Kao Hsuan-Kai, Wu Yi-Chao, Lu Chi-Heng, Hou Xiu-Ling, Lee Tsair-Fwu, Tuan Chiu-Ching
Department of Orthopedic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan.
Bone and Joint Research Center, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan.
Life (Basel). 2024 Nov 22;14(12):1534. doi: 10.3390/life14121534.
After a fracture, patients have reduced willingness to bend and extend their elbow joint due to pain, resulting in muscle atrophy, contracture, and stiffness around the elbow. Moreover, this may lead to progressive atrophy of the muscles around the elbow, resulting in permanent functional loss. Currently, a goniometer is used to measure the range of motion, ROM, to evaluate the recovery of the affected limb. However, the measurement process can cause measurement errors ranging from 4 to 5 degrees due to unskilled operation or inaccurate placement, leading to inaccurate judgments of the recovery of the affected limb. In addition, the current measurement methods do not include an assessment of muscle endurance. In this paper, the proposed device combines image recognition and a myoelectric signal sensor to measure the joint movement angle and muscle endurance. The movement angle of the elbow joint is measured using image recognition. Muscle endurance is measured using the myoelectric signal sensor. The measured data are transmitted to a cloud database via an app we have proposed to help medical staff track a patient's recovery status. The average error value of static image recognition and verification is up to 0.84 degrees. The average error value of dynamic image recognition and verification is less than 1%. The average error of total harmonic distortion (THD) verified by the myoelectric signal sensor is less than ±3%. It was proven that our system could be applied to measuring elbow joint range of motion. Since this is pilot research, most of the measurement subjects are healthy people without dysfunction in arm movement, and it is difficult to observe differences in the measurement results. In the future, experiments will be conducted on patients with elbow fractures through the IRB. This is expected to achieve the effect of encouraging patients to be actively rehabilitated at home through their measurement data and images of their actions being displayed in real time using our cheap and compact device and app.
骨折后，患者因疼痛而减少弯曲和伸展肘关节的意愿，导致肌肉萎缩、挛缩以及肘部周围僵硬。此外，这可能会导致肘部周围肌肉逐渐萎缩，造成永久性功能丧失。目前，使用角度计测量关节活动范围（ROM），以评估患肢的恢复情况。然而，由于操作不熟练或放置不准确，测量过程可能会导致4至5度的测量误差，从而对患肢的恢复情况做出不准确的判断。此外，当前的测量方法未包括对肌肉耐力的评估。在本文中，所提出的设备结合了图像识别和肌电信号传感器来测量关节运动角度和肌肉耐力。使用图像识别测量肘关节的运动角度。使用肌电信号传感器测量肌肉耐力。测量数据通过我们提出的一款应用程序传输到云数据库，以帮助医护人员跟踪患者的恢复状态。静态图像识别与验证的平均误差值高达0.84度。动态图像识别与验证的平均误差值小于1%。经肌电信号传感器验证的总谐波失真（THD）的平均误差小于±3%。事实证明，我们的系统可应用于测量肘关节活动范围。由于这是初步研究，大多数测量对象是手臂运动无功能障碍的健康人，难以观察到测量结果的差异。未来，将通过机构审查委员会对肘部骨折患者进行实验。预计通过使用我们廉价且紧凑的设备及应用程序实时显示患者的测量数据和动作图像，能够达到鼓励患者在家中积极康复的效果。
