A research protocol on leap motion tracking device: A novel intervention method in distal radial fracture rehabilitation

Semi-Immersive Virtual Reality Exercise Therapy for Upper Limb Rehabilitation in Patients With Spinal Cord Injury Using the Leap Motion Controller

In this article, we present a case study involving a patient with spinal cord injury (SCI), resulting in tetraplegia and subsequent loss of upper limb function. The subject of our study was a 23-year-old woman with incomplete tetraplegia stemming from a cervical spinal cord injury. Our primary objective was to enhance hand function and grip strength. Throughout the intervention, we observed substantial enhancements in hand function, range of motion, and muscle power. Notably, the patient exhibited a favorable response to the therapy, demonstrating commendable adherence and active participation. To create an optimal training environment tailored to the patient's needs, we employed the Unity 3D game engine in conjunction with a Leap Motion controller sensor. This combination facilitated the development of a semi-immersive virtual training environment. The utilization of this technology aimed to simulate a conducive training atmosphere for the rehabilitation of hand function. Based on our study outcomes, we advocate for the incorporation of leap motion-related exercises in the treatment of hand functional loss and weakness. The promising results observed in this case study prompt the recommendation for further large-scale studies to validate and substantiate our findings. Such investigations would contribute to the establishment of evidence-based practices and enhance the understanding of the efficacy of Leap Motion technology in addressing upper limb impairments associated with spinal cord injuries.

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.