A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices

  • Ivan Miguel Pires
  • Gonçalo MarquesEmail author
  • Nuno M. Garcia
  • Nuno Pombo
  • Francisco Flórez-Revuelta
  • Eftim Zdravevski
  • Susanna Spinsante
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1132)


Smart environments and mobile devices are two technologies that when combined may allow the recognition of Activities of Daily Living (ADL) and its environments. This paper focuses on the literature review of the existing machine learning methods for the recognition of ADL and its environments, by means of comparison jointly with a proposal of a novel taxonomy in this context. The sensors used for this purpose depends on the nature of the system and the ADL to recognize. The available in the mobile devices are mainly motion, magnetic and location sensors, but the sensors available in the smart environments may have different types. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment.


Activities of Daily Living Mobile devices Pattern recognition Sensors Methods Review 



This work is funded by FCT/MCTES through national funds and when applicable co-funded EU funds under the project UIDB/EEA/50008/2020 (Este trabalho é financiado pela FCT/MCTES através de fundos nacionais e quando aplicável cofinanciado por fundos comunitários no âmbito do projeto UIDB/EEA/50008/2020). This article/publication is based on work from COST Action IC1303 - AAPELE - Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226 - SHELD-ON - Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information in


  1. Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. 43(3), 1–43 (2011)CrossRefGoogle Scholar
  2. Alam, M.A.U., Roy, N.: GeSmart: a gestural activity recognition model for predicting behavioral health. In: 2014 International Conference on Smart Computing (SMARTCOMP) (2014)Google Scholar
  3. Allen, Y.Y., et al.: Distributed recognition of human actions using wearable motion sensor networks. J. Ambient Intell. Smart Environ. 1(2), 103–115 (2009). %@ 1876-1364CrossRefGoogle Scholar
  4. Anguita, D., et al.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: International Workshop of Ambient Assited Living (IWAAL 2012), Vitoria-Gasteiz, Spain (2012)Google Scholar
  5. Anguita, D., et al.: A public domain dataset for human activity recognition using smartphones. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (2013)Google Scholar
  6. Awan, M.A., et al.: A dynamic approach to recognize activities in WSN. Int. J. Distrib. Sens. Netw. 2013, 1–9 (2013)CrossRefGoogle Scholar
  7. Banos, O., et al.: Daily living activity recognition based on statistical feature quality group selection. Expert Syst. Appl. 39(9), 8013–8021 (2012)CrossRefGoogle Scholar
  8. Bao, L., Intille, S.S.: Activity Recognition from user-annotated acceleration data. In: Pervasive Computing, vol. 3001, pp. 1–17. Springer Heidelberg (2004)Google Scholar
  9. Bieber, G., et al.: The hearing trousers pocket – activity recognition by alternative sensors. In: PETRA. ACM (2011)Google Scholar
  10. Botia, J.A., et al.: Ambient assisted living system for in-home monitoring of healthy independent elders. Expert Syst. Appl. 39(9), 8136–8148 (2012)CrossRefGoogle Scholar
  11. Büber, E., Guvensan, A.M.: Discriminative time-domain features for activity recognition on a mobile phone. In: 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) (2014)Google Scholar
  12. Buettner, M., et al.: Recognizing daily activities with RFID-based sensors. In: Ubicomp 2009 Proceedings of the 11th International Conference on Ubiquitous Computing. ACM, New York (2009)Google Scholar
  13. Bujari, A., et al.: Movement pattern recognition through smartphone’s accelerometer. In: 2012 IEEE Consumer Communications and Networking Conference (CCNC). IEEE, Las Vegas (2012)Google Scholar
  14. Chen, Y., Shen, C.: Performance analysis of smartphone-sensor behavior for human activity recognition. IEEE Access 5, 3095–3110 (2017)CrossRefGoogle Scholar
