MusCare+: Muscle Monitoring for Anomalies

Nicholas Foley, Chen-Hsiang Yu
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引用次数: 1

Abstract

- Muscles are an essential part of everyday life and any damage or illness that affects them can cause massive problems. Patients who are diagnosed with muscle injuries and illnesses largely remain unmonitored, even though a few appointments they have with doctors annually. Moving from unmonitored to constant monitoring can not only paint a better picture of how a muscle condition is progressing, but it also can inform medical professionals if their treatment regimen is actually working. In this paper, we propose a new system that can monitor muscle health of a patient and predict the muscle conditions. This system mainly focuses on the shoulder but could be expanded to other areas of the body. By utilizing the strength of machine learning and the Android platform, we created a platform that can monitor muscle health quickly and easily. The current prototype system is not only able to display live data gathered from an EMG sensor, but it can also predict whether the muscle is currently flexed or relaxed. Although there is a limitation in current prototype system, a more robust machine learning algorithm could be trained to give a wide array of muscle health predictions.
MusCare+:肌肉异常监测
肌肉是日常生活中必不可少的一部分,任何影响肌肉的损伤或疾病都会导致大量问题。被诊断患有肌肉损伤和疾病的患者,尽管每年有几次与医生的预约,但在很大程度上仍未受到监控。从不监测到持续监测不仅可以更好地描绘肌肉状况的进展情况,而且还可以告知医疗专业人员他们的治疗方案是否有效。在本文中,我们提出了一个新的系统,可以监测病人的肌肉健康和预测肌肉状况。这个系统主要集中在肩膀上,但可以扩展到身体的其他部位。通过利用机器学习的优势和Android平台,我们创造了一个可以快速,轻松地监测肌肉健康的平台。目前的原型系统不仅能够显示从肌电图传感器收集的实时数据,而且还可以预测肌肉当前是弯曲还是放松。尽管目前的原型系统存在局限性,但可以训练更强大的机器学习算法来提供广泛的肌肉健康预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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