The main goal of Ambient Assisted Living solutions is to provide assistive technologies and services in smart environments allowing elderly people to have high quality of life. Since 3D sensing technologies are increasingly investigated as monitoring solution able to outperform traditional approaches, in this work a noninvasive monitoring platform based on 3D sensors is presented providing a wide-range solution suitable in several assisted living scenarios. Detector nodes are managed by low-power embedded PCs in order to process 3D streams and extract postural features related to person’s activities. The feature level of details is tuned in accordance with the current context in order to save bandwidth and computational resources. The platform architecture is conceived as a modular system suitable to be integrated into third-party middleware to provide monitoring functionalities in several scenarios. The event detection capabilities were validated by using both synthetic and real datasets collected in controlled and real-home environments. Results show the soundness of the presented solution to adapt to different application requirements, by correctly detecting events related to four relevant AAL services. 1. Introduction During the last years, the interest of the scientific community in smart environments has grown very fast especially within the European Ambient Assisted Living (AAL) Program with the aim of increasing the independent living and the quality of life of older people. The design of AAL systems is normally based on the use of monitoring infrastructures provided by smart environments. Such infrastructures include heterogeneous sensing devices in ad hoc networks with distributed data processing resources and coordinated by intelligent agents, offering information analysis and decision making capabilities. Human activities monitoring is a crucial function of AAL systems, especially in detection of critical situations (e.g., falls and abnormal behaviors) or as support during the execution of relevant tasks (e.g., daily living activities and training/rehabilitation exercises). Generally, human monitoring systems are based on both wearable devices or environmental equipment. In the first case, markers or kinematic sensors (e.g., MEMS accelerometers or gyroscopes) are worn by the end user for body’s movements detection. Recently Baek et al. [1] have presented a necklace embedding a triaxial accelerometer and a gyroscope able to distinguish falls from regular ADLs (activities of daily living) by measuring the angle of the upper body to the ground.
References
[1]
W. S. Baek, D. M. Kim, F. Bashir, and J. Y. Pyun, “Real life applicable fall detection system based on wireless body area network,” in Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC '13), pp. 62–67, 2013.
[2]
B. J. A. Kr？se, T. J. M. Oosterhout, and T. L. M. Kasteren, “Activity monitoring systems in health care,” in Computer Analysis of Human Behavior, A. A. Salah and T. Gevers, Eds., pp. 325–346, Springer, London, UK, 2011.
[3]
H. Rimminen, J. Lindstr？m, M. Linnavuo, and R. Sepponen, “Detection of falls among the elderly by a floor sensor using the electric near field,” IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 6, pp. 1475–1476, 2010.
[4]
H. Foroughi, B. S. Aski, and H. Pourreza, “Intelligent video surveillance for monitoring fall detection of elderly in home environments,” in Proceedings of the 11th International Conference on Computer and Information Technology (ICCIT '08), pp. 219–224, Khulna, Bangladesh, December 2008.
[5]
A. Edgcomb and F. Vahid, “Automated Fall Detection on Privacy-Enhanced Video,” in Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '12), pp. 252–255, San Diego, Calif, USA, 2012.
[6]
G. Mastorakis and D. Makris, “Fall detection system using Kinect’s infrared sensor,” Journal of Real-Time Image Processing, 2012.
[7]
K. Khoshelham, “Accuracy analysis of Kinect depth data,” in Proceedings of the ISPRS Workshop on Laser Scanning, Calgary, Canada, August 2011.
[8]
M. Grassi, A. Lombardi, G. Rescio et al., “An integrated system for people fall-detection with data fusion capabilities based on 3D ToF camera and wireless accelerometer,” in Proceedings of the 9th IEEE Sensors Conference 2010 (SENSORS '10), pp. 1016–1019, Waikoloa, Hawaii, USA, November 2010.
[9]
T. Fuxreiter, C. Mayer, S. Hanke, M. Gira, M. Sili, and J. Kropf, “A modular platform for event recognition in smart homes,” in Proceedings of the 12th IEEE International Conference on e-Health Networking, Application and Services (Healthcom '10), July 2010.
[10]
P. Wolf, A. Schmidt, J. Parada Otte et al., “openAAL—the open source middleware for ambient-assisted living (AAL),” in Proceedings of the AALIANCE Conference, Malaga, Spain, 2010.
[11]
J. Sch？fer, “A middleware for self-organising distributed ambient assisted living applications,” in Proceedings of the Workshop Selbstorganisierende, Adaptive, Kontextsensitive Verteilte Systeme (SAKS '10), 2010.
[12]
A. Coronato, G. De Pietro, and G. Sannino, “Middleware services for pervasive monitoring elderly and ill people in smart environments,” in Proceedings of the 7th International Conference on Information Technology—New Generations (ITNG '10), pp. 810–815, Las Vegas, Nev, USA., April 2010.
O. Gallo, R. Manduchi, and A. Rafii, “CC-RANSAC: fitting planes in the presence of multiple surfaces in range data,” Pattern Recognition Letters, vol. 32, no. 3, pp. 403–410, 2011.
[15]
A. Leone, G. Diraco, and P. Siciliano, “Detecting falls with 3D range camera in ambient assisted living applications: a preliminary study,” Medical Engineering and Physics, vol. 33, no. 6, pp. 770–781, 2011.
[16]
C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '99), pp. 246–252, June 1999.
[17]
M. Isard and A. Blake, “Condensation—conditional density propagation for visual tracking,” International Journal of Computer Vision, vol. 29, no. 1, pp. 5–28, 1998.
[18]
C. Won-Seok, K. Yang-Shin, O. Se-Young, and L. Jeihun, “Fast iterative closest point framework for 3D LIDAR data in intelligent vehicle,” in Proceedings of the IEEE Intelligent Vehicles Symposium (IV '12), pp. 1029–1034, 2012.
[19]
Y. Xiao, P. Siebert, and N. Werghi, “Topological segmentation of discrete human body shapes in various postures based on geodesic distance,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), pp. 131–135, August 2004.
[20]
S. Balocht, H. Krim, I. Kogan, and D. Zenkov, “Rotation invariant topology coding of 2D and 3D objects using morse theory,” in Proceedings of the IEEE International Conference on Image Processing 2005 (ICIP '05), pp. 796–799, September 2005.
[21]
A. Verroust and F. Lazarus, “Extracting skeletal curves from 3D scattered data,” Visual Computer, vol. 16, no. 1, pp. 15–25, 2000.
[22]
G. Diraco, A. Leone, and P. Siciliano, “Geodesic-based human posture analysis by using a single 3D TOF camera,” in Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE '11), pp. 1329–1334, 2011.
[23]
W. Brendel and S. Todorovic, “Activities as time series of human postures,” Lecture Notes in Computer Science, vol. 6312, no. 2, pp. 721–734, 2010.
[24]
R. Cucchiara, C. Grana, A. Prati, and R. Vezzani, “Probabilistic posture classification for human-behavior analysis,” IEEE Transactions on Systems, Man, and Cybernetics, Part A, vol. 35, no. 1, pp. 42–54, 2005.
[25]
R. Debnath, N. Takahide, and H. Takahashi, “A decision based one-against-one method for multi-class support vector machine,” Pattern Analysis and Applications, vol. 7, no. 2, pp. 164–175, 2004.
[26]
F. Buccolieri, C. Distante, and A. Leone, “Human posture recognition using active contours and radial basis function neural network,” in Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS '05), pp. 213–218, September 2005.
[27]
S. Park and H. Kautz, “Privacy-preserving recognition of activities in daily living from multi-view silhouettes and RFID-based training,” in Proceedings of the AAAI Fall Symposium, pp. 70–77, Arlington, Va, USA, November 2008.
[28]
F. V. Jensen and T. D. Nielsen, “Bayesian networks and decision graphs,” in Information Science and Statistics, M. Jordan, J. Kleinberg, and B. Sch？lkopf, Eds., Springer Science Business Media, New York, NY USA, 2007.
[29]
L. Gond, P. Sayd, T. Chateau, and M. Dhome, “A regression-based approach to recover human pose from voxel data,” in Proceedings of the IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops '09), pp. 1012–1019, October 2009.
[30]
W. Li, Z. Zhang, and Z. Liu, “Action recognition based on a bag of 3D points,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '10), pp. 9–14, June 2010.
[31]
“MESA Imaging AG,” SR4000 Data Sheet Rev.5.1., 2011, http://www.mesa-imaging.ch.
[32]
A. Ellelt, “Keeping dementia residents safe,” Assisted Living Consult, vol. 3, no. 5, pp. 19–41, 2007.
