Abstract
This chapter provides an overview on the background, basic concepts, existing approaches and methodologies, potential applications, opportunities and research trends and directions for computational behaviour analysis. It first introduces the background and context of this book, and the basic concepts and terms used in the discussion of activity recognition in the book. It then provides a high-level review on dominant approaches and methods that have been used for activity recognition in related research communities. Following this, it discusses potential application domains and particularly highlights the role and opportunities of activity recognition in ambient assisted living, which has recently been under vigorous investigation, and also serves as the main application scenario in our discussions throughout the book. Finally the chapter presents research trends and directions of this research field. This chapter is intended to provide necessary technical background and context for readers to help them best prepared for reading and understanding the book.
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References
Weiser M (1991) The computer for the 21st century. Sci Am (1991)
Turaga P, Chellappa R, Subrahmanian VS, Udrea O (2008) Machine recognition of human activities: a survey. IEEE Trans Circuits Syst Video Technol
Wren CR, Tapia EM (2006) Toward scalable activity recognition for sensor networks. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)
Wang Y, Cang S, Yu H (2018) A data fusion-based hybrid sensory system for older people’s daily activity and daily routine recognition. IEEE Sens J 18:6874–6888
Aggarwal JK, Cai Q (1999) Human motion analysis: a review. Comput Vis Image Underst
Cédras C, Shah M (1995) Motion-based recognition a survey. Image Vis Comput
Gavrila DM (1999) The visual analysis of human movement: a survey. Comput Vis Image Underst
Poppe R (2010) A survey on vision-based human action recognition. Image Vis Comput
Moeslund TB, Hilton A, Krüger V (2006) A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst 104(2–3):90–126
Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv
Weinland D, Ronfard R, Boyer E (2011) A survey of vision-based methods for action representation, segmentation and recognition. Comput Vis Image Underst
Aggarwal JK, Ryoo MS (2011) Human activity analysis: a review. ACM Comput Surv
MIT: House_n The PlaceLab. http://web.mit.edu/cron/group/house_n/placelab.html
Helal S, Mann W, El-Zabadani H, King J, Kaddoura Y, Jansen E (2005) The gator tech smart house: a programmable pervasive space. Computer (Long Beach Calif) 38:50–60
Design I Inhaus design. http://inhausdesign.co.uk/projects/
Georgia Institute of Technology: Aware Home Research Initiative. http://www.awarehome.gatech.edu/
The Domus Laboratory. https://cswww.essex.ac.uk/iieg/idorm.htm
The IDORM project. https://www.usherbrooke.ca/domus/fr/
Lin W, Xing S, Nan J, Wenyuan L, Binbin L (2018) Concurrent recognition of cross-scale activities via sensorless sensing. IEEE Sens J 19(2):658–669
De-La-Hoz-Franco E, Ariza-Colpas P, Quero JM, Espinilla M (2018) Sensor-based datasets for human activity recognition – a systematic review of literature. IEEE Access 6:59192–59210
Nef T, Urwyler P, Büchler M, Tarnanas I, Stucki R, Cazzoli D, Müri R, Mosimann U (2015) Evaluation of three state-of-the-art classifiers for recognition of activities of daily living from smart home ambient data. Sensors (Basel) 15:11725–11740
Liu J, Shahroudy A, Xu D, Kot Chichung A, Wang G (2017) Skeleton-based action recognition using spatio-temporal LSTM network with trust gates. IEEE Trans Pattern Anal Mach Intell XX:1–14
Liao L, Fox D, Kautz H (2007) Extracting places and activities from GPS traces using hierarchical conditional random fields. Int J Rob Res
Huỳnh T, Schiele B (2006) Unsupervised discovery of structure in activity data using multiple eigenspaces. In: Hazas M, Krumm J, Strang T (eds) Location- and context-awareness. Springer, Berlin, pp 151–167
Kautz HA (2014) A formal theory of plan recognition and its implementation. In: Reasoning about plans
Wobcke W (2002) Two logical theories of plan recognition. J Log Comput
Bouchard B, Giroux S, Bouzouane A (2006) A smart home agent for plan recognition of cognitively-impaired patients. J Comput
Chen L, Nugent C, Mulvenna M, Finlay D, Hong X, Poland M (2008) A logical framework for behaviour reasoning and assistance in a smart home. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)
Triboan D, Chen L, Chen F, Wang Z (2017) Semantic segmentation of real-time sensor data stream for complex activity recognition. Pers Ubiquitous Comput
Wu T, Lian C, Hsu JYY (2007) Joint recognition of multiple concurrent activities using factorial conditional random fields. In: Proceedings 22nd conferences on artificial intelligence
Modayil J, Bai T, Kautz H (2008) Improving the recognition of interleaved activities. In: Proceedings of the 10th international conference on Ubiquitous computing - UbiComp ’08
Gu T, Wu Z, Tao X, Pung HK, Lu J (2009) epSICAR: an emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In: 7th annual IEEE international conference on pervasive computing and communications, PerCom 2009
Gong S, Xiang T (2003) Recognition of group activities using dynamic probabilistic networks. In: Proceedings ninth IEEE international conference on computer vision
Nguyen N, Venkatesh S, Bui H (2006) Recognising behaviours of multiple people with hierarchial probabilistic model and statistical data association. In: British machine vision conference
