A review on radio based activity recognition

Digital Communications and Networks

Volumn 1, Issue 1, 2015, Pages 20-29

  Wang, Shuangquan ^a,b   Zhou, Gang ^a  

^a The College of William and Mary   (United States)

^b INSTITUTE OF COMPUTING TECHNOLOGY   (China)

Author keywords

Activity recognition; Radio; RFID; WiFi; Zigbee

Indexed keywords

EID: 84994802778     PISSN: 24685925     EISSN: 23528648     Source Type: Journal    
DOI: 10.1016/j.dcan.2015.02.006     Document Type: Review

References (68)

1. Kim, E., Helal, S., Cook, D., Human activity recognition and pattern discovery. IEEE Pervasive Comput. 9 (2010), 48–53.
2. Q. Li, G. Zhou, J.A. Stankovic, Accurate, fast fall detection method using posture and context information, in: Proceedings of the 6th ACM International Conference on Embedded Networked Sensor Systems, 2008, pp. 443–444.
3. Q. Li, J.A. Stankovic, M.A. Hanson, A. Barth, J. Lach, G. Zhou, Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information, in: Proceedings of the Sixth International Workshop on Wearable and Implantable Body Sensor Networks, 2009, pp. 138–143.
4. T. Hao, G. Xing, G. Zhou, iSleep: unobtrusive sleep quality monitoring using smartphones, in: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, 2013.
5. X. Qi, Y. Li, M. Keally, Z. Ren, G. Zhou, AdaSense: adapting sampling rates for activity recognition in body sensor networks, in: Proceedings of the 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium, 2013, pp. 163–172.
6. Y. Tang, S. Wang, Y. Chen, Z. Chen, PPCare: a personal and pervasive health care system for the elderly, in: Proceedings of the 9th International Conference on Ubiquitous Intelligence and Computing, 2012, pp. 935–939.
7. Hu, L., Chen, Y., Wang, S., Chen, Z., b-COELM: a fast, lightweight and accurate activity recognition model for mini-wearable devices. Mob. Comput. 15 (2014), 200–214.
8. T.V. Duong, H.H. Bui, D.Q. Phung, S. Venkatesh, Activity recognition and abnormality detection with the switching hidden semi-Markov model, in: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 838–845.
9. Y. Jia, Diatetic and exercise therapy against diabetes mellitus, in: Proceedings of Second International Conference on Intelligent Networks and Intelligent Systems, 2009, pp. 693–696.
10. Yin, J., Yang, Q., Pan, J., IEEE Trans. Knowl. Data Eng. 20 (2008), 1082–1090.
11. M. Keally, G. Zhou, G. Xing, J. Wu, A. Pyles, PBN: Towards Practical activity recognition using smartphone-based body sensor networks, in: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, 2011, pp. 246–259.
12. M. Keally, G. Zhou, G. Xing, J. Wu, Remora: sensing resource sharing among smartphone-based body sensor networks, in: Proceedings of 2013 IEEE/ACM 21st International Symposium on Quality of Service, 2013, pp. 1–10.
13. Wang, S., Chen, Y., Chen, Z., Recognizing transportation mode on mobile phone using probability fusion of extreme learning machines. Int. J. Uncertain. Fuzz. Knowl. Based Syst. 21 (2013), 13–22.
14. L. Hu, Y. Chen, S. Wang, L. Jia, A nonintrusive and single-point infrastructure-mediated sensing approach for water-use activity recognition, in: Proceedings of the 11th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 2013, pp. 2120–2126.
15. Kasteren, T.L., Englebienne, G., Kröse, B., An activity monitoring system for elderly care using generative and discriminative models. J. Pers. Ubiquitous Comput. 14 (2010), 489–498.
16. M. Buettner, R. Prasad, M. Philipose, D. Wetherall, Recognizing daily activities with RFID-based sensors, in: Proceedings of the 11th international conference on Ubiquitous Computing, 2009, pp. 51–60.
17. P. Hevesi, S. Willea, G. Pirkl, N. Wehn, P. Lukowicz, Monitoring household activities and user location with a cheap, unobtrusive thermal sensor array, in: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014, pp. 141–145.
18. Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P., Pfinder: Real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19 (1997), 780–785.
19. J. Shotton, A. Fitzgibbon, M. Cook, T. Sharp, M. Finocchio, R. Moore, A. Kipman, A. Blake, Real-time human pose recognition in parts from single depth images, in: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 1297–1304.
20. F. Huo, E. Hendriks, P. Paclik, A.H.J. Oomes, Markerless human motion capture and pose recognition, in: Proceedings of the 10th IEEE Workshop on Image Analysis for Multimedia Interactive Services, 2009, pp. 13–16.
