1,721,000 research outputs found
SMARE: Semi-supervised method for activities of daily living recognition
No full textThe significant growth of the average age of the world population requires effective solutions to meet the unsustainable number of requests for hospitalization. For this reason, the scientific community is seeking new paradigms of care that provide compensatory interventions to the first signs of warning that can be detected in the behavior of the individuals. At this aim, this work proposes a method to detect the Activities of Daily Living carried out by a subject monitored at home through unobtrusive environmental sensors. The Activity Recognition system is composed of an unsupervised segmentation layer which splits the collected sensors activation data in time periods and a semantic layer that exploits a knowledge-based approach to provide the most probable activity label for each period. The system is enriched with a further layer to adapt the base knowledge according to the information directly provided by the resident
Wellness assessment of alzheimer’s patients in an instrumented health-care facility
Full text linkWellness assessment refers to the evaluation of physical, mental, and social well-being. This work explores the possibility of applying technological tools to assist clinicians and professionals to improve the quality of life of people through continuous monitoring of their wellness. The contribution of this paper is manifold: a coarse-grained localization system is responsible for monitoring and collecting data related to patients, while a novel wellness assessment methodology is proposed to extract quantitative indicators related to the well-being of patients from the collected data. The proposed system has been installed at “Il Paese Ritrovato”, an innovative health-care facility for Alzheimer’s in Monza, Italy; first satisfactory results have been obtained, and the dataset shows great potential for several applications
Special track on digital life for human well-being - DLHWB
No full textThe special track DLHWB is dedicated to methodologies and technologies that can be helpful, guarantee and improve quality of life or support positive well-being. Digital services are an integral part of many aspects of our everyday life, from the workplace to leisure time, to our daily life in our own living environment. Digital Life for Human Well-being is part of the "smart society" and implies the need to respect the needs identified by Maslow's pyramid: physiological (for example, food and energy), safety, belonging (for example, friendship and social inclusion), esteem (for example, recognition) and self-fulfillment (for example, creativity) to achieve good quality of life
An Internal Representation Model for System-Level Co-Design of Heterogeneous Multiprocessor Embedded System
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High-efficiency multi-sensor system for chair usage detection
Full text linkRecognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%
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