Hip fracture incidence is a life-threatening event that increases with age and is common among the older population. It causes significant problems as there is an increased risk of mortality, restriction of movement and well-being, loss of independence, and other adverse health-related concerns for the injured. Following surgery, physiotherapy is essential for strengthening muscles, mobilizing joints, and fostering the return to regular physical activity. Ideally, appropriate rehabilitation with a set programme performed under a predefined supervised and unsupervised environment can play a significant role in recovering the person’s physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. Tracking, recording, and continuous real-time monitoring of activity movements can significantly help in following up the correct implementation of a predefined programme. The ever-increasing technology such as the Internet of Things (IoT), which produces advancements in digital health revolutionizing industries and markets could be useful in advancing conventional rehabilitation care. This will also aid in enhancing backup intelligence used in the rehabilitation process, and will provide transparent coordination and information about activity movements among relevant parties. This thesis provides a motivational background for the problem and a critical literary analysis of the key components involved in structuring an IoT-based rehabilitation care monitoring system. The thesis proposes and presents a post-operative hip fracture rehabilitation model from the existing rules and health programmes. The model reflects the key stages a patient undergoes straight after hospitalisation, and provides clarification for the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. Considering the model monitoring requirements, the thesis highlights the system modelling and development tools for testing the proof-of-concept and overall conceptual ideology. To support this model, the thesis proposes an IoT-enabled wearable movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real-time and throughout the rehabilitation process. The conceptual ideology was tested incrementally on ten young and healthy subjects, for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the recognition of the hip fracture rehabilitation activity movements based on frequency-domain analysis and concerning sensor localisation. Research findings suggested that the amplitude parameter was suitable for the classification of the static state of a patient and ambulatory activities. Whereas, for the hip fracture related movements, both the frequency content and related amplitude of the acceleration signal play a significant role. From the analysis, the ankle is considered to be an appropriate sensor location that can categorise the majority of the activity movements thought to be important during the rehabilitation programme and data collection time of four seconds is considered to be the minimum time for recognising a particular activity movement without any loss of information or signal distortion. Furthermore, the thesis presents the importance of personalisation and one-minute history of data in improving recognition accuracy and monitoring real-time behaviour. This thesis also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements. Finally, this thesis offers a clearly-defined structured rehabilitation follow-up programme use case and conclusion with an indication of our future research work.