Venue: Ubiquitous Computing Lab / F-128 | City: Konstanz, Germany
Individual stress as well as frequent sleep disorder seems to be related with chronicle diseases like diabetes, cerebrovascular disease or other psychiatric illnesses. Besides capturing a huge amount of data, it needs to be interpreted and compared to patterns suited to detect stress and sleep quality in real-time. One are of research is to detect and analyze a relationship between stress and healthy sleep. A second domain covered by the workshop is to derive individual recommendations to improve a health life. A relevant domains are models, systems, technology and services for Ambient Intelligence and Ambient Assisted Living to improve the quality of life, especially for people with "fragility" to obtain an active longevity.
Massimo Conti (Università Politecnica delle Marche)
Natividad Martínez Madrid (Reutlingen University)
Ralf Seepold (HTWG Konstanz)
Ana Jácome Valdés (HTWG Konstanz, Germany)
Title: Systems and Architecture for AAL: from behavioural analysis to fall detection
Speaker: Laura Montanini and Davide Perla
Abstract: In recent years the number of elderly people is growing exponentially. Systems and technologies for the monitoring of elderly people living alone at home can be very helpful in making a long-term analysis of their health status. This presentation will discuss some of the ongoing projects in which our research team is involved. First, the results of a sleep study conducted in collaboration with the University of Copenhagen will be shown. Then the presentation will focus on two different architectures for the monitoring of elderly people and patients suffering from the Alzheimer's disease, respectively. Finally, a system based on a wearable device for fall detection will be described, paying particular attention on the used transmission protocols.
Title: Design and Energetic Analysis of a Self-Powered Bluetooth Low Energy Speed Sensor
Speaker: Luca Buccolini, PhD Student, Università Politecnica delle Marche (Ancona, Italy)
Abstract: Most of the speedometers on bicycles uses batteries. Batteries are polluting materials and they must be replaced. This work presents an implementation of a self-powered speed sensor that uses energy harvesting to power itself, it measures the speed and transmits the data using Bluetooth Low Energy (BLE) to external devices such as smartphones. The energy harvester is a coil that acts as sensor, too. A prototype of the sensor has been built and a voltage regulation circuit has been simulated by using a SPICE simulator. Furthermore, a custom firmware has been designed using a Bluetooth Low Energy nRF51822 SoC by NordicSemiconductor and the parameters of the BLE connection has been accurately chosen to obtain low energy consumptions. Finally, the energy balance between the harvested energy by the coil and the used energy by the SoC has been accomplished. The results demonstrate the technical feasibility of the self-powered BLE speed sensor for bicycles.
Title: A comprehensive architecture for the Internet of Things: Smart Environment and Ambient Assisted Living Applications.
Speaker: Lorenzo Incipini and Sara Raggiunto
Abstract: Internet of Things (IoT) applications are growing in recent years and there are many research group focusing on it. Within this workshop we want to present our Wireless Sensor Network (WSN) architecture for IoT applications, based on motes developed by our team and our web server application to present the sensed data. The proposed mesh network is able to collect different types of sensing data, using the proper sensor unit connected to the mote. We will show our past and future IoT application projects, in particular we compare the data noticed from low cost accelerometer in order to realize an earthquake early warning system with a wide area coverage.
Title: Homomorphic deconvolution for MUAP Parameter Estimation from Surface EMG Signals
Speaker: Simone Orcioni
Abstract: This work presents a technique for parametric model estimation of the
motor unit action potential (MUAP) from the surface electromyography (sEMG)
signal by using homomorphic deconvolution.
The cepstrum-based deconvolution removes the effect of the stochastic impulse train, which originates the sEMG signal, from the power spectrum of sEMG signal itself.
In this way only information on MUAP shape and amplitude were maintained and then used to estimate the parameters of a time-domain model of the MUAP itself.
In order to validate the effectiveness of this technique, sEMG signals recorded
during several biceps curl exercises have been used for MUAP amplitude and time scale
estimation.
The parameters so extracted as functions of time were used to evaluate muscle fatigue showing a good agreement with previously published results.
Title:
Speaker: Ana Jácome Valdés (HTWG Konstanz, Germany)
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Speaker: Wilhelm Daniel Scherz (HTWG Konstanz, Germany)
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Speaker: Maksym Gaiduk (HTWG Konstanz, Germany)
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This worksop is supported by