Mario Tesconi

Strain sensing fabric for hand posture and gesture monitoring

In this paper, we report on a new technology used to implement strain sensors to be integrated in usual garments. A particular conductive mixture based on commercial products is realized and directly spread over a piece of fabric, which shows, after the treatment, piezoresistive properties, i.e., a change in resistance when it is strained. This property is exploited to realize sensorized garments such as gloves, leotards, and seat covers capable of reconstructing and monitoring body shape, posture, and gesture. In general, this technology is a good candidate for adherent wearable systems with excellent mechanical coupling with body surface. Here, we mainly focused on a sensorized glove able to detect posture and movements of the fingers. It could be used in several fields of application. We report on experimental results of a sensorized glove used as movements recorder for rehabilitation therapies and medicine. Furthermore, we describe a dedicated methodology used to read the output sensors which allowed to avoid using metallic wires for the connections. The price to be paid for all these advantages is a nonlinear electric response of the fabric sensor and a too long settling time, that in principle, make these sensors not suitable for real-time applications. Here we propose a hardware and computational solution to overcome this limitation.

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).

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

Strain sensing fabric characterization

Electrically conductive elastomer (CE) composites show piezoresistive properties when a deformation is applied. In several applications, CE can be integrated into fabric or into other flexible substrates and can be employed as a strain sensor. Moreover, integrated CE sensors may be used in biomechanical analysis to realize wearable kinesthetic interfaces able to detect the posture and movement of a subject. Unfortunately, the long transient time (up to several minutes) and some peculiar non-linear phenomena in CE require a complex treatment of signals which is described in the present work.

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.

Microfabricated electroactive carbon nanotube actuators

A variety of microfabrication techniques have been developed at the University of Pisa. They are based either on pressure or piston actuated microsyringes or modified ink-jet printers. This work present the results of a study aimed at fabricating carbon nanotube (NT) actuators using micro-syringes. In order to prevent the nanotubes from aggregating into clumps, they were enclosed in a partially cross-linked polyvinylalcohol - polyallylamine matrix. After sonication the solution remained homogenously dispersed for about 40 minutes, which was sufficient time for deposition. Small strips of NT, about 5 mm across and 15 mm long were deposited. Following deposition, the films were baked at 80 degree(s)C and their thickness, impedance and mechanical resistance measured. The results indicate that 50 minutes of baking time is sufficient to give a constant resistivity of 1.12 x 10-2 (Omega) m per layer similar to a typical semiconductor, and each layer has a thickness of about 6 micrometers .

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.

Wearable monitoring of lumbar spine curvature by inertial and e-textile sensory fusion

This paper describes the design, the development and the preliminary testing of a wearable system able perform a real time estimation of the local curvature and the length of the spine lumbar arch. The system integrate and fuse information gathered from textile based piezoresistive sensor arrays and tri-axial accelerometers. E-textile strain sensing garments suffer from non-linearities, hysteresis and long transient, while accelerometers, used as inclinometers, present biased values and are affected by the system acceleration due to subject movements. In this work, focused on the wearability and comfort of the user, we propose a fusion of the information deriving from the two class of sensors to reduce their intrinsic errors affecting measurements. Comparative evaluation of system performances with stereophotogrammetric techniques shows a 2% error in lumbar arch length reconstruction.