  15. Cheng, B.-C., et al.: HMM machine learning and inference for Activities of daily living recognition. J. Supercomput. 54(1), 29–42 (2009)CrossRefGoogle Scholar
  16. Chernbumroong, S., et al.: Activity classification using a single wrist-worn accelerometer. In: 2011 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA). IEEE (2011)Google Scholar
  17. Chernbumroong, S., et al.: Elderly activities recognition and classification for applications in assisted living. Expert Syst. Appl. 40(5), 1662–1674 (2013)CrossRefGoogle Scholar
  18. Chetty, G., White, M.: Body sensor networks for human activity recognition. In: 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN) (2016)Google Scholar
  19. Chiang, J.-H., et al.: Pattern analysis in daily physical activity data for personal health management. Pervasive Mob. Comput. 13, 13–25 (2013)CrossRefGoogle Scholar
  20. Chikhaoui, B., et al.: A Frequent pattern mining approach for ADLs recognition in smart environments. In: 2011 IEEE International Conference on Advanced Information Networking and Applications (AINA). IEEE, Biopolis (2011)Google Scholar
  21. Costa, J., et al.: A mobile application to improve the quality of life via exercise. In: 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP) (2016)Google Scholar
  22. Cruz-Silva, N., et al.: Features Selection for Human Activity Recognition with iPhone Inertial Sensors. In: 16th Portuguese Conference on Artificial Inteligence. Advances in Artificial Intelligence. APPIA, Angra do Heroísmo (2013)Google Scholar
  23. Danny, W., et al.: Unsupervised activity recognition using automatically mined common sense. In: Proceedings of the 20th National Conference on Artificial Intelligence – vol. 1, pp. 21–27. AAAI Press, Pittsburgh (2005). %@ 1-57735-236-xGoogle Scholar
  24. Das, S., et al.: Detecting user activities using the accelerometer on Android smartphones (2010)Google Scholar
  25. Dernbach, S., et al.: Simple and Complex activity recognition through smart phones. In: 2012 8th International Conference on Intelligent Environments (IE). IEEE, Guanajuato (2012)Google Scholar
  26. Dobre, C., et al.: Ambient Assisted Living and Enhanced Living Environments: Principles, Technologies and Control. Butterworth-Heinemann, Oxford (2016)Google Scholar
  27. Ermes, M., et al.: Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. Trans. Info. Tech. Biomed. 12(1), 20–26 (2008)CrossRefGoogle Scholar
  28. Eskaf, K., et al.: Aggregated activity recognition using smart devices. In: 2016 3rd International Conference on Soft Computing and Machine Intelligence (ISCMI) (2016)Google Scholar
  29. Fitz-Walter, Z., Tjondronegoro, D.: Simple classification of walking activities using commodity smart phones. In: OZCHI 2009 Proceedings of the 21st Annual Conference of the Australian Computer-Human Interaction Special Interest Group: Design: Open 24/7. ACM, New York (2009)Google Scholar
  30. Fortino, G., et al.: Activity-aaService: cloud-assisted, BSN-based system for physical activity monitoring. In: 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (2015)Google Scholar
  31. Foti, D., Koketsu, J.S.: Activities of daily living. Pedretti’s Occup. Ther.: Pract. Skills Phys. Dysfunct. 7, 157–232 (2013)Google Scholar
  32. Fulk, G.D., et al.: Identifying activity levels and steps of people with stroke using a novel shoe-based sensor. J. Neurol. Phys. Ther. 36(2), 100–107 (2012)CrossRefGoogle Scholar
  33. Gafurov, D., et al.: Gait authentication and identification using wearable accelerometer sensor. In: 2007 IEEE Workshop on Alghero Automatic Identification Advanced Technologies. IEEE (2007)Google Scholar
  34. Ganti, R.K., et al.: Multisensor fusion in smartphones for lifestyle monitoring. In: 2010 International Conference on Body Sensor Networks (2010)Google Scholar
  35. Garcia, N.M.: A roadmap to the design of a personal digital life coach. In: ICT Innovations 2015. Springer (2016)Google Scholar
