Advertisement

Deep Learning for Sensor-Based Activity Recognition: Recent Trends

  • Md Atiqur Rahman AhadEmail author
  • Anindya Das Antar
  • Masud Ahmed
Chapter
  • 8 Downloads
Part of the Intelligent Systems Reference Library book series (ISRL, volume 173)

Abstract

The field of human activity recognition (HAR) using different sensor modalities poses numerous challenges to the researchers working in this domain. Though traditional pattern recognition approaches performed well in this regard earlier, the cost of poor generalization and the cost of shallow learning due to the handcrafted features have opened a new door for deep learning in this field. This chapter discusses the importance of deep learning in sensor-based activity recognition explaining the deep models and their use in previous research works. This chapter also represents the importance of transfer learning and active learning in this field, that are new research topics. Finally, this chapter shows the challenges of using deep models along with feasible solutions.

References

  1. 1.
    Antar, A.D., Ahad, M.A.R., Shahid, O.: Vision-based action understanding for assistive healthcare: a short review. In: IEEE CVPR workshop (2019)Google Scholar
  2. 2.
    Ahad, M.A.R.: Vision and sensor based human activity recognition: Challenges ahead (2020)Google Scholar
  3. 3.
    Antar, A.D., Ahmed, M., Ahad, M.A.R.: Challenges in sensor-based human activity recognition and a comparative analysis of benchmark datasets: a review. In: 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 134–139. IEEE (2019)Google Scholar
  4. 4.
    Ahad, M.A.R.: Motion History Images for Action Recognition and Understanding. Springer Science & Business Media, Berlin (2012)zbMATHGoogle Scholar
  5. 5.
    Ahad, M.A.R.: Computer Vision and Action Recognition: A Guide for Image Processing and Computer Vision Community for Action Understanding, vol. 5. Springer Science & Business Media, Berlin (2011)CrossRefGoogle Scholar
  6. 6.
    Hossain, T., Islam, M.S., Ahad, M.A.R., Inoue, S.: Human activity recognition using earable device. In: Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 81–84. ACM (2019)Google Scholar
  7. 7.
    Tazin, T., Hossain, T., Ahad, M.A.R., Inoue, S.: Activity recognition by using lorawan sensor. In: 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2018 International Symposium on Wearable Computers (UbiComp/ISWC) (2018)Google Scholar
  8. 8.
    Ahmed, M., Antar, A.D., Ahad, M.A.R.: An approach to classify human activities in real-time from smartphone sensor data. In: 2019 Joint 8th International Conference on Informatics, Electronics Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision Pattern Recognition (icIVPR), pp. 140–145 (2019)Google Scholar
  9. 9.
    Wang, J., Chen, Y., Hao, S., Peng, X., Lisha, H.: Deep learning for sensor-based activity recognition: a survey. Pattern Recognit. Lett. 119, 3–11 (2019)CrossRefGoogle Scholar
  10. 10.
    Bengio, Y.: Deep learning of representations: looking forward. In: International Conference on Statistical Language and Speech Processing, pp. 1–37. Springer (2013)Google Scholar
  11. 11.
    Yang, J., Nguyen, M.N., San, P.P., Li, X.L., Krishnaswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)Google Scholar
  12. 12.
    Saha, S.S., Rahman, S., Rasna, M.J., Mahfuzul Islam, A.K.M., Ahad, M.A.R.: Du-md: an open-source human action dataset for ubiquitous wearable sensors. In: Joint 7th International Conference on Informatics, Electronics & Vision, 2nd International Conference on Imaging, Vision & Pattern Recognition (2018)Google Scholar
  13. 13.
    Hossain, T., Goto, H., Ahad, M.A.R., Inoue, S.: A study on sensor-based activity recognition having missing data. In: 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 556–561. IEEE (2018)Google Scholar
  14. 14.
