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Developing an Accelerometer-based Mobile Application to Aid Knee Extension Exercise

机译:开发基于加速度计的移动应用程序来辅助膝盖伸展运动

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摘要

Knee pain is a medical condition that disturbs many individuals throughout the world. Physicians prescribe knee exercises to restore mobility and strength to painful and weakened knees. However, some sufferers may find these exercises dull, which might affect their motivation. Currently, smartphones are ubiquities and they come equipped with sensors such as accelerometers. In this research, smartphone accelerometer data are used to compute knee angles and Google's TensorFlow machine learning software library for machine intelligence is applied to derive a linear regression equation. The resulting algorithm is the basis of a mobile application, which was developed to count the number of times patients' legs are raised and lowered correctly, calculate the length of time the legs are held, count the number of times the legs are held correctly and incorrectly, compute the percentage of correct leg holds, warn patients when leg movement is outside acceptable thresholds and evaluate the overall effectiveness of the exercise by giving it a score. The mobile application allows patients to recover more rapidly because knee extension exercises can be monitored and adjusted through feedback to improve their efficiency.
机译:膝盖疼痛是一种困扰全世界许多人的医学疾病。医师开具膝盖运动处方,以恢复膝盖疼痛和虚弱的活动能力和力量。但是,某些患者可能会觉得这些锻炼很乏味,这可能会影响他们的动力。当前,智能手机无处不在,并且配备了诸如加速度计之类的传感器。在这项研究中,智能手机加速度计数据用于计算膝盖角度,而Google的用于机器智能的TensorFlow机器学习软件库则用于导出线性回归方程。由此产生的算法是移动应用程序的基础,该应用程序被开发用于计算患者的双腿正确举起和降低的次数,计算双腿被握持的时间长度,计算双腿被正确握持的次数以及错误地计算出正确的腿部握持百分比,在腿部运动超出可接受的阈值时警告患者,并通过评分给运动进行总体评估。该移动应用程序使患者可以更快地恢复,因为可以通过反馈来监视和调整膝盖伸展运动以提高其效率。

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