Kale Nimish, Lee Jaeseong, Lotfian Reza, Jafari Roozbeh
Embedded Systems and Signal Processing Lab, The University of Texas at Dallas, Richardson, TX, 75080-3021.
Proc Wirel Health. 2012 Oct;2012. doi: 10.1145/2448096.2448103.
Daily living activity monitoring is important for early detection of the onset of many diseases and for improving quality of life especially in elderly. A wireless wearable network of inertial sensor nodes can be used to observe daily motions. Continuous stream of data generated by these sensor networks can be used to recognize the movements of interest. Dynamic Time Warping (DTW) is a widely used signal processing method for time-series pattern matching because of its robustness to variations in time and speed as opposed to other template matching methods. Despite this flexibility, for the application of activity recognition, DTW can only find the similarity between the template of a movement and the incoming samples, when the location and orientation of the sensor remains unchanged. Due to this restriction, small sensor misplacements can lead to a decrease in the classification accuracy. In this work, we adopt DTW distance as a feature for real-time detection of human daily activities like in the presence of sensor misplacement. To measure this performance of DTW, we need to create a large number of sensor configurations while the sensors are rotated or misplaced. Creating a large number of closely spaced sensors is impractical. To address this problem, we use the marker based optical motion capture system and generate inertial sensor data for different locations and orientations on the body. We study the performance of the DTW under these conditions to determine the worst-case sensor location variations that the algorithm can accommodate.
日常生活活动监测对于许多疾病发作的早期检测以及提高生活质量（尤其是老年人的生活质量）非常重要。一个由惯性传感器节点组成的无线可穿戴网络可用于观察日常活动。这些传感器网络生成的连续数据流可用于识别感兴趣的动作。动态时间规整（DTW）是一种广泛用于时间序列模式匹配的信号处理方法，因为与其他模板匹配方法相比，它对时间和速度变化具有鲁棒性。尽管具有这种灵活性，但对于活动识别应用，当传感器的位置和方向保持不变时，DTW只能找到运动模板与传入样本之间的相似性。由于这种限制，传感器的小错位可能会导致分类准确率下降。在这项工作中，我们采用DTW距离作为一种特征，用于在存在传感器错位的情况下实时检测人类日常活动。为了测量DTW的这种性能，我们需要在传感器旋转或错位时创建大量的传感器配置。创建大量紧密间隔的传感器是不切实际的。为了解决这个问题，我们使用基于标记的光学动作捕捉系统，并生成身体上不同位置和方向的惯性传感器数据。我们研究在这些条件下DTW的性能，以确定该算法能够适应的最坏情况的传感器位置变化。
