De Natale Francesco GB

Katsaggelos Aggelos K

Mayora Oscar

Wu Ying

EURASIP Journal on Advances in Signal Processing

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Signal Processing Technologies for Ambient Intelligence in Home-Care Applications

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Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2007, Article ID 91730, 3 pagesdoi:10.1155/2007/91730EditorialSignal Processing Technologies for Ambient Intelligence inHome-Care ApplicationsFrancesco G. B. De Natale,1 Aggelos K. Katsaggelos,2 Oscar Mayora,3 and YingWu21Department of Information and Communication Technology, University of Trento, Via Sommarive 14, 38050 Trento, Italy2Department of Electrical and Computer Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208-3118, USA3Multimedia, Interaction and Smart Environments Group, Create-Net International Research Center, Via Solteri 38,38100 Trento, ItalyReceived 22 March 2007; Accepted 22 March 2007Copyright © 2007 Francesco G. B. De Natale et al. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.The possibility of allowing elderly and people with differentkinds of disabilities to conduct a normal life at home and toachieve a more effective inclusion in the society is attractingmore and more interest from both industrial and govern-mental bodies (hospitals, healthcare institutions, and socialinstitutions).Ambient intelligence technologies, supported by ade-quate networks of sensors and actuators, as well as by suitableprocessing and communication technologies, could be one ofthe enabling factors to achieve such an ambitious objective.Recent researches demonstrated the possibility of provid-ing constant monitoring of environmental and biomedicalparameters, and the possibility to autonomously originatealarms, provide primary healthcare services, activate emer-gency calls, and rescue operations through distributed assis-tance infrastructures. Furthermore, proactive systems helpthe user to perform daily activities, stimulating a more ac-tive and healthy lifestyle, and supporting functional rehabili-tation and preservation processes.Although some products are already appearing on themarket, several technological challenges connected withthese applications are still open, ranging from the develop-ment of enabling technologies (hardware and software) tothe standardization of interfaces, the development of intu-itive and ergonomic human-machine interfaces, and the in-tegration of complex systems in a highly multidisciplinaryenvironment.The objective of this special issue is to collect themost significant contributions and visions coming fromboth academic and applied research bodies working in thisstimulating research field. This is a highly interdisciplinaryfield comprising many areas, such as signal processing, imageprocessing, computer vision, sensor fusion, machine learn-ing, pattern recognition, biomedical signal processing, mul-timedia, human-computer interfaces, and networking. Thefocus is primarily on ambient intelligence and home automa-tion technologies, considered as basic tools to build smartenvironments providing advanced home-care services.The possibility of continuously monitoring the elderlyand automatically detecting emergency situations clearlyrepresents one of the priorities in home-care. The paper“Event detection using “variable module graphs” for homecare applications,” by Amit Sethi et al. proposes a newparadigm to better exploit ubiquitous audio-visual capturedevices used in home-care applications, with a special focuson surveillance and complex event detection. Their approachrelies on variable/module (V/M) graphs, a recent extensionof factor graphs. V/M graphs are used to bridge the seman-tic gap between the huge amount of data produced by thecapture devices and the useful high-level concepts to be elab-orated by the vision system. From the application viewpoint,the primary objective is surveillance of location for subjecttracking as well as detection of irregular or anomalous be-haviors. This is done automatically with minimal human in-volvement, with the system being trained to raise an alarmwhen an anomalous behavior is detected.Similarly, the work by J.-S. Hu, and T.