    Rasna, M.J., Hossain, T., Inoue, S., Sha, S.S.,  Rahman, S., Ahad, M.A.R.: Supervised and neural classifiers for locomotion analysis. In: 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2018 International Symposium on Wearable Computers (UbiComp/ISWC) (2018)Google Scholar
  15. 15.
    Yang, Q.: Activity recognition: linking low-level sensors to high-level intelligence. In: Twenty-First International Joint Conference on Artificial Intelligence (2009)Google Scholar
  16. 16.
    Zaher, A., Faridee, M., Ramamurthy, S.R., Hossain, H.M., Roy, N.: Happyfeet: recognizing and assessing dance on the floor. In: Proceedings of the 19th International Workshop on Mobile Computing Systems & Applications, pp. 49–54. ACM (2018)Google Scholar
  17. 17.
    Active learning enabled activity recognition: Sajjad Hossain, H.M., Khan, M.A.A.H., Roy, N. Pervasive Mobile Comput. 38, 312–330 (2017)CrossRefGoogle Scholar
  18. 18.
    Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Vepakomma, P., De, D., Das, S.K., Bhansali, S.: A-wristocracy: Deep learning on wrist-worn sensing for recognition of user complex activities. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 1–6. IEEE (2015)Google Scholar
  20. 20.
    Walse, K.H., Dharaskar, R.V., Thakare, V.M.: Pca based optimal ann classifiers for human activity recognition using mobile sensors data. In: Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1, pp. 429–436. Springer (2016)Google Scholar
  21. 21.
    Hammerla, N.Y., Halloran, S., Plötz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables (2016). arXiv:1604.08880
  22. 22.
    Bengio, Y. et al.: Learning deep architectures for ai. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)Google Scholar
  23. 23.
    Deng, L.: A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inf. Process. 3, (2014)Google Scholar
  24. 24.
    Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)Google Scholar
  25. 25.
    Zeng, M., Nguyen, L.T., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P., Zhang, J.: Convolutional neural networks for human activity recognition using mobile sensors. In: 6th International Conference on Mobile Computing, Applications and Services, pp. 197–205. IEEE (2014)Google Scholar
  26. 26.
    Chen, Y., Xue, Y.: A deep learning approach to human activity recognition based on single accelerometer. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1488–1492. IEEE (2015)Google Scholar
  27. 27.
    Pourbabaee, B., Roshtkhari, M.J., Khorasani, K.: Deep convolutional neural networks and learning ecg features for screening paroxysmal atrial fibrillation patients. IEEE Trans. Syst. Man Cybern.: Syst. 99, 1–10 (2017)Google Scholar
  28. 28.
    Sathyanarayana, A., Joty, S., Fernandez-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A., Taheri, S. and Arora, T.: Impact of physical activity on sleep: a deep learning based exploration (2016). arXiv:1607.07034
  29. 29.
    Ha, S., Yun, J-M., Choi, S.: Multi-modal convolutional neural networks for activity recognition. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3017–3022. IEEE (2015)Google Scholar
  30. 30.
    Jiang, W., Yin, Z.: Human activity recognition using wearable sensors by deep convolutional neural networks. In: Proceedings of the 23rd ACM international conference on Multimedia, pp. 1307–1310. ACM (2015)Google Scholar
  31. 31.
    Singh, M.S., Pondenkandath, V., Zhou, B., Lukowicz, P. and Liwickit, M.: Transforming sensor data to the image domain for deep learning—an application to footstep detection. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2665–2672. IEEE (2017)Google Scholar
  32. 32.
    Kim, Y., Toomajian, B.: Hand gesture recognition using micro-doppler signatures with convolutional neural network. IEEE Access 4, 7125–7130 (2016)CrossRefGoogle Scholar
  33. 33.
    Zebin, T., Scully, P.J., Ozanyan, K.B.: Human activity recognition with inertial sensors using a deep learning approach. In: 2016 IEEE SENSORS, pp. 1–3. IEEE (2016)Google Scholar
  34. 34.
    Edel, M., Köppe, E.: Binarized-blstm-rnn based human activity recognition. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–7. IEEE (2016)Google Scholar
  35. 35.
