Combining Users' Activity Survey and Simulators to Evaluate Human Activity Recognition Systems

Open Access articleEvaluating human activity recognition systems usually implies following expensive and time-consuming methodologies,where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a syntheticdatasetgeneratedfollowingtheproposedmethodologyiscomparedtoarealdataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant

Discovering human activities from binary data in smart homes

With the rapid development in sensing technology, data mining, and machine learning fields for human health monitoring, it became possible to enable monitoring of personal motion and vital signs in a manner that minimizes the disruption of an individual’s daily routine and assist individuals with difficulties to live independently at home. A primary difficulty that researchers confront is acquiring an adequate amount of labeled data for model training and validation purposes. Therefore, activity discovery handles the problem that activity labels are not available using approaches based on sequence mining and clustering. In this paper, we introduce an unsupervised method for discovering activities from a network of motion detectors in a smart home setting. First, we present an intra-day clustering algorithm to find frequent sequential patterns within a day. As a second step, we present an inter-day clustering algorithm to find the common frequent patterns between days. Furthermore, we refine the patterns to have more compressed and defined cluster characterizations. Finally, we track the occurrences of various regular routines to monitor the functional health in an individual’s patterns and lifestyle. We evaluate our methods on two public data sets captured in real-life settings from two apartments during seven-month and three-month periods

Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

Abstract. Event detection is a central component in numerous wireless sensor network (WSN) applications. In spite of this, the area of event description has not received enough attention. The majority of current event description approaches rely on using precise values to specify event thresholds. However, we believe that crisp values cannot adequately handle the often imprecise sensor readings. In this paper we demonstrate that using fuzzy values instead of crisp ones significantly improves the accuracy of event detection. We also show that our fuzzy logic approach provides higher detection precision than a couple of well established classification algorithms. A disadvantage of using fuzzy logic is the exponentially growing size of the rule-base. Sensor nodes have limited memory and storing large rulebases could be a challenge. To address this issue we have developed a number of techniques that help reduce the size of the rule-base by more than 70 % while preserving the level of event detection accuracy. Key words: wireless sensor networks, fuzzy logic, event description, event detection accuracy

