11; 12; 13; 14; 15]. The authors of [10; 11; 12] also indicate that wearables based on IoT technology would be realized when an integrated IoT system is available that has all of the needed functionalities distributed at various levels. Despite all the progress in IoT-based healthcare systems and the potential to create an upsurge in different healthcare applications, there is limited focus on offering a wearable solution for the post-operative hip fracture rehabilitation process. This paper is part of a progressive attempt to address the organization and functionalities of an IoT-based rehabilitation model that supports post-operative hip fracture patients in their rehabilitation process. The main contributions of this paper can be summarized as follows:  
1. This paper enhances the post-operative hip fracture recovery model that we published in our conference paper [2];
2. This paper suggests an IoT-based movement monitoring system that supports the model's implementation;
3. This paper analyzes the data collected on the core rehabilitation movement and offers approaches that improve the movement's recognition;
4. This paper attempts to utilize the available computational resources in the Cloud, at the gateway edge, and at the wearable sensor edge to support the system's performance. The organization of this paper is as follows.