Accuracy

Finally, we have evaluated the prediction accuracy of HARAR in activity recognition. We have performed all four types of activities totally for 80 tests: 23 walkings, 20 joggings, 17 sit-to-stands and 20 stand-to-sits. Each activity has lasted for 60 seconds. All the data have been mixed together and preprocessed. Then they are passed in the frequency filtering and classified into two coarse categories. Then the final recognition has been performed in each category. The recognition between walking and jogging is performed with k-means algorithm based on the relative velocity as in Section 5.3.2. The results are shown in Table 2. The accuracies for walking, jogging, sit-to-stand, and stand-to- sit are respectively: 82.6%, 95%, 82.3%, and 80%. The overall accuracy is 85%. The

Figure 15: Relative velocity of high-frequency activities

performance seems not very high, but it is outstanding with only the radar sensor without extra equipment.

Table 2: Prediction Accuracy

Recognized/Actual walking jogging sit-to-stand stand-to-sit

walking 19 1 0 0

jogging 1 19 0 0

sit-to-stand 2 0 14 4

stand-to-sit 1 0 3 16

7

CONCLUSION

In this work, we describe two novel approaches to track users location system, DiLT, and recognize the human activates method HARAR through commodity off-the-shelf radar sensors. Through the mechanical beamforming supported by a rotational ultrasonic scanning, DiLT preprocesses and analyzes collected data to determine the location of a user and track any location changes. HARAR measures the human activities with radar signals at very high frequency sampling to capture activity dynamics. Window-based signal processing algorithms are designed to remove background environment noise.

For the localization systerm, DiLT collects data by using commodity off-the-shelf ultrasonic sensors for minimal invasion of privacy, while adopting signal processing tech- niques to reveal detail dynamics embedded in the sensed data. DiLT consists of a me- chanical ultrasonic beam-forming design for omni-space sensing, a contrastive divergence learning to localize a user and a binary back-off algorithm to track the motion of the user. For the recognition system, this solution designs a set of data preprocessing algo- rithms, including a data refining algorithm to filter outlier data, a contrastive divergence algorithm to remove background static reflection, and a transformation algorithm to con- vert the signal data into feature-rich spatial location changes. This solution also develops schemes to separate a collection of various activities into individuals. A low-pass fre- quency filter is designed to remove unwanted noisy data and the motion intensity is used to classify the activities into two high-level groups. It uses a slope-based approach and a k-means clustering to further finely recognize each activity. From many experiments mon- itoring laboratory room activity, our proposed recognition algorithms have demonstrated high effectiveness and accuracy in the real environment.

Considering the future work, we expect higher accuracy by using our mode. To improve the accuracy, we can increase the input dataset size and it is necessary to use more sensor data to learn user gestures. We will utilize more machine learning algorithm even the deep learning to test our experiments. Moreover, we will continue explore the depth motion features for recognizing more specific motions in daily lives. Through different kinds of activities, we can learn the users motion feature better. Also, based on our research, the algorithm for user identify will add in the next stage system design, in order to provide more convenient lives in smart homes.

REFERENCES

[1] F. Adib, H. Mao, Z. Kabelac, D. Katabi, and R. C. Miller. Smart homes that monitor breathing and heart rate. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pages 837–846. ACM, 2015.

[2] E. Akinlabi, M. Shukla, S. Akinlabi, S. Kanyanga, and C. Chizyuka. Forming be- haviour of steel sheets after mechanical and laser beam forming. Lasers in Engineer- ing (Old City Publishing), 29, 2014.

[3] A. S. Ali, A. G. Radwan, and A. M. Soliman. Fractional order butterworth filter: active and passive realizations. IEEE Journal on emerging and selected topics in circuits and systems, 3(3):346–354, 2013.

[4] I. Amir and K. Annamalai. Methods and systems for synchronized ultrasonic real time location, Dec. 10 2013. US Patent 8,604,909.

