Mazomenos Evangelos B, Biswas Dwaipayan, Cranny Andy, Rajan Amal, Maharatna Koushik, Achner Josy, Klemke Jasmin, Jobges Michael, Ortmann Steffen, Langendorfer Peter
IEEE J Biomed Health Inform. 2016 Jul;20(4):1088-99. doi: 10.1109/JBHI.2015.2431472. Epub 2015 May 8.
This paper reports an algorithm for the detection of three elementary upper limb movements, i.e., reach and retrieve, bend the arm at the elbow and rotation of the arm about the long axis. We employ two MARG sensors, attached at the elbow and wrist, from which the kinematic properties (joint angles, position) of the upper arm and forearm are calculated through data fusion using a quaternion-based gradient-descent method and a two-link model of the upper limb. By studying the kinematic patterns of the three movements on a small dataset, we derive discriminative features that are indicative of each movement; these are then used to formulate the proposed detection algorithm. Our novel approach of employing the joint angles and position to discriminate the three fundamental movements was evaluated in a series of experiments with 22 volunteers who participated in the study: 18 healthy subjects and four stroke survivors. In a controlled experiment, each volunteer was instructed to perform each movement a number of times. This was complimented by a seminaturalistic experiment where the volunteers performed the same movements as subtasks of an activity that emulated the preparation of a cup of tea. In the stroke survivors group, the overall detection accuracy for all three movements was 93.75% and 83.00%, for the controlled and seminaturalistic experiment, respectively. The performance was higher in the healthy group where 96.85% of the tasks in the controlled experiment and 89.69% in the seminaturalistic were detected correctly. Finally, the detection ratio remains close ( ±6%) to the average value, for different task durations further attesting to the algorithms robustness.
本文报告了一种用于检测三种基本上肢运动的算法，即伸手取物、屈肘和手臂绕长轴旋转。我们使用两个MARG传感器，分别附着在肘部和腕部，通过基于四元数的梯度下降方法和上肢双连杆模型进行数据融合，从中计算出上臂和前臂的运动学特性（关节角度、位置）。通过在一个小数据集上研究这三种运动的运动学模式，我们得出了指示每种运动的判别特征；然后将这些特征用于制定所提出的检测算法。我们采用关节角度和位置来区分这三种基本运动的新方法，在一系列实验中对22名参与研究的志愿者进行了评估：18名健康受试者和4名中风幸存者。在一项对照实验中，每位志愿者被要求多次执行每种运动。这得到了一项半自然实验的补充，在该实验中，志愿者将相同的运动作为模拟泡茶准备活动的子任务来执行。在中风幸存者组中，对于对照实验和半自然实验，所有三种运动的总体检测准确率分别为93.75%和83.00%。在健康组中表现更高，对照实验中96.85%的任务和半自然实验中89.69%的任务被正确检测。最后，对于不同的任务持续时间，检测率与平均值保持接近（±6%），进一步证明了该算法的稳健性。
