Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm
Funds:
The National Natural Science Foundation of China (61602016)
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摘要: 針對老年人跌倒檢測的準確性和實時性需求，該文首先建立了基于姿態(tài)角的活動描述模型，研發(fā)了集成加速度傳感器、陀螺儀和藍牙的活動感知模塊，從而實時采集運動變化數據并使用藍牙發(fā)送到智能手機。其次，選取姿態(tài)角及加速度信號向量模作為特征量，通過卡爾曼濾波對數據進行去噪與融合，并應用滑動窗口和k-NN算法實現了可實時感知老年人跌倒并報警的系統(tǒng)。實驗證明系統(tǒng)在二分類場景下的跌倒檢測準確率為98.9%，而敏感度和特異性分別達到98.9%和98.5%，驗證了系統(tǒng)具有良好的實時性和較高的準確率。Abstract: According to the accurate and real-time requirement for fall detection. An activity model based on attitude angles is firstly established. A sensor board integrated with trial-axil accelerator and gyroscope is developed, which can capture the accelerations and angular velocities of human activities and transmit them to a smart phone by Bluetooth. Secondly, the three-dimensional attitude angle and acceleration signal vector magnitude are selected as features for fall detection. The collected data is preprocessed using Kalman filter to reduce noise and enhance the precision of attitude angle calculation. The k-Nearest Neighbor (k-NN) algorithm and appropriate sliding window are introduced to develop the fall detection and alert system. At last, the experimental results show that the system discriminates falls from the activities of daily living with accuracy of 98.9%, while the sensitivity and specificity are 98.9%, and 98.5% respectively. It proves that the method has favorable accuracy and reliability.
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