-M. Su, titled “Ro-bust background subtraction with shadow and highlight re-moval for indoor surveillance,” tackles the problem of mon-itoring a person in the home environment. In this case, the2 EURASIP Journal on Advances in Signal Processingauthors concentrate on the robust detection of foregroundregions in complex indoor scenes, in the presence of illumi-nation changes and dynamic backgrounds. These are typicalconditions in ambient-assisted living infrastructures, wherethe environmental conditions cannot be strictly controlled,and the false alarm rate can become high. Their proposed ap-proach achieves a robust background subtraction by suitablycombining three models, namely the color-based probabilis-tic background model (CBM, based on a Gaussian mixturemodel), the gradient-based probabilistic background model(GBM, based on the short-term and long-term CBMs), andthe cone-shape illumination model (CSIM, used to identifyshadows and highlights).N. P. Cuntoor and R. Chellappa further emphasize thebehavioral analysis problem in their paper “Mixed-statemodels for nonstationary, multiobject activities.” Here, theobjective is to model and segment human activities in orderto achieve a better knowledge on the actions performed by asubject, and how such actions are performed. The method-ology developed by the authors to pursue this goal relieson a mixed state-space approach. The discrete-valued com-ponent of the mixed state represents higher-level behavior,while the continuous-state models the dynamics within be-havioral segments. A set of behaviors is defined, based ongeneric properties of motion trajectories, and is used to char-acterize segments of activities. A Viterbi-based algorithm isused to detect boundaries between segments. The usefulnessof the proposed approach for temporal segmentation andanomaly detection is illustrated in different contexts, includ-ing the UCF database of human actions.If technologies enabling a timely response to harm-ful events are important, the possibility of preventing suchevents through an early analysis of dangerous behaviorswould be even more attractive. The paper “The PARAChuteproject: remote monitoring of posture and gait for fall pre-vention,” by David J. Hewson et al. describes the resultsachieved within a joint research project named PARAChute(Personnes Âgées et Risque de Chute), whose primary aimwas to develop a methodology that enables the detection ofan increased risk of falling in community-dwelling elderly.The main goal is to provide a remote noninvasive assessmentfor static and dynamic balance assessments and gait analysis.This is achieved by using a combination of two tools: balanceassessment and gait analysis. The first is based on biome-chanical tests (a force plate, providing a measure of the staticand dynamic equilibria), while the second makes use of a vi-sion system. The two subsystems perform local processingand can be remotely interconnected to medical and supportnetworks.Remote monitoring is also the main goal of the pa-per “Real-time transmission and storage of video, audio,and health data in energency and home care sitiuations,”by Ivano Barbieri et al. In this case, the focus is put onthe efficient transmission of large-bandwidth streams ofaudio-visual data for telemedicine applications (continuousmonitoring and emergency handling). The proposed mo-bile communication system is based on the ITU-T H.323multimedia terminal recommendation, suitable for real-timedata/video/audio and telemedical applications. The audioand video codecs H.264 and G723.1, respectively, were im-plemented and optimized in order to obtain high perfor-mance on the system target processors. Furthermore, offlinemedia streaming, and storage and retrieval functionalitieswere supported by integrating a relational database in thehospital central system. A key aspect of the developed pro-totype is the use of low-cost consumer electronics in order toease the market penetration of potential products.Finally, the problem of assuring the privacy to endusersis of fundamental importance in hom-ecare applications,which have to deal with extremely sensitive data such as per-sonal video and voice, biomedical signals. This problem isconsidered in the paper by Datong Chen et al. “Tools for pro-tecting the privacy of specific individuals in video.” The au-thors address two problems: first the automatic identificationof people with limited labelled data, and second the prob-lem of obscuring a human body in the video with preservedstructure and motion information. The automatic identifi-cation is achieved by a discriminative learning algorithm, us-ing a robust face detection and tracking algorithm. The bodyobscuration is implemented through a novel method, whichremoves the appearance information of the people while pre-serving rich structure and motion information. A prototypesystem was tested in a nursing home environment, demon-strating the possibility of minimizing the risk of exposing theidentities of protected people while ensuring the usability ofcaptured data for activity/behavior analysis.We believe this issue will serve the readers well for manyyears to come on this important application area.Francesco G. B. De NataleAggelos K. KatsaggelosOscar