[5] D. Chakraborty, K. Sharma, R. K. Roy, H. Singh, and T. Bezboruah. Android application based monitoring and controlling of movement of a remotely controlled robotic car mounted with various sensors via bluetooth. In Advances in Electrical, Electronic and Systems Engineering (ICAEES), International Conference on, pages 170–175. IEEE, 2016.

[6] K. Chen, H.-S. Lee, A. P. Chandrakasan, and C. G. Sodini. Ultrasonic imaging transceiver design for cmut: A three-level 30-vpp pulse-shaping pulser with im- proved efficiency and a noise-optimized receiver. IEEE Journal of Solid-State Cir- cuits, 48(11):2734–2745, 2013.

[7] S. Choi, E. Kim, and S. Oh. Human behavior prediction for smart homes using deep learning. In RO-MAN, 2013 IEEE, pages 173–179. IEEE, 2013.

[8] T. Choudhury, S. Consolvo, B. Harrison, J. Hightower, A. LaMarca, L. LeGrand, A. Rahimi, A. Rea, G. Bordello, B. Hemingway, et al. The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Computing, 7(2), 2008.

[9] S. Das and D. Cook. Designing smart environments: A paradigm based on learning and prediction. Pattern Recognition and Machine Intelligence, pages 80–90, 2005.

[10] A. De Angelis, A. Moschitta, P. Carbone, M. Calderini, S. Neri, R. Borgna, and M. Peppucci. Design and characterization of a portable ultrasonic indoor 3- d positioning system. IEEE Transactions on Instrumentation and Measurement, 64(10):2616–2625, 2015.

[11] C. Debes, A. Merentitis, S. Sukhanov, M. Niessen, N. Frangiadakis, and A. Bauer. Monitoring activities of daily living in smart homes: Understanding human behavior. IEEE Signal Processing Magazine, 33(2):81–94, 2016.

[12] S. Dernbach, B. Das, N. C. Krishnan, B. L. Thomas, and D. J. Cook. Simple and complex activity recognition through smart phones. In Intelligent Environments (IE), 2012 8th International Conference on, pages 214–221. IEEE, 2012.

[13] A. Dixit and A. Naik. Use of prediction algorithms in smart homes. International Journal of Machine Learning and Computing, 4(2):157, 2014.

[14] J. Goto, T. Kidokoro, T. Ogura, and S. Suzuki. Activity recognition system for watching over infant children. In RO-MAN, 2013 IEEE, pages 473–477. IEEE, 2013.

[15] J. P. Gupta, P. Dixit, and V. B. Semwal. Analysis of gait pattern to recognize the human activities. IJIMAI, 2(7):7–16, 2014.

[16] P. Gupta and T. Dallas. Feature selection and activity recognition system us- ing a single triaxial accelerometer. IEEE Transactions on Biomedical Engineering, 61(6):1780–1786, 2014.

[17] E.-T. Ha, T.-K. Kim, D.-K. Ahn, S.-H. Jeong, I.-R. Yoon, and S.-H. Han. A stable control of legged robot based on ultrasonic sensor. In Control, Automation and Systems (ICCAS), 2015 15th International Conference on, pages 1256–1258. IEEE, 2015.

[18] P. M. Hansen, M. Hemmsen, A. Brandt, J. Rasmussen, T. Lange, P. S. Krohn, L. L¨onn, J. A. Jensen, and M. B. Nielsen. Clinical evaluation of synthetic aperture sequential beamforming ultrasound in patients with liver tumors. Ultrasound in medicine & biology, 40(12):2805–2810, 2014.

[19] J. A. Hartigan and M. A. Wong. Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):100–108, 1979.

[20] G. E. Hinton. Training products of experts by minimizing contrastive divergence. Training, 14(8), 2006.

[21] F. Ijaz, H. K. Yang, A. W. Ahmad, and C. Lee. Indoor positioning: A review of indoor ultrasonic positioning systems. In Advanced Communication Technology (ICACT), 2013 15th International Conference on, pages 1146–1150. IEEE, 2013.

