A Transfer Learning Algorithm Applied to Human Activity Recognition

doi:10.12068/j.issn.1005-3026.2022.06.003

Abstract

Abstract: Collecting wearable motion sensor signals and using transfer learning to overcome the inconsistency of data distribution to identify the daily behavior of the human body are very popular technologies. Using wearable sensors to collect signals will result in generating noise samples that affect the transfer effect. Traditional algorithms lack the processing of these samples. To solve this problem， the traditional algorithm was improved by introducing a sample screening algorithm based on Mahalanobis distance， and a transfer learning algorithm T-WMD was proposed that can be used for human activity recognition. And compared with other five algorithms on two public human activity recognition data sets， the results show that the algorithm proposed in this paper can effectively improve the effect of transfer learning.

Key words: physiological signal; human activity recognition; transfer learning; body area network; machine learning

CLC Number:

TP181

ZHAO Hai， CHEN Jia-wei， SHI Han， WANG Xiang. A Transfer Learning Algorithm Applied to Human Activity Recognition[J]. Journal of Northeastern University(Natural Science), 2022, 43(6): 776-782.

References

[1]汪成亮，王小均.基于三轴传感器的老年人日常活动识别［J］.电子学报，2017，45(3):570-576.(Wang Cheng-liang，Wang Xiao-jun.Daily activity recognition based on triaxial accelerometer of elderly people［J］.Acta Electronica Sinica，2017，45(3):570-576.)
[2]Ramirez H，Velastin S，Fabregas E，et al.Fall detection using human skeleton features［J］.IEEE Access，2021，4:73-78
[3]范长军，高飞.基于无线体域网的在线人体活动识别［J］.小型微型计算机系统，2020，41(1):72-77.(Fan Chang-jun，Gao Fei.Online human activity recognition based on wireless body area network［J］.Journal of Chinese Computer Systems，2020，41(1):72-77.)
[4]殷晓玲，陈晓江，夏启寿，等.基于智能手机内置传感器的人体运动状态识别［J］.通信学报，2019，40(3):157-169.(Yin Xiao-ling，Chen Xiao-jiang，Xia Qi-shou，et al.Human motion state recognition based on smart phone built-in sensor［J］.Journal on Communications，2019，40(3):157-169.)
[5]Fu Z，He X，Wang E，et al.Personalized human activity recognition based on integrated wearable sensor and transfer learning［J］.Sensors，2021，21(3):885.
[6]郑增威，杜俊杰，霍梅梅，等.基于可穿戴传感器的人体活动识别研究综述［J］.计算机应用，2018，38(5):1223-1229.(Zheng Zeng-wei，Du Jun-jie，Huo Mei-mei，et al.Review of human activity recognition based on wearable sensors［J］.Journal of Computer Applications，2018，38(5):1223-1229.)
[7]Hou W X，Wang J D，Tan X，et al.Cross-domain speech recognition with unsupervised character-level distribution matching［C］// Interspeech 2021.Brno，2021:1-5.
[8]彭雨荷，陈翔，陈双武，等.基于迁移学习的跨域异常流量检测［J］.北京邮电大学学报，2021，44(2):33-39.(Peng Yu-he，Chen Xiang，Chen Shuang-wu，et al.Cross-domain abnormal traffic detection based on transfer learning［J］.Journal of Beijing University of Posts and Telecommunications，2021，44(2):33-39.)
[9]Soleimani E，Nazerfard E.Cross-subject transfer learning in human activity recognition systems using generative adversarial networks［J］.Neurocomputing，2021，426:26-34.
[10]Niu H，Nguyen D，Yonekawa K，et al.Multi-source transfer learning for human activity recognition in smart homes［C］// 2020 IEEE International Conference on Smart Computing(SMARTCOMP).Bologna，2020:274-277.
[11]Long M，Wang J，Ding G，et al.Transfer joint matching for unsupervised domain adaptation［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Columbus，2014:1410-1417.
[12]Luo Z，Zou Y，Hoffman J，et al.Label efficient learning of transferable representations across domains and tasks［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems.Long Beach，2017:164-176.
[13]Dai W Y，Yang Q，Xue G R，et al.Boosting for transfer learning［C］// Proceedings of the 24th International Conference on Machine Learning.Corvallis，2007:193-200.
[14]Pan S J，Tsang I W，Kwok J T，et al.Domain adaptation via transfer component analysis［J］.IEEE Transactions on Neural Networks，2011，22(2):199-210.
[15]Mahalanobis P C.On the generalised distance in statistics［J］.Proceedings of the National Institute of Sciences of India，1936，2:49-55.
[16]Reiss A，Stricker D.Creating and benchmarking a new dataset for physical activity monitoring［C］// Proceedings of the 5th International Conference on Pervasive Technologies Related to Assistive Environments.Newcastle，2012:1-8.
[17]Banos O，Garcia R，Holgado-Terriza J A，et al.mHealthDroid:a novel framework for agile development of mobile health applications［C］// International Workshop on Ambient Assisted Living.Belfast，2014:91-98.
[18]Banos O，Villalonga C，Garcia R，et al.Design，implementation and validation of a novel open framework for agile development of mobile health applications［J］.Biomedical Engineering Online，2015，14(sup2):1-20.
[19]Wang J，Chen Y，Hao S，et al.Balanced distribution adaptation for transfer learning［C］// 2017 IEEE International Conference on Data Mining(ICDM).New Orleans，2017:1129-1134.
[20]Long M，Wang J，Ding G，et al.Transfer feature learning with joint distribution adaptation［C］// Proceedings of the IEEE International Conference on Computer Vision.Sydney，2013:2200-2207.