Transfer Learning in Smart Home Scenario

With the development in sensor technology ambiance of human beings are becoming intelligent to cater to the needs and enhance their living standards. As human is dynamic in nature; therefore, a solution should be tailored to the needs of an individual. This requires the capability to understand, analyze and learn the behavior of a human being. To learn human behavior, machine learning algorithms require a sufficient amount of training data. Collection of data and labeling data consumes an ample amount of time. Also, it is not possible to collect data in every possible scenario. To deal with the mentioned problem, in the paper the concept of Transfer learning has been leveraged. The foremost requirement is to calculate the similarity and differences between a selected source domain and a target domain. For the calculation of similarities and differences, multiple parameters are defined in this paper. Multiple experiments in different scenarios were carried out to support the proposed approach. Results obtained show the effects of transfer learning in the domain of smart homes.

[1]  D. Cook,et al.  Author's Personal Copy Pervasive and Mobile Computing Activity Knowledge Transfer in Smart Environments , 2022 .

[2]  Qiang Yang,et al.  Transferring Multi-device Localization Models using Latent Multi-task Learning , 2008, AAAI.

[3]  Sethuraman Panchanathan,et al.  Topology Preserving Domain Adaptation for Addressing Subject Based Variability in SEMG Signal , 2011, AAAI Spring Symposium: Computational Physiology.

[4]  Gavriel Salomon,et al.  T RANSFER OF LEARNING , 1992 .

[5]  Yiqiang Chen,et al.  Cross-mobile ELM based Activity Recognition , 2010 .

[6]  Qiang Yang,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Transfer Learning for Activity Recognition via Sensor Mapping , 2022 .

[7]  Jake K. Aggarwal,et al.  Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  L. Corno,et al.  Theory Into Practice: A Matter of Transfer , 2007 .

[9]  Niall Twomey,et al.  Active transfer learning for activity recognition , 2016, ESANN.

[10]  Sethuraman Panchanathan,et al.  Activity gesture spotting using a threshold model based on Adaptive Boosting , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[11]  Qiang Yang,et al.  Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning , 2007, AAAI.

[12]  Alois Ferscha,et al.  Real-Time Transfer and Evaluation of Activity Recognition Capabilities in an Opportunistic System , 2011 .

[13]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[14]  C. Bray.  Transfer of learning. , 1928 .

[15]  Diane J. Cook,et al.  Transferring Learned Activities in Smart Environments , 2009, Intelligent Environments.

[16]  Qiang Yang,et al.  Cross-domain activity recognition via transfer learning , 2011, Pervasive Mob. Comput..

[17]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[18]  Masashi Sugiyama,et al.  Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition , 2012, Neurocomputing.

[19]  Diane J. Cook,et al.  Multi Home Transfer Learning for Resident Activity Discovery and Recognition , 2010 .

[20]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[21]  Diane J. Cook,et al.  Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.