A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning

被引：8

作者：

Wu, Tong ^{[1
,2
]}

Gu, Yang ^{[1
,2
,3
]}

Chen, Yiqiang ^{[1
,2
,3
]}

Wang, Jiwei ^{[1
,2
]}

Zhang, Siyu

机构：

[1] Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing, Peoples R China

[2] Univ Chinese Acad Sci, Beijing, Peoples R China

[3] Peng Cheng Lab, Shenzhen, Peoples R China

来源：

22ND INTERNATIONAL ACM SIGACCESS CONFERENCE ON COMPUTERS AND ACCESSIBILITY (ASSETS '20) | 2020年

基金：

北京市自然科学基金;

关键词：

Fall detection; ensemble learning; mobile cloud collaboration; older people; health monitoring; WEARABLE-SENSOR;

D O I：

10.1145/3373625.3417010

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Falls are one of the major causes of accidental or unintentional injury death worldwide. Therefore, this paper proposes a reliable fall detection algorithm and a mobile cloud collaboration system for fall detection. The algorithm is an ensemble learning method based on decision tree, named Fall-detection Ensemble Decision Tree (FEDT). The mobile cloud collaboration system is composed of three stages: 1) mobile stage: a light-weighted threshold method is used to filter out activities of daily livings (ADLs), 2) collaboration stage: TCP protocol is used to transmit data to cloud and meanwhile features are extracted in the cloud, 3) cloud stage: the model trained by FEDT is deployed to give the final detection result with the extracted features. Experiments show that the proposed FEDT outperforms the others' over 1-3% both on sensitivity and specificity and has superior robustness on different devices.

引用

页数：7

共 30 条

[11] Integrating the enriched feature with machine learning algorithms for human movement and fall detection
Li, Chenghua
Lin, Man
Yang, Laurence T.
Ding, Chen
[J]. JOURNAL OF SUPERCOMPUTING, 2014, 67 (03) : 854 - 865
[12] Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data
Lu, Na
Wu, Yidan
Feng, Li
Song, Jinbo
[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2019, 23 (01) : 314 - 323
[13] Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing
Meng, Lei
Miao, Chunyan
Leung, Cyril
[J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2017, 76 (08) : 10779 - 10799
[14] Merrouche F, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP), P586, DOI 10.1109/SIPROCESS.2016.7888330
[15] Min Xiao, 2020, Artificial Intelligence in China. Proceedings of the International Conference on Artificial Intelligence in China. Lecture Notes in Electrical Engineering (LNEE 572), P46, DOI 10.1007/978-981-15-0187-6_6
[16] Nadee C, 2015, 2015 12TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING/ELECTRONICS, COMPUTER, TELECOMMUNICATIONS AND INFORMATION TECHNOLOGY (ECTI-CON)
[17] Nguyen LP, 2017, INT C IOT TECHN HEAL, P34
[18] Ojetola O., 2015, PROC 6 ACM MULTIMEDI, P243, DOI [DOI 10.1145/2713168.2713198, 10.1145/2713168.2713198]
[19] Putra IPES, 2017, 2017 IEEE LIFE SCIENCES CONFERENCE (LSC), P21, DOI 10.1109/LSC.2017.8268134
[20] Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment
Qu, Junsuo
Wu, Chen
Li, Qian
Wang, Ting
Soliman, Abdel Hamid
[J]. SENSORS AND MATERIALS, 2020, 32 (04) : 1209 - 1221

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