Oliver N, Rosario B, Pentland A (1999) A Bayesian computer vision system for modeling human interactions. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)
Choudhury T, Basu S (2005) Modeling conversational dynamics as a mixed-memory markov process. Adv Neural Inf Process Syst 17
Oliver N, Garg A, Horvitz E (2004) Layered representations for learning and inferring office activity from multiple sensory channels. In: Computer vision and image understanding
Wyatt D, Choudhury T, Bilmes J, Kautz H (2007) A privacy-sensitive approach to modeling multi-person conversations. In: IJCAI international joint conference on artificial intelligence
Youtian D, Feng C, Wenli X, Yongbin L (2006) Recognizing interaction activities using dynamic Bayesian network. In: Proceedings - international conference on pattern recognition
Lian C-C, Hsu JY-J (2008) Chatting activity recognition in social occasions using factorial conditional random fields with iterative classification. In: Proceedings of the 23rd national conference on artificial intelligence, vol 3. AAAI Press, pp 1814–1815
Lin ZH, Fu LC (2007) Multi-user preference model and service provision in a smart home environment. In: Proceedings of the 3rd IEEE international conference on automation science and engineering, IEEE CASE 2007
Wang L, Gu T, Tao X, Lu J (2008) Sensor-based human activity recognition in a multi-user scenario. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)
Singla G, Cook DJ, Schmitter-Edgecombe M (2010) Recognizing independent and joint activities among multiple residents in smart environments. J Ambient Intell Humaniz Comput
Hoey J, Grze M (2011) Distributed control of situated assistance in large domains with many tasks. In: Twenty-first international conference on automated planning and scheduling (2011)
Nugent C, Finlay D, Davies R, Wang H, Zheng H, Hallberg J, Synnes K, Mulvenna M (2007) homeML ? An open standard for the exchange of data within smart environments. In: Pervasive computing for quality of life enhancement
Chen L, Nugent C, Al-Bashrawi A (2009) Semantic data management for situation-aware assistance in ambient assisted living. In: Proceedings of the 11th international conference on information integration and web-based applications & services - iiWAS ’09
Biswas J, Baumgarten M, Tolstikov A, Wai AAP, Nugent C, Chen L, Donnelly M (2010) Requirements for the deployment of sensor based recognition systems for ambient assistive living. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)
Chen L, Nugent C (2009) Ontology-based activity recognition in intelligent pervasive environments. Int J Web Inf Syst
Okeyo G, Chen L, Wang H, Sterritt R (2011) Ontology-based learning framework for activity assistance in an adaptive smart home. In: Chen L, Nugent CD, Biswas J, Hoey J (eds) Activity recognition in pervasive intelligent environments. Atlantis Press, Paris, pp 237–263
Modayil J, Levinson R, Harman C (2008) Integrating sensing and cueing for more effective activity reminders. In: AAAI fall symposium: AI in eldercare: new solutions to old problems
Pollack ME, Brown L, Colbry D, McCarthy CE, Orosz C, Peintner B, Ramakrishnan S, Tsamardinos I (2003) Autominder: an intelligent cognitive orthotic system for people with memory impairment. In: Robotics and autonomous systems
Kan P, Huq R, Hoey J, Goetschalckx R, Mihailidis A (2011) The development of an adaptive upper-limb stroke rehabilitation robotic system. J Neuroeng Rehabil 8:33
Hoey J, Poupart P, von Bertoldi A, Craig T, Boutilier C, Mihailidis A (2010) Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process. Comput Vis Image Underst
Hoey J, Pltz T, Jackson D, Monk A, Pham C, Olivier P (2011) Rapid specification and automated generation of prompting systems to assist people with dementia. Pervasive Mob Comput
Latfi F, Lefebvre B, Descheneaux C (2007) Ontology-based management of the telehealth smart home, dedicated to elderly in loss of cognitive autonomy. In: CEUR workshop proceedings (2007)
Klein M, Schmidt A, Lauer R (2007) Ontology-centred design of an ambient middleware for assisted living: the case of SOPRANO. Context (2007)
Chen L, Nugent C, Mulvenna M, Finlay D, Hong X (2009) Semantic smart homes: towards knowledge rich assisted living environments. Stud Comput Intell
Patel S, Lorincz K, Hughes R, Huggins N, Growdon J, Standaert D, Akay M, Dy J, Welsh M, Bonato P (2009) Monitoring motor fluctuations in patients with parkinsons disease using wearable sensors. IEEE Trans Inf Technol Biomed (2009)
Fogarty J, Au C, Hudson SE (2006) Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition. In: Proceedings of the annual ACM symposium on user interface software and technology
Patel SN, Reynolds MS, Abowd GD (2008) Detecting human movement by differential air pressure sensing in HVAC system ductwork: an exploration in infrastructure mediated sensing. In: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)
Mei L, Easterbrook S (2007) Evaluating user-centric adaptation with goal models. In: Proceedings - ICSE 2007 workshops: first international workshop on software engineering for pervasive computing applications, systems, and environments, SEPCASE’07
Chen L, Rashidi P (2012) Situation, activity and goal awareness in ubiquitous computing. Int J Pervasive Comput Commun 8:216–224
Yin J, Yang Q, Pan JJ (2008) Sensor-based abnormal human-activity detection. IEEE Trans Knowl Data Eng
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Chen, L., Nugent, C.D. (2019). Introduction. In: Human Activity Recognition and Behaviour Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-19408-6_1
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