    Guan, Yu.: Plötz, Thomas: Ensembles of deep lstm learners for activity recognition using wearables. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1(2), 11 (2017)CrossRefGoogle Scholar
  36. 36.
    Inoue, M., Inoue, S., Nishida, T.: Deep recurrent neural network for mobile human activity recognition with high throughput. Artif. Life Robot. 23(2), 173–185 (2018)CrossRefGoogle Scholar
  37. 37.
    Zeng, M., Gao, H., Yu, T., Mengshoel, O.J., Langseth, H., Lane, I., Liu, X.: Understanding and improving recurrent networks for human activity recognition by continuous attention. In: Proceedings of the 2018 ACM International Symposium on Wearable Computers, pp. 56–63. ACM (2018)Google Scholar
  38. 38.
    Almaslukh, B., AlMuhtadi, J., Artoli, A.: An effective deep autoencoder approach for online smartphone-based human activity recognition. Int. J. Comput. Sci. Netw. Sec. 17, 160 (2017)Google Scholar
  39. 39.
    Wang, A., Chen, G., Shang, C., Zhang, M., Liu, L.: Human activity recognition in a smart home environment with stacked denoising autoencoders. In: International Conference on Web-Age Information Management, pp. 29–40. Springer (2016)Google Scholar
  40. 40.
    Li, Y., Shi, D., Ding, B., Liu, D.: Unsupervised feature learning for human activity recognition using smartphone sensors. In: Mining intelligence and knowledge exploration, pp. 99–107. Springer (2014)Google Scholar
  41. 41.
    Hammerla, N.Y., Fisher, J., Andras, P., Rochester, L., Walker, R., Plötz, T.: Pd disease state assessment in naturalistic environments using deep learning. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)Google Scholar
  42. 42.
    Lane, N.D., Georgiev, P., Qendro, L.: Deepear: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 283–294. ACM (2015)Google Scholar
  43. 43.
    Plötz, T., Hammerla, N.Y., Olivier, P.L.: Feature learning for activity recognition in ubiquitous computing. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)Google Scholar
  44. 44.
    Radu, V., Lane, N.D., Bhattacharya, S., Mascolo, C., Marina, M.K., Kawsar, F.: Towards multimodal deep learning for activity recognition on mobile devices. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, pp. 185–188. ACM (2016)Google Scholar
  45. 45.
    Li, X., Zhang, Y., Li, M., Marsic, I., Yang, J., Burd, R.S.: Deep neural network for rfid-based activity recognition. In: Proceedings of the Eighth Wireless of the Students, pp. by the Students, and for the Students Workshop, pp. 24–26. ACM (2016)Google Scholar
  46. 46.
    Fang, H., Hu, C.: Recognizing human activity in smart home using deep learning algorithm. In: Proceedings of the 33rd Chinese Control Conference, pp. 4716–4720. IEEE (2014)Google Scholar
  47. 47.
    Zhang, L., Wu, X., Luo, D.: Real-time activity recognition on smartphones using deep neural networks. In: 2015 IEEE 12th Intl Conf on Ubiquitous Intelligence and Computing and 2015 IEEE 12th Intl Conf on Autonomic and Trusted Computing and 2015 IEEE 15th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UIC-ATC-ScalCom), pp. 1236–1242. IEEE (2015)Google Scholar
  48. 48.
    Yao, S., Hu, S., Zhao, Y., Zhang, A., Abdelzaher, T.: Deepsense: a unified deep learning framework for time-series mobile sensing data processing. In: Proceedings of the 26th International Conference on World Wide Web, pp. 351–360. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  49. 49.
    Giallanza, T., Siems, T., Smith, E., Gabrielsen, E., Johnson, I., Thornton, M.A., Larson, E.C.: Keyboard snooping from mobile phone arrays with mixed convolutional and recurrent neural networks. Proc. ACM Interact. Mobile Wearab. Ubiquit. Technol. 3(2), 45 (2019)Google Scholar
  50. 50.
    Ordóñez, F., Roggen, D.: Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)CrossRefGoogle Scholar
  51. 51.
    Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Front. Comput. Sci. 10(1), 96–112 (2016)CrossRefGoogle Scholar
  52. 52.
    Liu, C., Zhang, L., Liu, Z., Liu, K., Li, X., Liu, Y.: Lasagna: towards deep hierarchical understanding and searching over mobile sensing data. In: Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, pp. 334–347. ACM (2016)Google Scholar
  53. 53.
    Wu, Z., Sivadas, S., Tan, Y.K., Bin, M., Goh, R.S.M.: Multi-modal hybrid deep neural network for speech enhancement. arXiv preprintarXiv:1606.04750 (2016)
  54. 54.
    Zheng, Y.,  Liu, Q., Chen, E., Ge, Y., Zhao, J.L.: Time series classification using multi-channels deep convolutional neural networks. In: International Conference on Web-Age Information Management, pp. 298–310. Springer (2014)Google Scholar
  55. 55.
    Roggen, D., Forster, K., Calatroni, A., Holleczek, T.,  Fang, Y., Troster, G., Ferscha, A., Holzmann, C., Riener, A., Lukowicz, P. et al.: Opportunity: Towards opportunistic activity and context recognition systems. In: 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops, pp. 1–6. IEEE (2009)Google Scholar
  56. 56.
    Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., Tröster, G.: Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. In: European Conference on Wireless Sensor Networks, pp. 17–33. Springer (2008)Google Scholar
  57. 57.
    Ronao, C.A., Cho, S.-B.: Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst. Appl. 59, 235–244 (2016)CrossRefGoogle Scholar
  58. 58.
    Ravi, D., Wong, C., Lo, B., Yang, G.-Z.: A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J. Biomed. Health Inf. 21(1), 56–64 (2016)CrossRefGoogle Scholar
  59. 59.
    Alsheikh, M.A., Selim, A., Niyato, D., Doyle, L., Lin, S., Tan, H.P.: Deep activity recognition models with triaxial accelerometers. In: Workshops at the Thirtieth AAAI Conference on Artificial Intelligence (2016)Google Scholar
  60. 60.
    Bhattacharya, S., Lane, N.D.: From smart to deep: Robust activity recognition on smartwatches using deep learning. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 1–6. IEEE (2016)Google Scholar
  61. 61.
    Panwar, M., Dyuthi, S.R., Prakash, K.C., Biswas, D., Acharyya, A., Maharatna, K., Gautam, A., Naik, G.R.: Cnn based approach for activity recognition using a wrist-worn accelerometer. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2438–2441. IEEE (2017)Google Scholar
  62. 62.
    Sani, S., Wiratunga, N., Massie, S.: Learning deep features for knn-based human activity recognition (2017)Google Scholar
  63. 63.
    San, P.P., Kakar, P., Li, X.L., Krishnaswamy, S., Yang, J.B., Nguyen, M.N.: Deep learning for human activity recognition. In: Big Data Analytics for Sensor-Network Collected Intelligence, pp. 186–204. Elsevier (2017)Google Scholar
  64. 64.
    Chen, Y., Zhong, K.,  Zhang, J., Sun, Q., Zhao, X.: Lstm networks for mobile human activity recognition. In: 2016 International Conference on Artificial Intelligence: Technologies and Applications. Atlantis Press (2016)Google Scholar
  65. 65.
    Cheng, W.Y., Scotland, A., Lipsmeier, F., Kilchenmann, T., Jin, L., Schjodt-Eriksen, J., Wolf, D., Zhang-Schaerer, Y.P., Garcia, I.F., Siebourg-Polster, J. et al.: Human activity recognition from sensor-based large-scale continuous monitoring of parkinson’s disease patients. In: Proceedings of the Second IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, pp. 249–250. IEEE Press (2017)Google Scholar
  66. 66.
    Gjoreski, H., Bizjak, J., Gjoreski, M., Gams, M.: Comparing deep and classical machine learning methods for human activity recognition using wrist accelerometer. In: Proceedings of the IJCAI 2016 Workshop on Deep Learning for Artificial Intelligence, New York, NY, USA, vol. 10 (2016)Google Scholar
  67. 67.
    Ha, S., Choi, S.: Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 381–388. IEEE (2016)Google Scholar
  68. 68.
    Hannink, J., Kautz, T., Pasluosta, C.F., Gaßmann, K.-G., Klucken, J., Eskofier, B.M.: Sensor-based gait parameter extraction with deep convolutional neural networks. IEEE J. Biomed. Health Inf. 21(1), 85–93 (2016)CrossRefGoogle Scholar
  69. 69.
    Hayashi, T., Nishida, M., Kitaoka, N., Takeda, K.: Daily activity recognition based on dnn using environmental sound and acceleration signals. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2306–2310. IEEE (2015)Google Scholar
  70. 70.
    Khan, U.M., Kabir, Z., Hassan, S.A., Ahmed, S.H.: A deep learning framework using passive wifi sensing for respiration monitoring. In: GLOBECOM 2017-2017 IEEE Global Communications Conference, pp. 1–6. IEEE (2017)Google Scholar
  71. 71.
    Kim, Y., Li, Y.: Human activity classification with transmission and reflection coefficients of on-body antennas through deep convolutional neural networks. IEEE Trans. Antennas Propag. 65(5), 2764–2768 (2017)CrossRefGoogle Scholar
  72. 72.
    Lane, N.D., Georgiev, P.: Can deep learning revolutionize mobile sensing? In: Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, pp. 117–122. ACM (2015)Google Scholar
  73. 73.
    Lee, S-M., Yoon, S.M., Cho, H.: Human activity recognition from accelerometer data using convolutional neural network. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 131–134. IEEE (2017)Google Scholar
  74. 74.
    Mohammed, S., Tashev, I.: Unsupervised deep representation learning to remove motion artifacts in free-mode body sensor networks. In: 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), pp. 183–188. IEEE (2017)Google Scholar
  75. 75.
    Morales, F.J.O., Roggen, D.: Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations. In: Proceedings of the 2016 ACM International Symposium on Wearable Computers, pp. 92–99. ACM (2016)Google Scholar
  76. 76.
    Murad, A.: Pyun, Jae-Young: Deep recurrent neural networks for human activity recognition. Sensors 17(11), 2556 (2017)CrossRefGoogle Scholar
  77. 77.
    Ronao, C.A., Cho, S-B.: Deep convolutional neural networks for human activity recognition with smartphone sensors. In: International Conference on Neural Information Processing, pp. 46–53. Springer (2015)Google Scholar
  78. 78.
    Wang, J., Zhang, X., Gao, Q., Yue, H., Wang, H.: Device-free wireless localization and activity recognition: A deep learning approach. IEEE Trans. Veh. Technol. 66(7), 6258–6267 (2016)CrossRefGoogle Scholar
  79. 79.
    Abdel-Hamid, O., Mohamed, A.R., Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 22(10), 1533–1545 (2014)CrossRefGoogle Scholar
  80. 80.
    Zhang, L., Wu, X., Luo, D.: Human activity recognition with hmm-dnn model. In: 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), pp. 192–197. IEEE (2015)Google Scholar
  81. 81.
    Zhang, L., Wu, X., Luo, D.: Recognizing human activities from raw accelerometer data using deep neural networks. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 865–870. IEEE (2015)Google Scholar
  82. 82.
    Zhang, Y., Li, X., Zhang, J., Chen, S., Zhou, M., Farneth, R.A., Marsic, I., Burd, R.S.: Car-a deep learning structure for concurrent activity recognition. In: 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pp. 299–300. IEEE (2017)Google Scholar
  83. 83.
    Zhang, S., Ng, W.W.Y., Zhang, J., Nugent, C.D.: Human activity recognition using radial basis function neural network trained via a minimization of localized generalization error. In: International Conference on Ubiquitous Computing and Ambient Intelligence, pp. 498–507. Springer (2017)Google Scholar
  84. 84.
    Hassan, M.M., Uddin, M.Z., Mohamed, A., Almogren, A.: A robust human activity recognition system using smartphone sensors and deep learning. Future Gener. Comput. Syst. 81, 307–313 (2018)CrossRefGoogle Scholar
  85. 85.
    Reyes-Ortiz, J.-L., Oneto, L., Samà, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016)CrossRefGoogle Scholar
  86. 86.
    Avilés-Cruz, C., Ferreyra-Ramírez, A., Zúñiga-López, A., Villegas-Cortéz, J.: Coarse-fine convolutional deep-learning strategy for human activity recognition. Sensors 19(7), 1556 (2019)CrossRefGoogle Scholar
  87. 87.
    Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: Esann (2013)Google Scholar
  88. 88.
    Antar, A.D., Ahmed, M., Ishrak, M.S., Ahad, M.A.R.: A comparative approach to classification of locomotion and transportation modes using smartphone sensor data. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, pp. 1497–1502 (2018)Google Scholar
  89. 89.
    Gjoreski, H., Ciliberto, M., Wang, L., Morales, F.J.O., Mekki, S., Valentin, S., Roggen, D.: The university of sussex-huawei locomotion and transportation dataset for multimodal analytics with mobile devices. IEEE Access 6, 42592–42604 (2018)CrossRefGoogle Scholar
  90. 90.
    Saha, S.S., Rahman, S., Haque, Z.R.R., Hossain, T., Inoue, S., Ahad, M.A.R.: Position independent activity recognition using shallow neural architecture and empirical modeling. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 808–813 (2019)Google Scholar
  91. 91.
    Antar, A.D., Ahmed, M., Hossain, T., Muramatsu, D., Makihara, Y., Inoue, S., Yagi, Y., Ahad, M.A.R., Ngo, T.T.: Wearable sensor-based gait analysis for age and gender estimation (2020)Google Scholar
  92. 92.
    Ngo, T.T., Ahad, M.A.R., Antar, A.D., Ahmed, M., Muramatsu, D., Makihara, Y., Yagi, Y., Inoue, S., Hossain, T. and Hattori, Y.: Ou-isir wearable sensor-based gait challenge: age and gender. In: Proceedings of the 12th IAPR International Conference on Biometrics, ICB (2019)Google Scholar
  93. 93.
    Ngo, T.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., Yagi, Y.: The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recognit. 47(1), 228–237 (2014)CrossRefGoogle Scholar
  94. 94.
    Cook, D., Feuz, K.D., Krishnan, N.C.: Transfer learning for activity recognition: a survey. Knowl. Inf. Syst. 36(3), 537–556 (2013)CrossRefGoogle Scholar
  95. 95.
    Byrnes, J.P.: Cognitive Development and Learning in Instructional Contexts. Allyn and Bacon Boston (1996)Google Scholar
  96. 96.
    Khan, M.A.A.H., Roy, N.: Transact: transfer learning enabled activity recognition. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 545–550. IEEE (2017)Google Scholar
  97. 97.
    Barshan, B., Yüksek, M.C.: Recognizing daily and sports activities in two open source machine learning environments using body-worn sensor units. Comput. J. 57(11), 1649–1667 (2014)CrossRefGoogle Scholar
  98. 98.
    Banos, O., Garcia, R., Holgado-Terriza, J.A., Damas, M., Pomares, H., Rojas, I., Saez, A., Villalonga, C.: Mhealthdroid: a novel framework for agile development of mobile health applications. In: International Workshop on Ambient Assisted Living, pp. 91–98. Springer (2014)Google Scholar
  99. 99.
    Deng, W.-Y., Zheng, Q.-H., Wang, Z.-M.: Cross-person activity recognition using reduced kernel extreme learning machine. Neural Netw. 53, 1–7 (2014)CrossRefGoogle Scholar
  100. 100.
    Wang, J., Chen, Y., Hu, L., Peng, X., Philip, S.Y.: Stratified transfer learning for cross-domain activity recognition. In: 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 1–10. IEEE (2018)Google Scholar
  101. 101.
    Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International Symposium on Wearable Computers, pp. 108–109. IEEE (2012)Google Scholar
  102. 102.
    Diethe, T., Twomey, N., Flach, P.A.: Active transfer learning for activity recognition. In: ESANN (2016)Google Scholar
  103. 103.
    Zhang, M., Sawchuk, A.A.: Usc-had: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 1036–1043. ACM (2012)Google Scholar
  104. 104.
    Ying, J.J-C., Lin, B-H., Tseng, V.S., Hsieh, S-Y.: Transfer learning on high variety domains for activity recognition. In: Proceedings of the ASE BigData & Social Informatics 2015, p. 37. ACM (2015)Google Scholar
  105. 105.
    Altun, K., Barshan, B., Tunçel, O.: Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recognit. 43(10), 3605–3620 (2010)zbMATHCrossRefGoogle Scholar
  106. 106.
    Rokni, S.A., Ghasemzadeh, H.: Synchronous dynamic view learning: a framework for autonomous training of activity recognition models using wearable sensors. In: Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 79–90. ACM (2017)Google Scholar
  107. 107.
    Alemdar, H., van Kasteren, T.L.M., Ersoy, C.: Active learning with uncertainty sampling for large scale activity recognition in smart homes. J. Ambient Intell. Smart Environ. 9(2), 209–223 (2017)CrossRefGoogle Scholar
  108. 108.
    Bannach, D., Jänicke, M., Rey, V.F., Tomforde, S., Sick, B., Lukowicz, B.: Self-adaptation of activity recognition systems to new sensors (2017). arXiv:1701.08528
  109. 109.
    Ogris, G., Stiefmeier, T., Junker, H., Lukowicz, P., Troster, G.: Using ultrasonic hand tracking to augment motion analysis based recognition of manipulative gestures. In: Ninth IEEE International Symposium on Wearable Computers (ISWC’05), pp. 152–159. IEEE (2005)Google Scholar
  110. 110.
    Stiefmeier, T., Roggen, D., Ogris, G., Lukowicz, P., Tröster, G.: Wearable activity tracking in car manufacturing. IEEE Pervasive Comput. 2, 42–50 (2008)CrossRefGoogle Scholar
  111. 111.
    Do, T.M., Loke, S.W., Liu, F.: Healthylife: an activity recognition system with smartphone using logic-based stream reasoning. In: International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services, pp. 188–199. Springer (2012)Google Scholar
  112. 112.
    Bagaveyev, S., Cook, D.J.: Designing and evaluating active learning methods for activity recognition. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pp. 469–478. ACM (2014)Google Scholar
  113. 113.
    Prelec, D., Seung, H.S., McCoy, J.: A solution to the single-question crowd wisdom problem. Nature 541(7638), 532 (2017)CrossRefGoogle Scholar
  114. 114.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)CrossRefGoogle Scholar
  115. 115.
    Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 1129–1134. IEEE (2017)Google Scholar
  116. 116.
    Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: International Symposium on Handheld and Ubiquitous Computing, pp. 304–307. Springer (1999)Google Scholar
  117. 117.
    Stewart, R., Ermon, S.: Label-free supervision of neural networks with physics and domain knowledge. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Md Atiqur Rahman Ahad
    • 1
    • 2
    Email author
  • Anindya Das Antar
    • 3
  • Masud Ahmed
    • 4
  1. 1.Osaka UniversityOsakaJapan
  2. 2.University of DhakaDhakaBangladesh
  3. 3.University of MichiganAnn ArborUSA
  4. 4.University of Maryland Baltimore CountyMarylandUSA

Personalised recommendations