21. R. Mehran, A. Oyama, M. Shah, Abnormal crowd behavior detection using social force model, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 935–942.
22. A.T. Campbell, S.B. Eisenman, K. Fodor, N.D. Lane, H. Lu, E. Miluzzo, M. Musolesi, R.A. Peterson, X. Zheng, CenceMe: injecting sensing presence into social network applications using mobile phones (Demo Abstract), in: Proceedings of the Ninth ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2008.
23. Y. Ke, R. Sukthankar, M. Hebert, Spatio-temporal shape and flow correlation for action recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007, pp. 1–8.
24. W. Lu, J.J. Little, Simultaneous tracking and action recognition using the PCA-HOG descriptor, in: Proceedings of the 3rd Canadian Conference on Computer and Robot Vision, 2006, pp. 6.
25. Ohgi, Y., Yasumura, M., Ichikawa, H., Miyaji, C., Analysis of stroke technique using acceleration sensor IC in freestyle swimming. Subic, A.J., Haake, S.J., (eds.) The Engineering of SPORT, 2000, Blackwell Science, Oxford, 503–511.
26. Lara, O.D., Labrador, M.A., A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15 (2013), 1192–1209.
27. H.T. Cheng, F.T. Sun, M. Griss, P. Davis, J. Li, D. You, NuActiv: recognizing unseen new activities using semantic attribute-based learning, in: Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services, 2013, pp. 361–374.
28. K. Aminian, F. Dadashi, B. Mariani, C.L. Hoskovec, B.S. Eggimann, C.J. Büla, Gait analysis using shoe-worn inertial sensors: how is fot clearance related to walking speed? in: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014, pp. 481–485.
29. Maekawa, T., Kishino, Y., Sakurai, Y., Suyama, T., Activity recognition with hand-worn magnetic sensors. Pers. Ubiquitous Comput. 17 (2013), 1085–1094.
30. O. Yürüten, J. Zhang, P.H.Z. Pu, Predictors of life satisfaction based on daily activities from mobile sensor data, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2014, pp. 497–500.
31. D. Schuldhaus, H. Leutheuser, B.M. Eskofier, Classification of daily life activities by decision level fusion of inertial sensor data, in: Proceedings of the 8th International Conference on Body Area Networks, 2013, pp. 77–82.
32. F.O. Mokaya, B. Nguyen, C. Kuo, Q. Jacobson, A. Rowe, P. Zhang, MARS: a muscle activity recognition system enabling self-configuring musculoskeletal sensor networks, in: Proceedings of the 12th International Conference on Information Processing in Sensor Networks, 2013, pp. 191–202.
33. Ke, S., Hoang, L.U.T., Lee, Y., Hwang, J., Yoo, J., Choi, K., A review on video-based human activity recognition. Computers 2 (2013), 88–131.
34. X. Ren, C. Gu, Figure-ground segmentation improves handled object recognition in egocentric video, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2010, pp. 3137–3144.
35. R. Messing, C. Pal, H. Kautz, Activity recognition using the velocity histories of tracked keypoints, in: Proceedings of IEEE 12th International Conference on Computer Vision, 2009, pp. 104-111.
36. I. Oikonomidis, N. Kyriazis, A.A. Argyros, Efficient model-based 3D tracking of hand articulations using kinect, in: Proceedings of the 22nd British Machine Vision Conference, 2011.
37. K. Lai, L. Boa, X. Ren, D. Fox, A scalable tree-based approach for joint object and pose recognition, in: Proceedings of Twenty-fifth Conference on Artificial Intelligence, 2011.
38. J. Lei, X. Ren, D. Fox, Fine-grained kitchen activity recognition using RGB-D, in: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 2012, pp. 208–211.
39. P. Rashidi, D.J. Cook, Mining sensor streams for discovering human activity patterns over time, in: Proceedings of the 2010 IEEE International Conference on Data Mining, 2010, pp. 431–440.
40. X. Qi, G. Zhou, Y. Li, G. Peng, RadioSense: exploiting wireless communication patterns for body sensor network activity recognition, in: Proceedings of the 2012 IEEE 33rd Real-Time Systems Symposium, 2012, pp. 95–104.
41. Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, H. Liu, E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures, in: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, 2014, pp. 617–628.
42. B. Kellogg, V. Talla, S. Gollakota, Bringing gesture recognition to all devices, in: Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation, 2014, pp. 303–316.
43. 〈http://en.wikipedia.org/wiki/ZigBee〉 (cited November 2014).
44. Zhou, G., He, T., Krishnamurthy, S., Stankovic, J.A., Models and solutions for radio irregularity in wireless sensor networks. ACM Trans. Sens. Netw. 2 (2006), 221–262.
