# IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
## 2 Related Work  
Over the last decade, many scholars have conducted extensive research in the area of post-operative rehabilitation of hip fracture patients [4]. Different exercises and activity movements to be performed during rehabilitation have been proposed [16; 17]. On top of surgical therapy, post-operative rehabilitation exercises for patients with a hip fracture have gained further attention. Findings indicate that the majority of patients are not able to return to their pre-functional level a year after the surgery. Furthermore, even after a 2-year follow-up, patients are likely to spend their time on their feet or doing prescribed exercises and lack independence in performing the basic activities of daily living (ADLs) [18]. It is believed that there is a high chance of neglecting the exercise program following the discharge which could lead to the interruption of rehabilitation instruction [4]. As a result, a patient's post-operative function can be promoted if a continuity in the rehabilitation tion guidance from the hospital to the home is maintained. Recent studies have shown the effect of home-based exercise programs in strengthening muscle, healing fractures, as well as improving the quality of life and functional performance [4; 19]. However, evidence and a standardized-based approach for the treatment in the patient rehabilitation process are lacking. Therefore, a more task-based structural rehabilitation program [4] in addition to a continuous active monitoring system can provide pervasive and personalized healthcare treatment which this paper aims to address. This could cater to the needs of both healthcare professionals and patients in accelerating the return of physical functionality [8]. The recognition of human limb movements plays an important role in distinguishing information about the human psychological state and daily physical changes. Many scholars have contributed to activity recognition across a wide range of applications such as posture recognition [20], fall detection [21; 22], human tracking [23], and gesture-based movements [24]. Many different activity recognition methods for data analysis such as digital signal processing [25], time and frequency domain features extraction methods [26], as well as statistical, inclination angle and threshold-based methods [27; 28; 29] have been used in the classification of static, gait-related activities, rehabilitation movement activities. All of these proposed methods are somewhat associated with the hip fracture patient monitoring system. However, all the techniques used by different researchers have based their recognition algorithm on the axis-dependent technique. This might not always be feasible as it restricts the user to wearing the sensor in a specific orientation. This paper addresses this gap by using a technique similar to that proposed by the authors in [3]. Moreover, it highlights the significance of personalization over the subject's overall movement behavior when recognizing a particular activity. With the development of fitness trackers, smartwatches and Web-enabled glasses, wireless body-worn sensors have gained significant popularity in healthcare monitoring applications and medical use cases [10]. They play a central role in acquiring the patient's activity movement data, which are responsible for the recognition of the activity movements and controlling the overall movement monitoring process. It has been realized that IoT-enabled wearables [10] are becoming quite attractive in healthcare monitoring applications, as these make the healthcare system transparent and cost-effective as well as allow personalization, improved outcomes, provide high-quality care, reduce diagnostic time, and enable the effective utilization of the collected data which are accessible from anywhere and at any time. A strong synergy exists between the unprecedented advancements made in the Internet of Things (IoT) and the emerging demands of healthcare applications.