| CHEN Jing(陈 晶)* ** ***,YAN Bowen** ***,YE Xiaoxia*,ZHANG Hao*.[J].高技术通讯(英文),2025,31(2):131~143 |
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| Research on fall detection algorithm based on heterogeneous sensor data fusion |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 02. 003 |
| 中文关键词: |
| 英文关键词: fall detection, acceleration, audio, decision fusion, Dempster-shafer Theory (D-S Theory) |
| 基金项目: |
| Author Name | Affiliation | | CHEN Jing(陈 晶)* ** *** | (* College of Mathematics and Computer Science , Guangdong Ocean University, Zhanjiang 524088, P. R. China)
(** College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, P. R. China)
(*** Hebei Key Laboratory of Virtual Technology and System Integration, Qinhuangdao 066004, P. R. China ) | | YAN Bowen** *** | | | YE Xiaoxia* | | | ZHANG Hao* | |
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| 中文摘要: |
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| 英文摘要: |
| In order to solve the problems of limited performance of single and homogeneous sensors and high false alarm rate caused by environmental noise, heterogeneous sensor data fusion (HSDF), a fall detection framework based on heterogeneous sensor fusion of acceleration and audio signals, is proposed in this paper. By analyzing the heterogeneity of acceleration data and audio data, the framework uses Dempster-shafer Theory (D-S Theory) to integrate the output of acceleration and audio data at decision level. Firstly, a normalized window interception algorithm ——— anomaly location window algorithm (ALW) is proposed by analyzing the fall process and the characteristics of acceleration changes based on acceleration data. Secondly, the one-dimensional residual convolutional network (1D-ReCNN) is designed for fall detection based on the audio data. Finally, it is verified that the HSDF framework has good advantages in terms of sensitivity and false alarm rate by the collection of volunteers’ simulated fall data and free living data in real environment. |
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