基于阈值和极端随机树的实时跌倒检测方法

针对基于可穿戴设备的跌倒检测存在的实时性与准确性无法兼得的问题，提出一种阈值和极端随机树融合的实时跌倒检测方法。在该方法中，可穿戴设备只需计算阈值量，无需确保跌倒检测的准确率，从而减少了计算量；同时，上位机利用极端随机树算法确保了跌倒检测的准确率。可穿戴设备通过阈值的方法过滤了大部分日常动作，因此减少了上位机检测的动作数据量。这样一来所提方法既满足了跌倒检测的高准确率，又满足了实时性。另外，为了降低跌倒检测的假阳性率，可穿戴设备融合了姿态角度传感器和压力传感器，上位机中加入了反馈机制。当检测结果出现假阳性时，通过上位机将检测错误的样本加入非跌倒数据集中进行再训练，模型经过这样的不断学习会生成适合个人的报警模型，且这种反馈机制为降低跌倒检测的假阳性率提供了新思路。实验结果表明，在1 259个测试样本中，所提方法具有平均99.7%的准确率，最低0.08%的假阳性率。

Real-time fall detection method based on threshold and extremely randomized tree

Aiming at the problem that wearable device-based fall detection cannot have good accuracy real-timely, a real-time fall detection method based on the fusion of threshold and extremely randomized tree was proposed. In this method, the wearable devices only needed to calculate the threshold value and did not need to ensure the accuracy of fall detection, which reduced the amount of calculation; at the same time, the host computer used the extremely randomized tree algorithm to ensure the accuracy of fall detection. Most of the daily actions were filtered by the wearable devices through the threshold method, so as to reduce the amount of action data detected by the host computer. In this way, the proposed method had high accuracy of fall detection in real time. In addition, in order to reduce the false positive rate of fall detection, the attitude angle sensor and the pressure sensor were integrated into the wearable devices, and the feedback mechanism was added to the host computer. When the detection result was false positive, the wrong detected sample was added to the non-fall dataset for retraining through the host computer. Through this kind of continuous learning, the model would generate an alarm model suitable for the individual. And this feedback mechanism provided a new idea for reducing the false positive rate of fall detection. Experimental results show that in 1 259 test samples, the proposed method has an average accuracy of 99.7% and the lowest false positive rate of 0.08%.