Ferdinando Grossi

Light on! Real world evaluation of a P300-based brain–computer interface (BCI) for environment control in a smart home

Brain–computer interface (BCI) systems aim to enable interaction with other people and the environment without muscular activation by the exploitation of changes in brain signals due to the execution of cognitive tasks. In this context, the visual P300 potential appears suited to control smart homes through BCI spellers. The aim of this work is to evaluate whether the widely used character-speller is more sustainable than an icon-based one, designed to operate smart home environment or to communicate moods and needs. Nine subjects with neurodegenerative diseases and no BCI experience used both speller types in a real smart home environment. User experience during BCI tasks was evaluated recording concurrent physiological signals. Usability was assessed for each speller type immediately after use. Classification accuracy was lower for the icon-speller, which was also more attention demanding. However, in subjective evaluations, the effect of a real feedback partially counterbalanced the difficulty in BCI use. Practitioner Summary: Since inclusive BCIs require to consider interface sustainability, we evaluated different ergonomic aspects of the interaction of disabled users with a character-speller (goal: word spelling) and an icon-speller (goal: operating a real smart home). We found the first one as more sustainable in terms of accuracy and cognitive effort.

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.

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.

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.

Comprehensive human monitoring based on heterogeneous sensor network

Healthcare paradigms, due to demographic changes, are definitely aiming at effective prevention and early diagnosis strategies. This inherently calls for continuous monitoring of (ageing) people in their own living environment and while attending at daily living activities. Such monitoring may rely on a wide range of sensing technologies, each featuring different trade-offs among main parameters such as accuracy, expressivity, cost, reliability and intrusively. This includes clinical sensors (suitable for self-managed, precise measurement of physiological parameters), wearable devices (continuously monitoring health or activity features) and environmental sensors distributed in the living environment (suitable for indirect assessment of relevant behaviours, besides serving basic safety purposes). In this chapter, the meaning of human monitoring from a home-care point of view will be defined, and the basic sensor categories will be reviewed. Then, the design and the main features of the CARDEA home monitoring system will be discussed. Finally, some application examples, coming from European project living-lab experiences, will be illustrated, and some results obtained by data fusion and analysis techniques, suitable for inferring health and wellness information by effectively correlating raw data coming from the sensor field, will be presented.

MuSA: A Smart Wearable Sensor for Active Assisted Living

This paper focuses at features introduced in the wearable sensor MuSA, to support behavioral analysis within the context of the HELICOPTER project, funded in the AAL European joint program. In particular, the wearable device performs two key function: on one hand it is used as a behavioral data source, continuously monitoring the quantity of user physical activity (through the energy expenditure index evaluation), location and posture; on the other hand, MuSA enables fusion of data coming from the environmental sensors, properly attributing actions on a particular sensor to a specific user in a multi-user environment. These function are carried out without the need of external devices (RFID tags etc.), but only relying on sensors embedded on the wearable device and its communication capabilities. Some sample results coming from pilot studies are shown.

Tools for Behavior Monitoring: An Ambient Assisted Living Real Experience

In recent years, sophisticated technologies for personal monitoring are rapidly spreading. Mainly, these devices are medical sensors for domestic use, that allow for monitoring the most important physiological parameters. These smart devices enable to detect information closely related to the user’s health. Despite the effectiveness of these tools, such approaches do not allow for the detection of the personal wellness state, in a wider sense. From this point of view, more information can be detected by the analysis of user’s behavior. Especially in the case of elderly users, changes of behavior may be clues of situation of uneasiness or worsening of health condition. To support the monitoring of wellness conditions, tools and techniques for behavioral analysis have been developed at Information Engineering Department of University of Parma (ITALY). In this paper, a non-invasive and cost-effective technology is presented: the CARDEAdomus Ambient Assisted Living System. Eventually, results related to a experimentation, carried out by means of the System in a real context, are showed.

MyCmon: Cloud-Based Smart Home Monitoring for Elderly People and People with Disabilities

This paper describes the MyCmon Cloud-Based Smart Home Monitoring System including a home automation system permanently connected through the internet to a Cloud-based control system. MyCmon aims at supporting independent living of elderly people and people with disabilities, by making monitoring functionalities available to their loved ones or to caregivers, anywhere and at any time, through mobile devices. The project is under development, expecting to be ready for the market by March 2014.