Jeong Hyeon Ki, Whittingslow Daniel, Inan Omer T
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30308, USA.
School of Medicine, Emory University, Atlanta, GA, 30318, USA.
IEEE Sens Lett. 2018 Dec;2(4). doi: 10.1109/LSENS.2018.2871981. Epub 2018 Sep 24.
This paper explores the novel application of an automated -value extraction algorithm for the interpretation of sounds produced by the knee joint during movement. Acoustical emissions were recorded from a total of eight subjects with acute knee injuries a first time, within one week of the injury, then a second time, four to six months following corrective surgery and rehabilitation. The data were collected from each subject using miniature electret microphones placed on the medial and lateral side of the patella during knee flexion and extension exercises. From the acoustical signals measured from each subject, we computed the -value using the modified Gutenberg-Ritcher equation which is widely used in seismology. The -value increased for each subject's injured knee from immediately following the injury to several months post recovery. (mean -value: 1.46 ± 0.35 [injured] and 1.92 ± 0.21 [post-surgery and recovery], p < 0.01). In addition, we compared this analysis technique against an unsupervised machine learning algorithm from our previous work and found that the -value metric can be as effective to differentiate changes in the joint sounds as our prior approach while requiring less computational time and complexity - both of which are preferable for future integration of this technology into a wearable system.
本文探讨了一种自动值提取算法在解释膝关节运动时产生的声音方面的新应用。对总共八名急性膝关节损伤患者进行了声学发射记录，第一次记录是在受伤后一周内，第二次记录是在矫正手术和康复后的四至六个月。在膝关节屈伸运动期间，使用放置在髌骨内侧和外侧的微型驻极体麦克风从每个受试者收集数据。根据从每个受试者测量的声学信号，我们使用地震学中广泛使用的修正古登堡-里希特方程计算值。从受伤后立即到恢复后的几个月，每个受试者受伤膝关节的值都有所增加。（平均值：1.46±0.35[受伤时]和1.92±0.21[手术后及恢复后]，p<0.01）。此外，我们将这种分析技术与我们之前工作中的一种无监督机器学习算法进行了比较，发现值指标在区分关节声音变化方面与我们之前的方法一样有效，同时所需的计算时间和复杂度更低——这两者对于该技术未来集成到可穿戴系统中都是更可取的。
