GestureKeeper：用于在IoT环境中控制设备的手势识别
GestureKeeper: Gesture Recognition for Controlling Devices in IoT Environments
摘要：
本文介绍和评估GestureKeeper，一个强大的手势识别系统的基础上可穿戴惯性测量单元（IMU）。在不依赖于明确的用户动作或特殊手势标记的情况下识别手势发生的时间窗口是非常具有挑战性的任务。为了解决这个问题，GestureKeeper通过使用递归量化分析（RQA）利用相关时间序列的潜在动态来识别手势的开始。RQA是一种强有力的非线性时间序列分析方法，它可以检测系统动力学行为中的临界跃迁。最重要的是，它没有对管理数据的底层分布或模型做出任何假设。在估计手势窗口之后，采用支持向量机来识别特定手势。我们所提出的方法是通过一个小规模的试点研究在FORTH进行评估，并证明GestureKeeper可以正确地识别一个手势的开始与87\%的平均平衡准确率和正确分类的平均准确率超过96\%的特定手势。据我们所知，GestureKeeper是第一个仅基于加速度计的自动手势识别系统。性能分析揭示了预测能力的功能和系统的鲁棒性存在的加性噪声。我们还进行了敏感性分析，以检查各种参数的影响和不同分类器（SVM，随机森林）的比较分析。最重要的是，该系统可以扩展为包含大量手势字典，并且无需为新用户进行进一步校准即可操作。
英文摘要：
This paper introduces and evaluates the GestureKeeper, a robust hand-gesture recognition system based on a wearable inertial measurements unit (IMU). The identification of the time windows where the gestures occur, without relying on an explicit user action or a special gesture marker, is a very challenging task. To address this problem, GestureKeeper identifies the start of a gesture by exploiting the underlying dynamics of the associated time series using a recurrence quantification analysis (RQA). RQA is a powerful method for nonlinear time-series analysis, which enables the detection of critical transitions in the system's dynamical behavior. Most importantly, it does not make any assumption about the underlying distribution or model that governs the data. Having estimated the gesture window, a support vector machine is employed to recognize the specific gesture. Our proposed method is evaluated by means of a small-scale pilot study at FORTH and demonstrated that GestureKeeper can identify correctly the start of a gesture with a 87\% mean balanced accuracy and classify correctly the specific hand-gesture with a mean accuracy of over 96\%. To the best of our knowledge, GestureKeeper is the first automatic hand-gesture identification system based only on accelerometer. The performance analysis reveals the predictive power of the features and the system's robustness in the presence of additive noise. We also performed a sensitivity analysis to examine the impact of various parameters and a comparative analysis of different classifiers (SVM, random forests). Most importantly, the system can be extended to incorporate a large dictionary of gestures and operate without further calibration for a new user.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
