Valentina Bianchi

An Assistive Home Automation and Monitoring System

A versatile, reliable and inexpensive home automation system is presented, suited for assisting and monitoring elderly and disabled people in home daily living. A smart interface module has been fabricated, which allows for straightforward integration of a wide variety of devices in a standard LAN. A pilot installation provides successfully HW and SW validation.

Simple and efficient methods for steady state visual evoked potential detection in BCI embedded system

Brain Computer Interfaces (BCI) can provide severely impaired users with alternative communication paths, by means of interpretation of the users brain activity. Among BCI operating paradigms, SSVEP is largely exploited for its potentially high throughput and reliability. In this paper, two novel SSVEP processing algorithms are presented, focused on calibration-free operation and computational efficiency, targeted for development of BCI embedded modules. A comparison with other popular SSVEP signal processing algorithm (MEC, AMCC, CCA) is also made; results demonstrate the feasibility and effectiveness of the proposed solutions.

A BCI Platform Supporting AAL Applications

Brain Computer Interface (BCI) technology can provide users lacking voluntary muscle control with an augmentative communication channel, based on the interpretation of her/his brain activity. Such technologies, combined with AAL (Ambient Assisted Living) systems, can potentially have a great impact on daily living, extending the scope of the ageing at home paradigm also to individuals affected by severe motor impairments, for whom interacting with the environment is troublesome. In this paper, a low cost BCI development platform is presented; it consists of a customized EEG acquisition unit and a Matlab-based signal processing environment. An application example using SSVEP paradigm is discussed.

A Wireless Sensor Platform for Assistive Technology Applications

In this paper, the development of a prototypal wireless sensor platform is described, aimed at assisting elderly people and people with disabilities in their daily living activities at home. The wireless sensor network is embedded into a more general home control and monitoring network, from which it can borrow remote communication and supervision facilities, enhancing versatility and reliability. A wearable sensor has been developed, capable of smart recognition of abnormal gait and falls. Effective algorithms have been devised, to make the device suitable for low-power hardware implementation. After initial prototyping phases (based on microcontrollers and FPGA) VLSI synthesis has been carried out, to estimate actual silicon area and power consumption. Then, extensions of the approach have been foreseen, accounting for multiple sensor management. An example of a low cost embedded heartbeat monitor is discussed.

CARDEAGate: Low-cost, ZigBee-based localization and identification for AAL purposes

World population is facing deep demographic changes. A number of societal challenges are to be tackled, and possible support from ICT is sought for. AAL (Ambient Assisted Living) technologies, aimed at fostering independent life of elderly people, are therefore becoming increasingly important. Based on the AAL-system named CARDEA and previously developed at the University of Parma, in this paper a new feature is presented, named CARDEAGate and aimed at providing an inexpensive and scarcely intrusive way for providing user localization and identification information. Such information is needed to implement a number of useful functionalities, and most notably to carry out behavioral analysis in a multi-user context. A simple approach, based on a “wireless barrier” and exploiting ZigBee protocol features is shown to provide reliable monitoring information. Preliminary test results are given, whereas a full characterization is currently under way.

Controlling AAL environments through BCI

A Brain-Computer Interface (BCI) is an alternative/augmentative communication device that can provide users with a different interaction path, based on the interpretation of his/her brain activity. Such technology, applied to Ambient Assisted Living (AAL) contexts, could potentially make the full set of features of such systems accessible to users affected by severe motor impairments, for whom the interaction with the surrounding environment is troublesome. In this paper, a low cost BCI development platform, consisting of a hardware acquisition unit and a Matlab-based prototyping environment is presented. BCI performance assessed by means of an illustrative application example using a 4 class SSVEP paradigm to switch on and off lights. Comparison with other reference methods from literature is also presented.

A Wearable Sensor for AAL-Based Continuous Monitoring

Continuous monitoring of safety and health conditions are among the primary goals of Ambient Assisted Living technologies. Effective solutions, aimed at fostering independent life of elderly persons, need for carefully balancing system intrusiveness, perspicacity, reliability and cost features. Heterogeneous networks, including different combination of environmental and personal wearable sensors can be used, with relevant value coming from data fusion: analysis techniques, aiming at inferring safety and health-related information in an indirect fashion from behavioral features are being deeply investigated with this aim. Device cooperation and interoperability are thus key factors: in this paper, the development of a wearable sensor suitable for broad range AAL application is introduced, addressing features specifically oriented to behavioral analysis. First, the device itself is capable of analyzing different features of the user motion patterns, synthesizing high-level information simple task identification, energy expenditure on board. This result in better battery management less data transferred over the radio link and interoperability thanks to data abstraction. Second, by means of a suitable operating protocol, it cooperates with environmental sensors e.g., a toilet sensor providing the latters with user identification information, and thus allowing to exploit related data even in a multi-user context. This avoid the need of more expensive and complex indoor localization techniques or of more intrusive identification technologies e.g., NFC/RFID tags.

MuSA: Wearable Multi Sensor Assistant for Human Activity Recognition and Indoor Localization

In this paper a wearable multi-sensor device is used for a Behavioral Analysis (BA) focused on Human Activity Recognition (HAR) and Indoor Localization (IL). The analysis exploit a wearable device equipped with inertial sensors like accelerometer, gyroscope and compass in order to evaluate quantity and quality of movements.

Action tagging in a multi-user indoor environment for behavioural analysis purposes

EU population is getting older, so that ICT-based solutions are expected to provide support in the challenges implied by the demographic change. At the University of Parma an AAL (Ambient Assisted Living) system, named CARDEA, has been developed. In this paper a new feature of the system is introduced, in which environmental and personal (i.e., wearable) sensors coexist, providing an accurate picture of the users activity and needs. Environmental devices may greatly help in performing activity recognition and behavioral analysis tasks. However, in a multi-user environment, this implies the need of attributing environmental sensors outcome to a specific user, i.e., identifying the user when he performs a task detected by an environmental device. We implemented such an “action tagging” feature, based on information fusion, within the CARDEA environment, as an inexpensive, alternative solution to the problematic issue of indoor locationing.

The HELICOPTER Project: A Heterogeneous Sensor Network Suitable for Behavioral Monitoring

In this paper, the infrastructure supporting the HELICOPTER AAL-JP project is described. The project aims at introducing behavioral analysis features for early detection of age-related diseases: to this purpose, a heterogeneous sensor network has been designed and implemented, encompassing in the same vision environmental, wearable and clinical sensors. In order to make environmental sensors suitable for behavioral inference, the issue of activity tagging i.e., attribution to a given user of the action detected by the sensors needs to be tackled. Within the HELICOPTER scenario, cooperation between environmental and wearable sensors is exploited to this aim. Preliminary results offer encouraging perspectives: piloting phase, which will validate the approach on a larger scale, is close to start.