Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm

HE Jian; ZHOU Mingwo; WANG Xiaoyi

doi:10.11999/JEIT170173

Volume 39 Issue 11

Nov. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(11): 2627-2634

HE Jian, ZHOU Mingwo, WANG Xiaoyi. Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2627-2634. doi: 10.11999/JEIT170173

Citation:

HE Jian, ZHOU Mingwo, WANG Xiaoyi. Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm[J]. Journal of Electronics & Information Technology, 2017, 39(11): 2627-2634. doi: 10.11999/JEIT170173

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Wearable Method for Fall Detection Based on Kalman Filter and k-NN Algorithm

doi: 10.11999/JEIT170173

Funds:

The National Natural Science Foundation of China (61602016)

Received Date: 2017-02-20
Rev Recd Date: 2017-08-10
Publish Date: 2017-11-19

Abstract

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.
- Computer application technology,
- Fall detection,
- Data fusion,
- Kalman filter,
- k-NN algorithm,
- Attitude angle,
- Signal vector magnitude

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References(20)

References

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