Abstract
Hip fracture incidence is life-threatening and has an impact on the person’s physical functionality and their ability to live independently. Proper rehabilitation with a set program 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. The Internet of Things (IoT), with advancements in digital health, could be leveraged to enhance the backup intelligence used in the rehabilitation process and provide transparent coordination and information about movement during activities among relevant parties. This paper presents a post-operative hip fracture rehabilitation model that clarifies the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. To support this model, the paper proposes an IoT-enabled movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real time and throughout the rehabilitation process. The approach was tested incrementally on ten healthy subjects, particularly for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the significance of personalization and the significance of a one-minute history of data in monitoring the real-time behavior. This paper 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.
Highlights
This paper enhances the post-operative hip fracture recovery model that we published in our conference paper [2]; This paper suggests an Internet of Things (IoT)-based movement monitoring system that supports the model’s implementation; This paper analyzes the data collected on the core rehabilitation movement and offers approaches that improve the movement’s recognition; 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 following section illustrates the post-operative hip fracture rehabilitation movement process that could support the development of an online movement monitoring system
In the first scenario, which is related to the wearable sensor edge, the FFT-based signal processing is embedded within the wireless sensor where only one frame of 16 bytes of data packet size is sent to the gateway once at the four different time intervals chosen
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Future Internet 2021, 13, 195 urgently 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]. This paper enhances the post-operative hip fracture recovery model that we published in our conference paper [2]; This paper suggests an IoT-based movement monitoring system that supports the model’s implementation; This paper analyzes the data collected on the core rehabilitation movement and offers approaches that improve the movement’s recognition; 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.
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