ALBERT

Beschreibung: Ageing is a global phenomenon which is pushing the scientific community forward the development of innovative solutions in the context of Active and Assisted Living (AAL). Among functionality to be implemented, a major role is covered by falls and human activities monitoring. In this paper, main technological solutions to cope with the aforementioned needs are briefly introduced. A specific focus is given on solutions for Falls recognition and classification. A case of study is presented, where a classification methodology based on an event-driven correlation paradigm and an advanced threshold-based classifier is addressed. The receiver operating characteristic (ROC) theory is used to properly define thresholds’ values while, in order to properly assess performances of the classification methodology proposed, dedicated metrics are suggested, such as sensitivity and specificity. The solution proposed shows an average Sensitivity of 0.97 and an average Specificity of 0.99.

Beschreibung: Population ageing is having a direct influence on serious health issues, including hampered mobility and physical decline. Good habits in performing physical activities, in addition to eating and drinking, are essential to improve the life quality of the elderly population. Technological solutions, aiming at increasing awareness or providing reminders to eat/drink regularly, can have a significant impact in this scenario. These solutions enable the possibility to constantly monitor deviations from users’ normal behavior, thus allowing reminders to be provided to users/caregivers. In this context, this paper presents a radio-frequency identification (RFID) system to monitor user’s habits, such as the use of food, beverages, and/or drugs. The device was optimized to fulfill specifications imposed by the addressed application. The approach could be extended for the monitoring of home appliances, environment exploitation, and activity rate. Advantages of the approach compared to other solutions, e.g., based on cameras, are related to the low level of invasiveness and flexibility of the adopted technology. A major contribution of this paper is related to the wide investigation of system behavior, which is aimed to define the optimal working conditions of the system, with regards to the power budget, user (antenna)-tag reading range, and the optimal inter-tag distance. To investigate the performance of the system in tag detection, experiments were performed in a scenario replicating a home environment. To achieve this aim, specificity and sensitivity indexes were computed to provide an objective evaluation of the system performance. For the case considered, if proper conditions are meet, a specificity value of 0.9 and a sensitivity value of 1 were estimated.

Beschreibung: Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time–space evolution require information about characteristic parameters such as the ash granulometry. Typically, such information is gained by spot direct observation of the ash at the ground or by using expensive instrumentation. A distributed Wireless Sensor Network (WSN) of low-cost monitoring stations would represent a suitable solution in performing continuous and high spatial resolution monitoring. In this paper, a novel low-cost vision-based methodology, together with a dedicated image processing algorithm aimed at the estimation and classification of the ash granulometry, is presented. The first prototype developed to investigate the methodology consists of a light-controlled tank and a camera. The acquired images of the ash samples are transmitted to a PC and processed by a dedicated paradigm developed in LabVIEW™. A threshold algorithm was developed to provide a classification of the detected ash. Optimal thresholds were estimated by using the theory of receiver operating characteristic (ROC) curves. The methodology was validated experimentally using real ash erupted from Mount Etna, with three different nominal granulometries: ɸ1 = 0.5 mm, ɸ2 = 1 mm, and ɸ3 = 2 mm. The preliminary results demonstrated the viability of the proposed approach, showing average accuracies in the estimation of the granulometry of 50 µm, suitable for the implementation of a low-cost distributed early warning solution. The main novelties of this work reside in both the low-cost vision-based methodology and the proposed classification algorithm.

Beschreibung: Volcanic ash fall-out represents a serious hazard for air and road traffic. The forecasting models used to predict its time–space evolution require information about the core characteristics of volcanic particles, such as their granulometry. Typically, such information is gained by the spot direct observation of the ash collected at the ground or by using expensive instrumentation. In this paper, a vision-based methodology aimed at the estimation of ash granulometry is presented. A dedicated image processing paradigm was developed and implemented in LabVIEW™. The methodology was validated experimentally using digital reference images resembling different operating conditions. The outcome of the assessment procedure was very encouraging, showing an accuracy of the image processing algorithm of 1.76%.