RF-ARP: RFID-Based Activity Recognition and Prediction in Smart Home

Smart Home is generally considered to be the final solution for human living problem, especially for health care of the elderly and disabled, power saving, etc. Human activity recognition in smart home is the key to achieve home automation, which enables smart services automatically run according to human mind. Recent researches have made several progresses in this field, however most of them can only recognize default activities which is probably not needed by smart home services. In addition, low scalability makes such researches infeasible out of laboratory. In this work, we unwrap this issue and propose a novel framework to not only recognize human activity, but also predict it. The framework contains three stages: recognition after the activity; recognition in progress and activity prediction in advance. With the help of RFID tags, the hardware cost of our framework is low enough to popularize. And the experiment result shows that our framework can realize good performance in activity recognition and prediction with high scalability.

[1]  Matthai Philipose.  Large-Scale Human Activity Recognition Using Ultra-Dense Sensing , 2005 .

[2]  Kent Larson,et al.  The Design of a Portable Kit of Wireless Sensors for Naturalistic Data Collection , 2006, Pervasive.

[3]  Zhiyang Li,et al.  MVSS: Mobile Visual Search Based on Saliency , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[4]  Yuto Lim,et al.  RF-Switch: A Novel Wireless Controller in Smart Home , 2018, 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW).

[5]  Jian Lu,et al.  An unsupervised approach to activity recognition and segmentation based on object-use fingerprints , 2010, Data Knowl. Eng..

[6]  Zhiyang Li,et al.  A Low Overhead Progressive Transmission for Visual Descriptor Based on Image Saliency , 2015, J. Multiple Valued Log. Soft Comput..

[7]  Araceli Sanchis,et al.  Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors , 2013, Sensors.

[8]  Robert Bergevin,et al.  Semantic human activity recognition: A literature review , 2015, Pattern Recognit..

[9]  Simon Haykin,et al.  Smart Home: Cognitive Interactive People-Centric Internet of Things , 2017, IEEE Communications Magazine.

[10]  Jie Wu,et al.  Fast Identification of Blocked RFID Tags , 2018, IEEE Transactions on Mobile Computing.

[11]  Jiannong Cao,et al.  Efficient Range Queries for Large-Scale Sensor-Augmented RFID Systems , 2019, IEEE/ACM Transactions on Networking.

[12]  Karthikeyan Sundaresan,et al.  RIO: A Pervasive RFID-based Touch Gesture Interface , 2017, MobiCom.

[13]  Tao Gu,et al.  Object relevance weight pattern mining for activity recognition and segmentation , 2010, Pervasive Mob. Comput..

[14]  Zhaozheng Yin,et al.  Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks , 2015, ACM Multimedia.

[15]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[16]  Jiannong Cao,et al.  Fast RFID Sensory Data Collection: Trade-off Between Computation and Communication Costs , 2019, IEEE/ACM Transactions on Networking.

[17]  Kaishun Wu,et al.  GRfid: A Device-Free RFID-Based Gesture Recognition System , 2017, IEEE Transactions on Mobile Computing.

[18]  Faicel Chamroukhi,et al.  Physical Human Activity Recognition Using Wearable Sensors , 2015, Sensors.

[19]  Tieniu Tan,et al.  Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts , 2016, AAAI.

[20]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .