Athanasios I. Kyritsis, G. Willems, Michel Deriaz, D. Konstantas
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引用次数: 0
摘要
术后康复是骨科手术后重建关节运动和加强关节周围肌肉的重要项目。这种康复是由物理治疗师领导的,他们评估每种情况并规定适当的运动。现代智能设备已经影响了人类生活的方方面面。新开发的技术已经颠覆了各种行业的运作方式,包括医疗保健行业。关于如何将智能手机惯性传感器用于活动识别,已经进行了广泛的研究。然而,很少有研究系统监测患者和检测不同的步态模式,以协助物理治疗师在上述康复阶段的工作,甚至在时间限制的物理治疗疗程之外,因此,这一主题的文献仍处于起步阶段。在本文中,我们开发了一种步态识别系统,用于检测不同的步态模式,包括不同负重水平的拐杖行走,不同框架的行走,跛行和正常行走。瑞士日内瓦的一家骨科诊所Hirslanden Clinique La Colline记录了接受过下体整形手术的患者的数据,并对该系统进行了培训、测试和验证。在9个不同的步态类别中,步态检测准确率达到94.9%,因为这些是由专业物理治疗师标记的。
Gait Recognition with Smart Devices Assisting Postoperative Rehabilitation in a Clinical Setting
Postoperative rehabilitation is a vital program that re-establishes joint motion and strengthens the muscles around the joint after an orthopedic surgery. This kind of rehabilitation is led by physiotherapists who assess each situation and prescribe appropriate exercises. Modern smart devices have affected every aspect of human life. Newly developed technologies have disrupted the way various industries operate, including the healthcare one. Extensive research has been carried out on how smartphone inertial sensors can be used for activity recognition. However, there are very few studies on systems that monitor patients and detect different gait patterns in order to assist the work of physiotherapists during the said rehabilitation phase, even outside the time-limited physiotherapy sessions, and therefore literature on this topic is still in its infancy. In this paper, we are presenting a gait recognition system that was developed to detect different gait patterns including walking with crutches with various levels of weight-bearing, walking with different frames, limping and walking normally. The proposed system was trained, tested and validated with data of people who have undergone lower body orthopedic surgery, recorded by Hirslanden Clinique La Colline, an orthopedic clinic in Geneva, Switzerland. A gait detection accuracy of 94.9% was achieved among nine different gait classes, as these were labeled by professional physiotherapists.