  36. Garcia, N.M., Rodrigues, J.J.P.: Ambient Assisted Living. CRC Press, Boca Raton (2015)CrossRefGoogle Scholar
  37. Gyllensten, I.C., Bonomi, A.G.: Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life. IEEE Trans. Biomed. Eng. 58(9), 2656–2663 (2011)CrossRefGoogle Scholar
  38. He, Z., Bai, X.: A wearable wireless body area network for human activity recognition. In: 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN) (2014)Google Scholar
  39. Hong, J.-H., et al.: An activity recognition system for ambient assisted living environments. In: Evaluating AAL Systems Through Competitive Benchmarking, vol. 362, pp. 148–158. Springer, Heidelberg (2013)Google Scholar
  40. Hong, Y.-J., et al.: Activity recognition using wearable sensors for elder care. In: Second International Conference on Future Generation Communication and Networking, FGCN 2008. IEEE, Hainan Island (2008)Google Scholar
  41. Hoque, E., Stankovic, J.: AALO: activity recognition in smart homes using active learning in the presence of Overlapped activities. In: 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) (2012)Google Scholar
  42. Hsu, H.H., et al.: Two-phase activity recognition with smartphone sensors. In: 2015 18th International Conference on Network-Based Information Systems (2015)Google Scholar
  43. Huynh, D.T.G.: Human activity recognition with wearable sensors. Fachbereich Informatik, Darmstadt. Technische Universitat Darmstadt. Doktor-Ingenieur (Dr.-Ing.) (2008)Google Scholar
  44. Ivascu, T., et al.: Activities of daily living and falls recognition and classification from the wearable sensors data. In: 2017 E-Health and Bioengineering Conference (EHB) (2017)Google Scholar
  45. Jie, Y., et al.: Wearable accelerometer based extendable activity recognition system. In: 2010 IEEE International Conference on Robotics and Automation (ICRA). IEEE, Anchorage (2010)Google Scholar
  46. Kaghyan, S., Sarukhanyan, H.: Activity recognition using K-nearest neighbor algorithm on smartphone with tri-axial accelerometer. In: International Journal of Informatics Models and Analysis (IJIMA), 146–156. ITHEA International Scientific Society, Bulgaria (2012)Google Scholar
  47. Kasteren, T.V., Krose, B.: Bayesian activity recognition in residence for elders. In: 3rd IET International Conference on Intelligent Environments, IE (2007)Google Scholar
  48. Kazushige, O., Miwako, D.: Indoor-outdoor activity recognition by a smartphone. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 537–537. ACM, Pittsburgh (2012). %@ 978-1-4503-1224-0Google Scholar
  49. Kelly, D., Caulfield, B.: An investigation into non-invasive physical activity recognition using smartphones. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2012)Google Scholar
  50. Khalifa, S., et al.: HARKE: Human activity recognition from kinetic energy harvesting data in wearable devices. IEEE Trans. Mob. Comput. PP(99), 1 (2017)Google Scholar
  51. Khan, A.M., et al.: A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. IEEE Trans. Inf. Technol. Biomed. 14(5), 1166–1172 (2010)CrossRefGoogle Scholar
  52. Kilinc, O., et al.: Inertia based recognition of daily activities with ANNs and spectrotemporal features. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) (2015)Google Scholar
  53. Kim, K.H., Cho, S.B.: A dining context-aware system with mobile and wearable devices. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (2015)Google Scholar
  54. Kmiecik, L.S.: Cloud centered, smartphone based long-term human activity recognition solution (2013)Google Scholar
  55. Kuspa, K., Pratkanis, T.: Classification of mobile device accelerometer data for unique activity identification (2013)Google Scholar
  56. Kwapisz, J.R., et al.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newsl. 12(2), 74 (2011)CrossRefGoogle Scholar
  57. Lara, Ó.D., et al.: Centinela: a human activity recognition system based on acceleration and vital sign data. Pervasive Mob. Comput. 8(5), 717–729 (2012)CrossRefGoogle Scholar
