Gaetano Anania

Development of a novel algorithm for human fall detection using wearable sensors

A novel algorithm for human fall detection by means of a tri-axial accelerometer, is described. A module constituted by the accelerometer and an on board processing unit was designed and realized. The system is conceived to be used in a multi-sensor network context for the remote monitoring of personnel working in very severe conditions (firefighters and civil protection operators). In the real application the module is thought to be integrated in the operator uniform collar. The algorithm is based on the detection of a critical trunk inclination in correspondence of an high rotational velocity. A Kalman filter was designed in order to separate the signal component due to gravity (i.e useful to extract the subject orientation) from the one due to the system acceleration. In comparison with the existing solutions the realized algorithm presents many advantages: no training is needed, low computational costs, fast time response and good performances also during critical activities (e.g jumping, running).

Heart Rate and Accelerometer Data Fusion for Activity Assessment of Rescuers During Emergency Interventions

The current state of the art in wearable electronics is the integration of very small devices into textile fabrics, the so-called ¿smart garment.¿ The ProeTEX project is one of many initiatives dedicated to the development of smart garments specifically designed for people who risk their lives in the line of duty such as fire fighters and Civil Protection rescuers. These garments have integrated multipurpose sensors that monitor their activities while in action. To this aim, we have developed an algorithm that combines both features extracted from the signal of a triaxial accelerometer and one ECG lead. Microprocessors integrated in the garments detect the signal magnitude area of inertial acceleration, step frequency, trunk inclination, heart rate (HR), and HR trend in real time. Given these inputs, a classifier assigns these signals to nine classes differentiating between certain physical activities (walking, running, moving on site), intensities (intense, mild, or at rest) and postures (lying down, standing up). Specific classes will be identified as dangerous to the rescuer during operation, such as, ¿subject motionless lying down¿ or ¿subject resting with abnormal HR.¿ Laboratory tests were carried out on seven healthy adult subjects with the collection of over 4.5 h of data. The results were very positive, achieving an overall classification accuracy of 88.8%.

Piezoresistive Goniometer Network for Sensing Gloves

This paper presents a kinesthetic glove realized with knitted piezoresistive fabric (KPF) sensor technology. The glove forefinger area is sensorized by two KPF goniometers obtained on the same piezoresistive substrate. The piezoresistive textile is used for the realization of both electrogoniometers and connections, thus avoiding mechanical constraints due to metallic wires. Sensors are characterized in comparison with commercial goniometers. The glove behavior is pointed out in terms of methacarpal-phalangeal and interphalangeal joint movement reconstruction.

Wearable biomonitoring system for stress management: A preliminary study on robust ECG signal processing

There is a close correlation between stress and health risk factors such as poor immune function and cardiovascular problems. Various researches showed that long-term exposure to stress and its related diseases are responsible of dramatic increase of mortality in theWestern Countries. In this context, the European Collaborative Project INTERSTRESS is aimed at designing and developing advanced simulation and sensing technologies for the assessment and treatment of psychological stress, based on mobile biosensors. In this paper a wearable system able to implement the acquisition and the real-time elaboration of the ECG signal for stress management purposes will be described. A novel and robust algorithm for QRS complex detection has been developed. Robust QRS detection is fundamental to evaluate Heart Rate and Heart Rate Variability that are relevant parameters used as quantitative marker related to mental stress. In comparison to existing solutions the realized algorithm presents many advantages: an adaptive optimal filtering technique that avoids the use of thresholds and empirical rules for R peaks detection, low computational cost for real time elaboration and good tollerance with noisy ECG signal.

An Innovative Multisensor Controlled Prosthetic Hand

This article reports the design, realisation and preliminary testing of an innovative multisensor controlled prosthetic hand. The mechanical design is strongly oriented to satisfy the requirements of delivery a penta-digital prosthetic hand with reduced hand size, low weight, independent finger movement and bio-mimetic shape. Moreover, an ad hoc sensing architecture has been designed including traditional EMG system together with in socket force measurement and inertial sensing. This sensing architecture is intended to reduce the socket complexity in terms of adaptation to different patient forearms, without loosing the possibility of performing multiple hand grips. An innovative concept introducing inertial sensing in order to select appropriate robotic hand configurations is also described. A first prototype of the prosthetic hand has been realised and preliminary tests are reported.

Interreality: The use of advanced technologies in the assessment and treatment of psychological stress

Stress and its related comorbid diseases are responsible for a large proportion of disability worldwide. In particular, chronic stress is the main responsible for the dramatic increase of premature mortality in the Western countries. However, advanced simulation and sensing technologies, such as virtual reality and mobile biosensors offer interesting opportunities for innovative personal health-care solutions to stress. In this work, we describe a technology-based approach to the assessment and treatment of stress that is based on the vision of Interreality. The main feature of interreality is the creation of a hybrid-augmented experience merging the physical and virtual world. This is achieved through: a) an extended sense of presence: in interreality advanced simulations (3-D virtual worlds) are used to transform health guidelines and provisions into experience; b) an extended sense of community (social presence): interreality uses hybrid social interaction and dynamics of group sessions to provide each user with targeted social support in both the physical and virtual world; c) real-time feedback between the physical and virtual worlds: interreality uses bio and activity sensors and devices (e.g. smartphones) to track both the behavior and the health status of the user in real time and to provide targeted suggestions and guidelines. The feedback activity is twofold: (1) behavior in physical world influences the experience in the virtual one, and (2) behavior in the virtual world influences the experience in the real one.

Enhancing the performance of upper limb gesture reconstruction through sensory fusion

A novel method devoted to the reconstruction of the joint angles in a kinematic chain is described. The reconstruction algorithm is based on the fusion of the information deriving from inertial sensors (accelerometers) and conductive elastomer strain sensors. Accelerometers provide a reliable reconstruction when they are employed as inclinometers in quasi-static conditions. They suffer from artifacts when they are used to detect fast movements or when interactions with the environment occur. The knowledge of the frequency components of the movement to be detected permits removal of these artifacts. Conversely, conductive elastomer sensors have a complex dynamic response, but they can easily provide the frequency content of the movement to be detected. A filtering strategy of the inertial sensor signals based on the elastomer sensor response provides a reliable reconstruction of joint variables during the movement.

Psychometric Assessment of Cardio-Respiratory Activity Using a Mobile Platform

It is increasingly recognized that stress has negative effects on growing numbers of people. Stress assessment is a complex issue, but different studies have shown that monitoring user psychophysiological parameter during daily life can be greatly helpful in stress evaluation. In this context, the European Collaborative Project INTERSTRESS is aimed at designing and developing advanced simulation and sensing technologies for the assessment and treatment of psychological stress, based on mobile biosensors.In this study a wearable biosensor platform able to collect physiological and behavioral parameters is reported. The developed mobile platform, in terms of hardware and processing algorithms, is described. Moreover the use of this wearable biosensor platform in combination with advanced simulation technologies, such as virtual reality, offer interesting opportunities for innovative personal health-care solutions to stress.