[22] A. Jalal and S. Kamal. Real-time life logging via a depth silhouette-based human activity recognition system for smart home services. In Advanced Video and Signal Based Surveillance (AVSS), 2014 11th IEEE International Conference on, pages 74–80. IEEE, 2014.

[23] T. Ji et al. Frequency and velocity of people walking. Structural Engineer, 84(3):36– 40, 2005.

[24] B. Jokanovic, M. Amin, and F. Ahmad. Radar fall motion detection using deep learning. In Radar Conference (RadarConf ), 2016 IEEE, pages 1–6. IEEE, 2016.

[25] J.-G. Juang, Y.-C. Yang, and J.-A. Wang. Exploring an unknown environment using ultrasonic and infrared sensors. Sensors and Materials, 28(9):991–1004, 2016.

[26] Y. Kim, S. Ha, and J. Kwon. Human detection using doppler radar based on physical characteristics of targets. IEEE Geoscience and Remote Sensing Letters, 12(2):289– 293, 2015.

[27] J. Lien, N. Gillian, M. E. Karagozler, P. Amihood, C. Schwesig, E. Olson, H. Raja, and I. Poupyrev. Soli: Ubiquitous gesture sensing with millimeter wave radar. ACM Transactions on Graphics (TOG), 35(4):142, 2016.

[28] U. Maurer, A. Smailagic, D. P. Siewiorek, and M. Deisher. Activity recognition and monitoring using multiple sensors on different body positions. In Wearable and Implantable Body Sensor Networks, 2006. BSN 2006. International Workshop on, pages 4–pp. IEEE, 2006.

[29] M. S. K. Mishra, F. JTMCOE, and K. Bhagat. A survey on human motion detection and surveillance. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume, 4, 2015.

[30] S. C. Mukhopadhyay. Wearable sensors for human activity monitoring: A review. IEEE sensors journal, 15(3):1321–1330, 2015.

[31] Y. Nishida, H. Aizawa, T. Hori, N. H. Hoffman, T. Kanade, and M. Kakikura. 3d ultrasonic tagging system for observing human activity. In Intelligent Robots and Systems, 2003.(IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on, volume 1, pages 785–791. IEEE, 2003.

[32] Y. Nishida, T. Hori, S.-i. Murakami, and H. Mizoguchi. Minimally privacy-violative system for locating human by ultrasonic radar embedded on ceiling. In Systems, Man and Cybernetics, 2004 IEEE International Conference on, volume 2, pages 1549–1554. IEEE, 2004.

[33] D. Sloo, N. Webb, M. L. Rogers, A. M. Fadell, J. Lee, S. Le Guen, and A. W. Goldenson. Smart-home hazard detector providing useful follow up communications to detection events, Mar. 24 2015. US Patent 8,988,232.

[34] X. Su, H. Tong, and P. Ji. Activity recognition with smartphone sensors. Tsinghua Science and Technology, 19(3):235–249, 2014.

[35] Q. Wan, Y. Li, C. Li, and R. Pal. Gesture recognition for smart home applica- tions using portable radar sensors. In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pages 6414–6417. IEEE, 2014.

[36] M. Yamamoto, N. Ajiki, and T. Nakazawa. Ultrasonic diagnosis arrangements for comparing same time phase images of a periodically moving target, Jan. 26 2016. US Patent 9,241,684.

[37] Y. Yan, E. Ricci, G. Liu, and N. Sebe. Egocentric daily activity recognition via multitask clustering. IEEE Transactions on Image Processing, 24(10):2984–2995, 2015.

[38] E. Yavari, H. Jou, V. Lubecke, and O. Boric-Lubecke. Doppler radar sensor for occupancy monitoring. In Silicon Monolithic Integrated Circuits in RF Systems (SiRF), 2013 IEEE 13th Topical Meeting on, pages 216–218. IEEE, 2013.

[39] Y. Zhang, C. W. de Silva, D. Su, and Y. Xue. Autonomous robot navigation with self-learning for collision avoidance with randomly moving obstacles. In Computer Science & Education (ICCSE), 2014 9th International Conference on, pages 117–122. IEEE, 2014.