  58. Lara, S.D., Labrador, M.A.: A mobile platform for real-time human activity recognition. In: CCNC IEEE Consumer Communications and Networking Conference, pp. 667–671 (2012)Google Scholar
  59. Lau, S.L., David, K.: Movement recognition using the accelerometer in smartphones. In: 2010 Future Network and Mobile Summit (2010)Google Scholar
  60. Libal, V., et al.: Multimodal classification of activities of daily living inside smart homes. In: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living, vol. 5518, pp. 687–694. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  61. Liming, C., et al.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 790–808 (2012)CrossRefGoogle Scholar
  62. Lorenzi, P., et al.: Mobile devices for the real-time detection of specific human motion disorders. IEEE Sens. J. 16(23), 8220–8227 (2016)Google Scholar
  63. Maekawa, T., et al.: Activity recognition with hand-worn magnetic sensors. Pers. Ubiquit. Comput. 17(6), 1085–1094 (2012)CrossRefGoogle Scholar
  64. Mashita, T., et al.: A content search system for mobile devices based on user context recognition. In: 2012 IEEE Virtual Reality Workshops (VRW) (2012)Google Scholar
  65. Maurer, U., et al.: Activity recognition and monitoring using multiple sensors on different body positions. In: International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2006. IEEE, Cambridge (2006)Google Scholar
  66. Naeem, U., Bigham, J.: A comparison of two hidden markov approaches to task identification in the home environment. In: 2nd International Conference on Pervasive Computing and Applications, ICPCA 2007, pp. 383–388. IEEE, Birmingham (2007)Google Scholar
  67. Nam, Y., et al.: Physical activity recognition using multiple sensors embedded in a wearable device. ACM Trans. Embed. Comput. Syst. 12(2), 1–14 (2013)CrossRefGoogle Scholar
  68. Nishida, M., et al.: Development and preliminary analysis of sensor signal database of continuous daily living activity over the long term. In: 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) (2014)Google Scholar
  69. Nurwanto, F., et al.: Light sport exercise detection based on smartwatch and smartphone using k-Nearest neighbor and dynamic time warping algorithm. In: 2016 8th International Conference on Information Technology and Electrical Engineering (ICITEE) (2016)Google Scholar
  70. Okour, S., et al.: An adaptive rule-based approach to classifying activities of daily living. In: 2015 International Conference on Healthcare Informatics (2015)Google Scholar
  71. Ordonez, F.J., et al.: Activity recognition using hybrid generative/discriminative models on home environments using binary sensors. Sens. (Basel) 13(5), 5460–5477 (2013)CrossRefGoogle Scholar
  72. Phithakkitnukoon, S., et al.: Activity-aware map: identifying human daily activity pattern using mobile phone data. In: Human Behavior Understanding, vol. 6219, pp. 14–25. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  73. Pires, I., et al.: From data acquisition to data fusion: a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices. Sensors 16(2), 184 (2016a)CrossRefGoogle Scholar
  74. Pires, I.M., et al.: Multi-sensor data fusion techniques for the identification of activities of daily living using mobile devices. In: Proceedings of the ECMLPKDD 2015 Doctoral Consortium, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal (2015)Google Scholar
  75. Pires, I.M., et al.: Identification of activities of daily living using sensors available in off-the-shelf mobile devices: research and hypothesis. In: Ambient Intelligence-Software and Applications–7th International Symposium on Ambient Intelligence (ISAmI 2016). Springer, Cham (2016b)CrossRefGoogle Scholar
  76. Pires, I.M., et al.: Limitations of the use of mobile devices and smart environments for the monitoring of ageing people. In: ICT4AWE 2018 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health, Madeira, Portugal (2018a)Google Scholar
