Serious game versus standard care for rehabilitation after distal radius fractures: a protocol for a multicentre randomised controlled trial

Novel Tools to Approach and Measure Outcomes in Patients with Fractures

Upper extremity fractures are prevalent and pose a great burden to patients and society. In the US alone, the annual incidence of upper extremity fractures is 67.6 fractures per 10,000 persons. While the majority of patients with upper extremity fractures demonstrate satisfactory outcomes when treated appropriately (the details of which are discussed in prior articles), the importance of follow-up and outcome measurement cannot be understated. Outcome measurement allows for accountability and improvement in clinical outcomes and research. The purpose of this article is to describe recent advances in methods and tools for assessing clinical and research outcomes in hand and upper extremity care. Three specific advances that are broadly changing the landscape of follow-up care of our patients include: 1) telemedicine, 2) patient-reported outcome measurement, and 3) wearables/remote patient monitoring.

Wearable rehabilitation wristband for distal radius fractures

Background

Distal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients' rehabilitation.

Methods

A wearable rehabilitation training wristband was proposed, which could be used in the patient's daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result.

Results

Experimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions.

Conclusion

The developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings.