Context-Aware AAL Services through a 3D Sensor-Based Platform

Abstract

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.

Smart home technologies that support independent living: challenges and opportunities for the building industry – a systematic mapping study

“…They may be instrumented with accelerometers (Li 2013), piezoelectric sensors (Alwan 2009;Klack et al 2011;Leusmann et al 2011), (capacitive) pressure sensors (Olivier et al 2009;Helal et al 2012;Hoey et al 2011;Stefanov, Bien, and Bang 2004), micro-electro-mechanical systems (MEMS) (Fernandez-Luque et al 2013;Helal et al 2012), or innovative sensors using the near-field imaging principle (reported in Leone, Diraco, and Siciliano 2013), but the solution of a ready-to-be-used pressure-sensitive textile layer (Hanson, Barth, and Silverman 2011;Lauterbach et al 2012), called a presence mat or pressure mat, has been popular since the last years . The same technologies can be used for detecting sofa/bed/chair occupancy (Almudevar et al 2008;Cruz-Sanchez et al 2012;Fernandez-Luque et al 2013;Hilbe et al 2010;Kosse et al 2013;Muñoz et al 2011;Palumbo et al 2014;Suryadevara et al 2013;Raad and Yang 2009;Zhang, Hariz, and Mokhtari 2008).…”

“…The recent topic of augmented reality is mainly supported by the new CV-based fusionsensing device called Kinect (As'ari and Sheikh 2012; Dingli, Attard, and Mamo 2012;Georgoulas, Linner, and Bock 2014;Nishigaki et al 2013). More generally, threedimensional sensing technologies have been thoroughly reviewed by Leone, Diraco, and Siciliano (2013). Among other advantages, they are non-intrusive, and they enable a new vision-based monitoring modality where privacy is preserved since only distance data unable to reveal the subject identity is processed .…”

Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment

“…Qiang Li et al [ 33 ] investigated a novel fall detection method that utilizes acceleration, posture and context information, where context can be presented by environmental sensors (room location or furniture positions) and personal profiles (e.g., health status and age). A wireless accelerometer, 3-D camera and microphone are simultaneously processed by Leone et al to reach a better result in fall risk assessment [ 34 ]. All the presented studies, however, are lacking a reliable fusing technique, combining processing results from independent components.…”

Performance Analysis of ZigBee Wireless Networks for AAL through Hybrid Ray Launching and Collaborative Filtering

“…In the past decade, with the steady adoption of software solutions and seamless connectivity, electronic health (e-Health) and mobile health (m-Health) have enabled ambient assisted living (AAL) and context-aware scenarios. In this context, real time monitoring and interaction of patients and users with health specialists can be performed remotely [1,2], decreasing overall costs and increasing quality of life, reducing patients displacement, and allowing them to live in their homes [3,4]. Parameters such as biomedical signals, drug distribution, patients' behavior, interactions, and alarm signals can be readily collected and analyzed.…”