  77. Pires, I.M., et al.: Validation techniques for sensor data in mobile health applications. J. Sens. 2016, 1687–1725 (2016c)Google Scholar
  78. Pires, I.M., et al.: Approach for the development of a framework for the identification of activities of daily living using sensors in mobile devices. Sensors (Basel) 18(2), 640 (2018b)CrossRefGoogle Scholar
  79. Pires, I.M., et al.: Identification of activities of daily living through data fusion on motion and magnetic sensors embedded on mobile devices. Pervasive Mob. Comput. 47, 78–93 (2018c)CrossRefGoogle Scholar
  80. Pires, I.M., et al.: Recognition of activities of daily living based on environmental analyses using audio fingerprinting techniques: a systematic review. Sensors (Basel) 18(1), 160 (2018a)CrossRefGoogle Scholar
  81. Pires, I.M., et al.: Android library for recognition of activities of daily living: implementation considerations, challenges, and solutions. Open Bioinf. J. 11(1), 61–88 (2018b)MathSciNetCrossRefGoogle Scholar
  82. Pombo, N., et al.: Classification techniques on computerized systems to predict and/or to detect apnea: a systematic review. Comput. Methods Programs Biomed. 140, 265–274 (2017)CrossRefGoogle Scholar
  83. Prabowo, O.M., et al.: Missing data handling using machine learning for human activity recognition on mobile device. In: 2016 International Conference on ICT For Smart Society (ICISS) (2016)Google Scholar
  84. Ramanan, D.: Detecting activities of daily living in first-person camera views. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2847–2854. IEEE Computer Society (2012)Google Scholar
  85. Rasheed, M. B., et al.: Evaluation of human activity recognition and fall detection using android phone. In: 2015 IEEE 29th International Conference on Advanced Information Networking and Applications (2015)Google Scholar
  86. Ravì, D., et al.: Real-time food intake classification and energy expenditure estimation on a mobile device. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (2015)Google Scholar
  87. Roy, N., et al.: Infrastructure-assisted smartphone-based ADL recognition in multi-inhabitant smart environments. In: 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2013)Google Scholar
  88. Salazar, L.H.A., et al.: A systematic literature review on usability heuristics for mobile phones. Int. J. Mob. Hum. Comput. Interact. 5(2), 50–61 (2013)CrossRefGoogle Scholar
  89. Saponas, T., et al.: ilearn on the iphone: real-time human activity classification on commodity mobile phones. University of Washington CSE Tech Report UW-CSE-08-04-02 (2008)Google Scholar
  90. Shen, B., et al.: Motion intent recognition for control of a lower extremity assistive device (LEAD). In: 2013 IEEE International Conference on Mechatronics and Automation (2013)Google Scholar
  91. Shen, C., et al.: On motion-sensor behavior analysis for human-activity recognition via smartphones. In: 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA) (2016)Google Scholar
  92. Shoaib, M.: Human activity recognition using heterogeneous sensors. In: Adjunct Publication of the 2013 ACM Conference on Ubiquitous Computing, UbiComp 2013 Adjunct. ACM, Zurich (2013)Google Scholar
  93. Siirtola, P., Röning, J.: Recognizing human activities user-independently on smartphones based on accelerometer data. Int. J. Interact. Multimed. Artif. Intell. 1(5), 38 (2012)Google Scholar
  94. Silva, J.R.C.D.: Smartphone based human activity prediction. Faculdade de engenharia, Universidade do Porto, Master in Bioengineering, Porto (2013)Google Scholar
  95. Stikic, M., et al.: ADL recognition based on the combination of RFID and accelerometer sensing. In: 2008 Second International Conference on Pervasive Computing Technologies for Healthcare (2008)Google Scholar
  96. Suryadevara, N.K., et al.: Intelligent sensing systems for measuring wellness indices of the daily activities for the elderly. In: 2012 8th International Conference on Intelligent Environments (IE) (2012)Google Scholar
  97. Szewcyzk, S., et al.: Annotating smart environment sensor data for activity learning. Technol. Health Care 17(3), 161–169 (2009)CrossRefGoogle Scholar
  98. Tolstikov, A., et al.: Eating activity primitives detection - a step towards ADL recognition. In: 10th International Conference on e-health Networking, Applications and Services, HealthCom (2008)Google Scholar
  99. Tsai, P.Y., et al.: Gesture-aware fall detection system: design and implementation. In: 2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin) (2015)Google Scholar
  100. Ueda, K., et al.: A method for recognizing living activities in homes using positioning sensor and power meters. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) (2015)Google Scholar
  101. Urwyler, P., et al.: Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers. Biomed. Eng. Online 14, 54 (2015)CrossRefGoogle Scholar
  102. Vacher, M., et al.: Complete sound and speech recognition system for health smart homes: application to the recognition of activities of daily living (2010)Google Scholar
  103. Vallabh, P., et al.: Fall detection using machine learning algorithms. In: 2016 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (2016)Google Scholar
  104. Varkey, J.P., et al.: Human motion recognition using a wireless sensor-based wearable system. Pers. Ubiquit. Comput. 16(7), 897–910 (2011)CrossRefGoogle Scholar
  105. Vilarinho, T., et al.: A combined smartphone and smartwatch fall detection system. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (2015)Google Scholar
  106. Wang, J., et al.: Generative models for automatic recognition of human daily activities from a single triaxial accelerometer. In: The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE, Brisbane (2012)Google Scholar
  107. Zdravevski, E., et al.: Improving activity recognition accuracy in ambient-assisted living systems by automated feature engineering. IEEE Access 5, 5262–5280 (2017)CrossRefGoogle Scholar
  108. Zhan, K., et al.: Multi-scale conditional random fields for first-person activity recognition. In: 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom) (2014)Google Scholar
  109. Zhang, M., Sawchuk, A.A.: Human daily activity recognition with sparse representation using wearable sensors. IEEE J. Biomed. Health Inf. 17(3), 553–560 (2013)CrossRefGoogle Scholar
  110. Zhang, S., et al.: Detection of activities by wireless sensors for daily life surveillance: eating and drinking. Sensors (Basel) 9(3), 1499–1517 (2009)CrossRefGoogle Scholar
  111. Zhu, C., et al.: Human activity recognition via motion and vision data fusion. In: 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers (ASILOMAR). IEEE, Pacific Grove (2010)Google Scholar
  112. Zhu, C., Sheng, W.: Recognizing human daily activity using a single inertial sensor. In: 2010 8th World Congress on Intelligent Control and Automation (WCICA), pp. 282–287. IEEE, Jinan (2010)Google Scholar
  113. Zhu, C., Sheng, W.: Realtime recognition of complex daily activities using dynamic Bayesian network. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, San Francisco (2011)Google Scholar
  114. Zhu, C., Sheng, W.: Realtime recognition of complex human daily activities using human motion and location data. IEEE Trans. Biomed. Eng. 59(9), 2422–2430 (2012)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Instituto de TelecomunicaçõesUniversidade da Beira InteriorCovilhãPortugal
  2. 2.AltranportugalLisbonPortugal
  3. 3.Polytechnic Institute of ViseuViseuPortugal
  4. 4.Polytechnic Institute of GuardaGuardaPortugal
  5. 5.Universidade Lusófona de Humanidades e TecnologiasLisbonPortugal
  6. 6.Department of Computer TechnologyUniversidad de AlicanteSan Vicente del RaspeigSpain
  7. 7.Faculty of Computer Science and EngineeringUniversity Ss Cyril and MethodiusSkopjeMacedonia
  8. 8.Department of Information EngineeringMarche Polytechnic UniversityAnconaItaly

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