45. G. Zhou, C. Wan, M.D. Yarvis, J.A. Stankovic, Aggregator-centric QoS for body sensor networks, in: Proceedings of the 6th International Symposium on Information Processing in Sensor Networks, 2007, pp. 539–540.
46. G. Zhou, J. Lu, C. Wan, M.D. Yarvis, J.A. Stankovic, BodyQoS: adaptive and radio-agnostic QoS for body sensor networks, in: Proceedings of the 27th Conference on Computer Communications, 2008.
47. Zhou, G., Wu, Y., Yan, T., He, T., Huang, C., Stankovic, J.A., Abdelzaher, T.F., ACM Trans. Embed. Comput. Syst., 9, 2010.
48. Zhou, G., Li, Q., Li, J., Wu, Y., Lin, S., Lu, J., Wan, C., Yarvis, M.D., Stankovic, J.A., Adaptive and radio-agnostic QoS for body sensor networks. ACM Trans. Embed. Comput. Syst., 10, 2011.
49. Guyon, I., Elisseeff, A., J. Mach. Learn. Res. 3 (2003), 1157–1182.
50. A. Fort, C. Desset, J. Ryckaert, P.D. Doncker, L.V. Biesen, S. Donnay, Ultra wide-band body area channel model, in: Proceedings of the 2005 IEEE International Conference on Communications, 2005, pp. 2840–2844.
51. I. Ekure, S. Wang, G. Zhou, A Theoretical Analysis of path loss based activity recognition, in: Proceedings of 11th IEEE International Conference on Mobile Ad hoc and Sensor Systems, 2014.
52. Y.I. Nechayev, P.S. Hall, C. Constantinou, H. Yang, A. Alomainy, R. Dubrovka, C.G. Parini, On-body path gain variations with changing body posture and antenna position, in: Proceedings of IEEE Antennas and Propagation Society International Symposium, 2005, pp. 731–734.
53. M. Scholz, T. Riedel, M. Hock, M. Beigl, Device-free and device-bound activity recognition using radio signal strength, in: Proceedings of the 4th Augmented Human International Conference, 2013, pp. 100–107.
54. S. Sigg, U. Blanke, G. Troester, The telepathic phone: frictionless activity recognition from WiFi-RSSI, in: Proceedings of IEEE International Conference on Pervasive Computing and Communications, 2014, pp. 148–155.
55. S. Sigg, M. Hock, M. Scholz, G. Troester, L. Wolf, Y. Ji, M. Beigl,Passive, Device-free recognition on your mobile phone: tools, features and a case study, in: Proceedings of the 10th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2013, pp. 435–446.
56. K. Wu, J. Xiao, Y. Yi, M. Gao, L.M. Ni, FILA: fine-grained indoor localization, in: Proceedings of IEEE INFOCOM, 2012, pp. 2210–2218.
57. F. Adib, D. Katabi, See through walls with Wi-Fi! in: Proceedings of the ACM conference on SIGCOMM, 2013, pp. 75–86.
58. Q. Pu, S. Gupta, S. Gollakota, S. Patel, Whole-home gesture recognition using wireless signals, in: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, 2013, pp. 27–38.
59. Z. Chen, S. Wang, Y. Chen, Z. Zhao, M. Lin, InferLoc: calibration free based location inference for temporal and spatial fine-granularity magnitude, in: Proceedings of the 15th International Conference on Computational Science and Engineering (CSE), 2012, pp. 453-460.
60. Y. Wang, J. Yang, Y. Chen, H. Liu, M. Gruteser, R.P. Martin, Tracking human queues using single-point signal monitoring, in: Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services, 2014, pp. 42–54.
61. J. Wang, D. Vasisht, D. Katabi, RF-IDraw: virtual touch screen in the air using RF signals, in: Proceedings of the 2014 ACM Conference on SIGCOMM, 2014, pp. 235–246.
62. Y. Liu, L. Chen, J. Pei, Q. Chen, Y. Zhao, Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays, in: Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications, 2007, pp. 37–46.
63. S. Shi, S. Sigg, Y. Ji, Joint localization and activity recognition from ambient fm broadcast signals, in: Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, 2013, pp. 521–530.
64. M. Scholz, S. Sigg, G. Bagschik, T. Guenther, G.V. Zengen, D. Shiskova, Y. Ji, M. Beigl, SenseWaves: radiowaves for context recognition, in: video Proceedings of the 9th International Conference on Pervasive Computing, 2011.
65. M. Scholz, S. Sigg, H.R. Schmidkte, M. Beigl, Challenges for Device-free radio-based activity recognition, in: Proceedings of the 3rd Workshop on Context Systems Design Evaluation and Optimisation (CoSDEO), 2011.
66. S. Sigg, S. Shi, Y. Ji, RF-based device-free recognition of simultaneously conducted activities, in: Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, 2013, pp. 531–540.
67. Sekine, M., Maeno, K., Activity recognition using radio doppler effect for human monitoring service. J. Inf. Process. 20 (2012), 396–405.
68. F. Adib, Z. Kabelac, H. Mao, D. Katabi, R.C. Miller, Real-time breath monitoring using wireless signals, in: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, 2014, pp. 261–262.