在诸多物联网实际应用中,原始采集信号数据多含有大量噪声,特别是在运动相关场景里.需从含大量噪声的一维时序信号中对有效信号活动区域起止点进行准确识别,以支持相关分析.已有的基于双阈值规则的识别方法对噪声十分敏感,噪声的存在会导致计算出的识别阈值无法匹配非噪声段的原始数据,从而导致将随机噪声数据识别为信号活动区间或者漏检信号活动区间.基于机器学习和深度学习的识别方法需要大量的样本数据,在样本量较小的物联网场景中模型会产生欠拟合问题,从而降低识别精度.为了对含有大量噪声且数据量少的一维时序信号中的信号活动区间进行准确识别,提出了一种基于局部动态阈值的信号活动区间识别方法EasiLTOM(signal activity interval recognition based on local dynamic threshold).该方法基于局域信号计算识别阈值,并使用最短信号长度对噪声尖峰进行过滤,可避免随机噪声对信号活动区间识别的影响,解决漏检和误检问题,从而提高识别精度.此外,EasiLTOM方法所需数据量小,适用于数据稀少的物联网场景.为验证EasiLTOM方法的有效性,该研究于3个月间采集了 14人次的表面肌电数据,并使用2个公开数据集进行了对比实验.结果表明:EasiLTOM方法对信号活动区间可达到平均93.17％的识别精度,相对于现有的双阈值和机器学习方法,分别提升了 15.03％和4.70％,在运动分析相关场景中具有实用价值.
目的 探索基于机器学习的人工智能(Artificial Intelligence,AI)辅助诊疗系统在非特异性腰痛诊断中的应用效果.方法 使用Thought Technology Ltd生产的FlexComp Infiniti 10肌电仪采集受试者腰部左右两侧多裂肌、左右两侧最长肌和左右两侧腰髂肋肌的肌电信号,15名受试者均为确诊非特异性腰痛患者,一次采集流程包括屈曲放松运动五次,双足桥式运动、左足桥式运动、右足桥式运动和Biering Sorensen等长运动各一次.3名资深医师使用常规诊断方式对患者进行诊断,并以此为标准,对比评价AI辅助诊断系统获得的结果.结果 AI辅助诊疗系统对本文选取的15名非特异性腰痛患者的检出准确性达到100％,且对于肌肉募集能力、疲劳速度和静息速度的检测结果与医生常规诊断方式相比具有良好的一致性,平均诊断用时减少26.3 min,具有统计学意义(P<0.05).结论 初步验证表明,该AI辅助诊疗系统可对非特异性腰痛提供准确高效的辅助诊断及量化评估,为临床检测提供可靠帮助.
We developed a portable non-specific low back pain measurement system EasiLBP and evaluated its performance in collecting EMG signals:during the wearer's movement without the assistance of a doctor, the collection of EMG signals by portable devices met problems such as large noise interference, difficulty in accurately calibrating the start and end points of the action interval, and imbalanced samples for feature recognition, et al. To challenge these problems, we proposed a small group-based noise removal method, a dynamic dual-threshold automatic method for identifying the start and end points of the motion interval, and a sampling method to balance group samples, respectively. Portable device and a medical EMG acquisition equipment Thought Technology FlexComp Infiniti 10 were used to perform EMG measurements on 15 patients with non-specific low back pain and 15 normal people. Clinical experiments and statistical analysis show that the portable EMG acquisition system has significant differences in EMG signal characteristics between normal people and non-specific low back pain patients, and it has good measurement consistency and accuracy with the medical EMG acquisition equipment.