MayoraYing WuFrancescoG. B.DeNatale received the Lau-rea degree in electronic engineering in 1990,and the Ph.D. degree in telecommunica-tions in 1994, both from the University ofGenoa, Italy. In 1995-1996, he was a VisitingProfessor at the University of Trento, Italy,and from 1996 to 1999, he was AssistantProfessor at the University of Cagliari, Italy.At present, he is Full Professor of telecom-munications at the University of Trento,where he coordinates the didactic activities of the B.S. and M.S.courses in telecommunications engineering. He is Deputy Headof the Department of Information and Communication Tech-nologies, where he leads the research activities of the MultimediaCommunications Lab. His research interests are focused on im-age and signal processing, with particular attention to multime-dia data compression, processing, and transmission. He was Gen-eral Cochair of the Packet Video Workshop in 2000, and TechnicalProgram Cochair of the IEEE International Conference on ImageProcessing (ICIP) in 2005 and of the Multimedia Services AccessNetworks (MSAN) in 2005. He is also an Associate Editor of theACM/SpringerWireless Networks Journal from 2006. In 1998, he wasthe corecipient of the IEEE Chester-Sall Best Paper Award. He is aSenior Member of IEEE.Francesco G. B. De Natale et al. 3Aggelos K. Katsaggelos received the Dip-loma degree in electrical and mechanicalengineering from the Aristotelian Univer-sity of Thessaloniki, Greece, in 1979, andthe M.S. and Ph.D. degrees both in elec-trical engineering from the Georgia Insti-tute of Technology, in 1981 and 1985, re-spectively. He is currently Professor of EECSat Northwestern University, Director of theMotorola Center for Seamless Communica-tions, and a Member of the Academic Affiliate Staff at EvanstonHospital. Dr. Katsaggelos is a member of the Publication Boardof the IEEE Proceedings and a number of additional publica-tions. He is the editor of Digital Image Restoration (Springer-Verlag, 1991), coauthor of Rate-Distortion Based Video Compres-sion (Kluwer, 1997), coeditor of Recovery Techniques for Image andVideo Compression and Transmission, (Kluwer, 1998), coauthor ofSuper-Resolution of Images and Video and Joint Source-ChannelVideo Transmission (both Morgan & Claypool Publishers, 2007).He is the co-inventor of twelve international patents, a Fellow ofthe IEEE (1998), and the recipient of the IEEE Third MillenniumMedal (2000), the IEEE Signal Processing Society Meritorious Ser-vice Award (2001), an IEEE Signal Processing Society Best PaperAward (2001), and an IEEE ICBE Best Paper Award (2006). Heis a Distinguished Lecturer of the IEEE Signal Processing Society(2006-07).Oscar Mayora obtained his B.S. degree inelectronics and communications at Tec-nológico de Monterrey, Mexico, in 1991.Later, he received an M.S. degree in com-puter science in the same institute and aPh.D. degree in electronic engineering andinformatics at DIBE, University of Genoa,Italy. In 2000, he joined the Advance Inter-active Systems Laboratory at VTT Electron-ics in Oulu, Finland, as an ERCIM Visit-ing Research Fellow. In August 2001, he was appointed AssociateProfessor in the Computer Science Department at Tecnologico deMonterrey. In 2002, he became a Head of the Graduate Program InComputer Science at the same institution. Since September 2004,he is the Head of Multimedia, Interaction and Smart EnvironmentsGroup in CREATE-NET International Research Center in Trento,Italy. His main research interests are in technologies for ambientintelligence and human-computer interaction.Ying Wu received the B.S. degree fromHuazhong University of Science and Tech-nology, Wuhan, China, in 1994, the M.S.degree from Tsinghua University, Beijing,China, in 1997, and the Ph.D. in elec-trical and computer engineering from theUniversity of Illinois at Urbana-Champaign(UIUC), Urbana, Ill, in 2001. From 1997to 2001, he was a Research Assistant atthe Beckman Institute for Advanced Scienceand Technology at UIUC. During summer 1999 and 2000, he wasa Research Intern with Microsoft Research, Redmond, Washing-ton. Since 2001, he has been an Assistant Professor at the Depart-ment of Electrical Engineering and Computer Science of North-western University, Evanston, Ill. His current research interests in-clude computer vision, image and video analyses, pattern recog-nition, machine learning, multimedia data mining, and human-computer interaction. He is an Associate Editor of SPIE Journalof Electronic Imaging and an Associate Editor of IAPR Journal ofMachine Vision and Applications. He received the Robert T. ChienAward at UIUC in 2001, and the NSF Career award in 2003. He is aSenior Member of the IEEE.

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