论文标题
使用隐藏的马尔可夫模型从可穿戴肌电图传感器中检测到活性检测
Activity Detection from Wearable Electromyogram Sensors using Hidden Markov Model
论文作者
论文摘要
在医疗保健系统的消费电子产品，手势分析，识别和手语交流的最新进步期间，表面肌电图（SEMG）已获得了重要的重要性。对于这样的系统，必须在连续记录的SEMG信号中确定活动区域。拟议的工作使用在执行各种手势时记录的SEMG信号基于隐藏的Markov模型（HMM）提供了一种新型的活动检测方法。检测过程是通过使用数学模型来基于概率前景设计的。阈值对活动检测的要求被避免了，使其受试者和活动无关。通过将检测到的过渡区域周围的信号段分类为活动或休息，可以断言预测输出的正确性。将分类的输出与受试者进行活动的刺激中的过渡区域进行比较。该活动的精度平均为96.25％，而活动终止区域的平均精度为87.5％，而考虑的六个活动和四个受试者。
Surface electromyography (sEMG) has gained significant importance during recent advancements in consumer electronics for healthcare systems, gesture analysis and recognition and sign language communication. For such a system, it is imperative to determine the regions of activity in a continuously recorded sEMG signal. The proposed work provides a novel activity detection approach based on Hidden Markov Models (HMM) using sEMG signals recorded when various hand gestures are performed. Detection procedure is designed based on a probabilistic outlook by making use of mathematical models. The requirement of a threshold for activity detection is obviated making it subject and activity independent. Correctness of the predicted outputs is asserted by classifying the signal segments around the detected transition regions as activity or rest. Classified outputs are compared with the transition regions in a stimulus given to the subject to perform the activity. The activity onsets are detected with an average of 96.25% accuracy whereas the activity termination regions with an average of 87.5% accuracy with the considered set of six activities and four subjects.
