Giuseppe Zupone

Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation

BackgroundMonitoring body kinematics has fundamental relevance in several biological and technical disciplines. In particular the possibility to exactly know the posture may furnish a main aid in rehabilitation topics. In the present work an innovative and unobtrusive garment able to detect the posture and the movement of the upper limb has been introduced, with particular care to its application in post stroke rehabilitation field by describing the integration of the prototype in a healthcare service.MethodsThis paper deals with the design, the development and implementation of a sensing garment, from the characterization of innovative comfortable and diffuse sensors we used to the methodologies employed to gather information on the posture and movement which derive from the entire garments. Several new algorithms devoted to the signal acquisition, the treatment and posture and gesture reconstruction are introduced and tested.ResultsData obtained by means of the sensing garment are analyzed and compared with the ones recorded using a traditional movement tracking system.ConclusionThe main results treated in this work are summarized and remarked. The system was compared with a commercial movement tracking system (a set of electrogoniometers) and it performed the same accuracy in detecting upper limb postures and movements.

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).

Characterization of a Novel Data Glove Based on Textile Integrated Sensors

The present work is about the realization and the characterization of a novel data glove able to detect hand kinematic configurations. The sensing glove has been realized by directly integrate sensors in the fabric used to manufacture the glove. Main specifications for the realized device are lightness, wearability and user comfort. As a fundamental requirement to address this purpose we have estimated the employment of a material which does not substantially change the mechanical properties of the fabric and maintains the wearability of the garment. To obtain this result, we have integrated sensor networks made by conductive elastomer into an elastic fabric used to manufacture the sensing glove. Electrically conductive elastomer composites show piezoresistive properties when a deformation is applied. Conductive elastomers materials can be applied to fabric or to other flexible substrate and they can be employed as strain sensors. To validate the realized device, a function that relates glove sensor values to hand motion has been realized and tested

Sensing Glove for Brain Studies: Design and Assessment of Its Compatibility for fMRI With a Robust Test

In this paper, we describe a biomimetic-fabric-based sensing glove that can be used to monitor hand posture and gesture. Our device is made of a distributed sensor network of piezoresistive conductive elastomers integrated into an elastic fabric. This solution does not affect natural movement and hand gestures, and can be worn for a long time with no discomfort. The glove could be fruitfully employed in behavioral and functional studies with functional MRI (fMRI) during specific tactile or motor tasks. To assess MR compatibility of the system, a statistical test on phantoms is introduced. This test can also be used for testing the compatibility of mechatronic devices designed to produce different stimuli inside the MR environment. We propose a statistical test to evaluate changes in SNR and time-domain standard deviations between image sequences acquired under different experimental conditions. fMRI experiments on subjects wearing the glove are reported. The reproducibility of fMRI results obtained with and without the glove was estimated. A good similarity between the activated regions was found in the two conditions.

Wearable kinesthetic systems for capturing and classifying body posture and gesture

Monitoring body kinematics has fundamental relevance in several biological and technical disciplines. In particular the possibility to know the posture exactly may furnish a main aid in rehabilitation topics. This paper deals with the design, the development and the realization of sensing garments, from the characterization of innovative comfortable and spreadable sensors to the methodologies employed to gather information on posture and movement. In the present work an upper limb kinesthetic garment (ULKG), which allows to reconstruct shoulder, elbow and wrist movements and a kinesthetic glove able to detect posture an gesture of the hand are presented. Sensors are directly integrated in Lycra fabrics by using conductive elastomer (CE) sensors. CE sensors show piezoresistive properties when a deformation is applied and they can be integrated onto fabric or other flexible substrate to be employed as strain sensors

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.

Wearable kinesthetic system for capturing and classifying upper limb gesture

Electrically conductive elastomer composites (CEs) show piezoresistive properties when a deformation is applied. In several applications, CEs can be integrated onto fabric or other flexible substrate and can be employed as strain sensors. Moreover, integrated CE sensors may be used in biomechanical analysis to realize wearable kinesthetic interfaces able to detect posture and movement of the human body. In the following a kinesthetic upper limb garment realized by CEs which allows to reconstruct shoulder, elbow and wrist movements is presented.

Analysis and synthesis of human movements: wearable kinesthetic interfaces

Electrically conductive elastomer composites (CEs) show piezoresistive properties when a deformation is applied. In several applications, CEs can be integrated onto fabric or other flexible substrate and can be employed as strain sensors. Moreover, integrated CE sensors may be used in biomechanical analysis to realize wearable kinesthetic interfaces able to detect posture and movement of the human body. In the following a kinesthetic upper limb garment realized by CEs which allows to reconstruct shoulder, elbow and wrist movements and a kinesthetic glove able to detect posture and gesture of the hand are presented.

Body Gesture and Posture Classification by Smart Clothes

Monitoring body kinematics has fundamental relevance in several biological and technical disciplines. In particular the possibility to know the posture exactly may furnish a main aid in rehabilitation topics. This paper deals with the design, the development and the realization of sensing garments, from the characterization of innovative comfortable and spreadable sensors to the methodologies employed to gather information on posture and movement. In the present work an upper limb kinesthetic garment (ULKG), which allows to reconstruct shoulder, elbow and wrist movements and a kinesthetic glove able to detect posture an gesture of the hand are presented. Sensors are directly integrated in Lycra fabrics by using conductive elastomer (CE) sensors. CE sensors show piezoresistive properties when a deformation is applied and they can be integrated onto fabric or other flexible substrate to be employed as strain sensors

Neural correlates of human-robot handshaking

Handshaking represents a complex motor and cognitive task that poses several challenges from both engineering and neuroscientific viewpoints. In particular, it is an intriguing application which can be profitably studied in the field of Human Robot Interaction (HRI). In this work an experimental paradigm is proposed to investigate the neural correlates of handshaking between humans and between humans and robots using functional Magnetic Resonance Imaging. More specifically the role of visual and haptic components during handshaking interaction will be studied. A wearable sensing glove will be used to monitor hand finger position and movement. Preliminary results will be reported and discussed.