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment

[EN] Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.Shirali, M.; Bayo-Monton, JL.; Fernández Llatas, C.; Ghassemian, M.; Traver Salcedo, V. (2020). Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment. Sensors. 20(24):1-25. https://doi.org/10.3390/s20247167S1252024Lutz, W., Sanderson, W., & Scherbov, S. (2001). The end of world population growth. Nature, 412(6846), 543-545. doi:10.1038/35087589United Nations, Department of Economic and Social Affairs, World Population Prospoects 2019 https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdfAtzori, L., Iera, A., & Morabito, G. (2017). Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56, 122-140. doi:10.1016/j.adhoc.2016.12.004Cook, D. J., Duncan, G., Sprint, G., & Fritz, R. L. (2018). Using Smart City Technology to Make Healthcare Smarter. Proceedings of the IEEE, 106(4), 708-722. doi:10.1109/jproc.2017.2787688Cook, D. J., & Krishnan, N. (2014). Mining the home environment. Journal of Intelligent Information Systems, 43(3), 503-519. doi:10.1007/s10844-014-0341-4Alaa, M., Zaidan, A. A., Zaidan, B. B., Talal, M., & Kiah, M. L. M. (2017). A review of smart home applications based on Internet of Things. Journal of Network and Computer Applications, 97, 48-65. doi:10.1016/j.jnca.2017.08.017Palipana, S., Pietropaoli, B., & Pesch, D. (2017). Recent advances in RF-based passive device-free localisation for indoor applications. Ad Hoc Networks, 64, 80-98. doi:10.1016/j.adhoc.2017.06.007Chen, G., Wang, A., Zhao, S., Liu, L., & Chang, C.-Y. (2017). Latent feature learning for activity recognition using simple sensors in smart homes. Multimedia Tools and Applications, 77(12), 15201-15219. doi:10.1007/s11042-017-5100-4Tewell, J., O’Sullivan, D., Maiden, N., Lockerbie, J., & Stumpf, S. (2019). Monitoring meaningful activities using small low-cost devices in a smart home. Personal and Ubiquitous Computing, 23(2), 339-357. doi:10.1007/s00779-019-01223-2Krishnan, N. C., & Cook, D. J. (2014). Activity recognition on streaming sensor data. Pervasive and Mobile Computing, 10, 138-154. doi:10.1016/j.pmcj.2012.07.003Wang, A., Chen, G., Wu, X., Liu, L., An, N., & Chang, C.-Y. (2018). Towards Human Activity Recognition: A Hierarchical Feature Selection Framework. Sensors, 18(11), 3629. doi:10.3390/s18113629Liu, Y., Wang, X., Zhai, Z., Chen, R., Zhang, B., & Jiang, Y. (2019). Timely daily activity recognition from headmost sensor events. ISA Transactions, 94, 379-390. doi:10.1016/j.isatra.2019.04.026Viani, F., Robol, F., Polo, A., Rocca, P., Oliveri, G., & Massa, A. (2013). Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation. Proceedings of the IEEE, 101(11), 2381-2396. doi:10.1109/jproc.2013.2266858Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011). Discovering Activities to Recognize and Track in a Smart Environment. IEEE Transactions on Knowledge and Data Engineering, 23(4), 527-539. doi:10.1109/tkde.2010.148Samsung SmartThings http://www.smartthings.com/Apple HomeKit https://www.apple.com/ios/home/Vera3 Advanced Smart Home Controller http://getvera.com/controllers/vera3/AndroidThings https://developer.android.com/things/index.htmlTeleAlarm Assisted Living http://www.telealarm.com/en/products/assisted-livingBirdie—Connected Sensors around the Home https://birdie.care/AllJoyn Framework https://identity.allseenalliance.org/developersCook, D. J., Crandall, A. S., Thomas, B. L., & Krishnan, N. C. (2013). CASAS: A Smart Home in a Box. Computer, 46(7), 62-69. doi:10.1109/mc.2012.328Skubic, M., Alexander, G., Popescu, M., Rantz, M., & Keller, J. (2009). A smart home application to eldercare: Current status and lessons learned. Technology and Health Care, 17(3), 183-201. doi:10.3233/thc-2009-0551Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., & Jansen, E. (2005). The Gator Tech Smart House: a programmable pervasive space. Computer, 38(3), 50-60. doi:10.1109/mc.2005.107Doctor, F., Hagras, H., & Callaghan, V. (2005). A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 35(1), 55-65. doi:10.1109/tsmca.2004.838488Abowd, G. D., & Mynatt, E. D. (2005). Designing for the Human Experience in Smart Environments. Smart Environments, 151-174. doi:10.1002/047168659x.ch7Technology Integrated Health Management (TIHM) Project https://www.sabp.nhs.uk/tihmAhvar, E., Daneshgar-Moghaddam, N., Ortiz, A. M., Lee, G. M., & Crespi, N. (2016). On analyzing user location discovery methods in smart homes: A taxonomy and survey. Journal of Network and Computer Applications, 76, 75-86. doi:10.1016/j.jnca.2016.09.012Milenkovic, M., & Amft, O. (2013). Recognizing Energy-related Activities Using Sensors Commonly Installed in Office Buildings. Procedia Computer Science, 19, 669-677. doi:10.1016/j.procs.2013.06.089Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Dogan, O., Bayo-Monton, J.-L., Fernandez-Llatas, C., & Oztaysi, B. (2019). Analyzing of Gender Behaviors from Paths Using Process Mining: A Shopping Mall Application. Sensors, 19(3), 557. doi:10.3390/s19030557Schmitter-Edgecombe, M., & Cook, D. J. (2009). Assessing the Quality of Activities in a Smart Environment. Methods of Information in Medicine, 48(05), 480-485. doi:10.3414/me0592Alberdi Aramendi, A., Weakley, A., Aztiria Goenaga, A., Schmitter-Edgecombe, M., & Cook, D. J. (2018). Automatic assessment of functional health decline in older adults based on smart home data. Journal of Biomedical Informatics, 81, 119-130. doi:10.1016/j.jbi.2018.03.009Dawadi, P. N., Cook, D. J., & Schmitter-Edgecombe, M. (2016). Automated Cognitive Health Assessment From Smart Home-Based Behavior Data. IEEE Journal of Biomedical and Health Informatics, 20(4), 1188-1194. doi:10.1109/jbhi.2015.2445754Sprint, G., Cook, D. J., & Schmitter-Edgecombe, M. (2017). Unsupervised Detection and Analysis of Changes in Everyday Physical Activity Data. Intelligent Systems Reference Library, 97-122. doi:10.1007/978-3-319-67513-8_6Taheri Tanjanai, P., Moradinazar, M., & Najafi, F. (2016). Prevalence of depression and related social and physical factors amongst the Iranian elderly population in 2012. Geriatrics & Gerontology International, 17(1), 126-131. doi:10.1111/ggi.12680Zhao, Z., Zhang, M., Yang, C., Fang, J., & Huang, G. Q. (2018). Distributed and collaborative proactive tandem location tracking of vehicle products for warehouse operations. Computers & Industrial Engineering, 125, 637-648. doi:10.1016/j.cie.2018.05.00

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications

HUMAN ACTIVITY RECOGNITION IN SMART-HOME ENVIRONMENTS FOR HEALTH-CARE APPLICATIONS

With a growing population of elderly people, the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Clinicians are interested in monitoring several behavioral aspects for a wide variety of applications: early diagnosis, emergency monitoring, assessment of cognitive disorders, etcetera. Among the several behavioral aspects of interest, anomalous behaviors while performing activities of daily living (ADLs) are of great importance. Indeed, these anomalies can be indicators of serious cognitive diseases like Mild Cognitive Impairment. The recognition of such abnormal behaviors relies on robust and accurate ADLs recognition systems. Moreover, in order to enable unobtrusive and privacy-aware monitoring, environmental sensors in charge of unobtrusively capturing the interaction of the subject with the home infrastructure should be preferred. This thesis presents several contributions on this topic. The major ones are two novel hybrid ADLs recognition algorithms. The former is supervised while the latter is unsupervised. Preliminary results, which still need to be confirmed, show that the recognition rate of the unsupervised method is comparable to the one obtained by the supervised one, with the great advantage of not requiring the acquisition of an annotated dataset. Beyond ADLs recognition, other contributions on smart sensing and anomaly recognition are presented. Regarding unobtrusive sensing, we propose a machine learning technique to detect fine-grained manipulations performed by the inhabitant on household objects instrumented with tiny accelerometer sensors. Finally, a novel rule-based framework for the recognition of fine-grained abnormal behaviors is presented. Experimental results on several datasets show the effectiveness of all the proposed techniques

Enhanced Living Environments

This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area