Search

Textile-based transducers are an innovative category of devices that use conductive fibres meshed with elastic textile fabrics. Within this paper, a new class of strain sensors, which represents an excellent trade-off between figures of merit in mechano-electrical transductionand possibility of integration in textiles, is presented. Electrically conductive elastomer composites show piezo-resistive properties when a deformation is applied. Conductive elastomer can be applied to fabric or to other flexible substrate and they can be employed as strain sensors. We integrated conductive elastomer sensors into fabrics to realize wearable kinaesthetic garments able to detect posture and movement of a user. This paper deals with the design, the development and realization of a set of sensing garments, from the characterization of innovative textile-based sensors to the methodologies employed to gather information on the posture and movement from the entire garments. Data deriving from the prototypes are analysed and compared with those deriving from a traditional movement tracking system. The realized kinaesthetic garments have shown very promising performance in terms of body segment position reconstruction and posture classification. [ABSTRACT FROM AUTHOR]

Posture and gesture analysis, together with the monitoring of body kinematics, is a field of increasing interest in bioengineering and several connected disciplines. In this paper, some typical features of distributed sensing systems are described, as well as a methodology to read signals from such systems. Theory, simulation, results, and some specific applications are shown. Strain gauges have been used as sensors and have been deposited directly onto textile fibers, demonstrating one way to realize a wearable sensor system. [ABSTRACT FROM PUBLISHER]

In recent years, an innovative technology based on polymeric conductors and semiconductors has undergone rapid growth. These materials offer several advantages with respect to metals and inorganic conductors: lightness, large elasticity and resilience, resistance to corrosion, flexibility, impact strength, etc. These properties are suitable for implementing wearable devices. In particular, a sensitive glove able to detect the position and the motion of fingers and a sensorized leotard have been developed. Here, the characterization of the strain-sensing fabric is presented. In the first section, the polymerization process used to realize the strain sensor is described. Then, the thermal and mechanical transduction properties of the strain sensor are investigated and a geometrical parameter to invariantly codify the sensor response during aging is proposed. Finally, a brief outline of ongoing applications is reported. [ABSTRACT FROM PUBLISHER]

Abstract Background Monitoring 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. Methods This 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. Results Data obtained by means of the sensing garment are analyzed and compared with the ones recorded using a traditional movement tracking system. Conclusion The 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.

Background: Patient-specific performance assessment of arm movements in daily life activities is fundamental for neurological rehabilitation therapy. In most applications, the shoulder movement is simplified through a socket-ball joint, neglecting the movement of the scapular-thoracic complex. This may lead to significant errors. We propose an innovative bi-articular model of the human shoulder for estimating the position of the hand in relation to the sternum. The model takes into account both the scapular-toracic and gleno-humeral movements and their ratio governed by the scapular-humeral rhythm, fusing the information of inertial and textile-based strain sensors., Method: To feed the reconstruction algorithm based on the bi-articular model, an ad-hoc sensing shirt was developed. The shirt was equipped with two inertial measurement units (IMUs) and an integrated textile strain sensor. We built the bi-articular model starting from the data obtained in two planar movements (arm abduction and flexion in the sagittal plane) and analysing the error between the reference data - measured through an optical reference system - and the socket-ball approximation of the shoulder. The 3D model was developed by extending the behaviour of the kinematic chain revealed in the planar trajectories through a parameter identification that takes into account the body structure of the subject., Result: The bi-articular model was evaluated in five subjects in comparison with the optical reference system. The errors were computed in terms of distance between the reference position of the trochlea (end-effector) and the correspondent model estimation. The introduced method remarkably improved the estimation of the position of the trochlea (and consequently the estimation of the hand position during reaching activities) reducing position errors from 11.5 cm to 1.8 cm., Conclusion: Thanks to the developed bi-articular model, we demonstrated a reliable estimation of the upper arm kinematics with a minimal sensing system suitable for daily life monitoring of recovery.

Monitoring physical activities during post-stroke rehabilitation in daily life may help physicians to optimize and tailor the training program for patients. The European research project INTERACTION (FP7-ICT-2011-7-287351) evaluated motor capabilities in stroke patients during the recovery treatment period. We developed wearable sensing platform based on the sensor fusion among inertial, knitted piezoresistive sensors and textile EMG electrodes. The device was conceived in modular form and consists of a separate shirt, trousers, glove, and shoe. Thanks to the novel fusion approach it has been possible to develop a model for the shoulder taking into account the scapulo-thoracic joint of the scapular girdle, considerably improving the estimation of the hand position in reaching activities. In order to minimize the sensor set used to monitor gait, a single inertial sensor fused with a textile goniometer proved to reconstruct the orientation of all the body segments of the leg. Finally, the sensing glove, endowed with three textile goniometers and three force sensors showed good capabilities in the reconstruction of grasping activities and evaluating the interaction of the hand with the environment, according to the project specifications. This paper reports on the design and the technical evaluation of the performance of the sensing platform, tested on healthy subjects.

Human motion analysis is crucial for a wide range of applications and disciplines. The development and validation of low cost and unobtrusive sensing systems for ambulatory motion detection is still an open issue. Inertial measurement systems and e-textile sensors are emerging as potential technologies for daily life situations. We developed and conducted a preliminary evaluation of an innovative sensing concept that combines e-textiles and tri-axial accelerometers for ambulatory human motion analysis. Our sensory fusion method is based on a Kalman filter technique and combines the outputs of textile electrogoniometers and accelerometers without making any assumptions regarding the initial accelerometer position and orientation. We used our technique to measure the flexion-extension angle of the knee in different motion tasks (monopodalic flexions and walking at different velocities). The estimation technique was benchmarked against a commercial measurement system based on inertial measurement units and performed reliably for all of the various tasks (mean and standard deviation of the root mean square error of 1:96 and 0:96, respectively). In addition, the method showed a notable improvement in angular estimation compared to the estimation derived by the textile goniometer and accelerometer considered separately. In future work, we will extend this method to more complex and multi-degree of freedom joints.

This paper presents an innovative wearable kinesthetic glove realized with knitted piezoresistive fabric (KPF) sensor technology. The glove is conceived to capture hand movement and gesture by using KPF in a double-layer configuration working as angular sensors (electrogoniometers). The sensing glove prototype is endowed by three KPF goniometers, used to track flexion and extension movement of metacarpophalangeal joint of thumb, index, and middle fingers. The glove is devoted to the continuous monitoring of patients during their daily-life activities, in particular for stroke survivors during their rehabilitation. The prototype performances have been evaluated in comparison with an optical tracking system considered as a gold standard both for relieving static and dynamic posture and gesture of the hand. The introduced prototype has shown very interesting figures of merit. The angular error, evaluated through the standard Bland Altman analysis, has been estimated in ±3° which is slightly less accurate than commercial electrogoniometers. Moreover, a new conceptual prototype design, preliminary evaluated within this study, is presented and discussed in order to solve actual limitations in terms of number and type of sensor connections, avoiding mechanical constraints given by metallic inextensible wires and improving user comfort.

Background: Monitoring joint angles through wearable systems enables human posture and gesture to be reconstructed as a support for physical rehabilitation both in clinics and at the patient's home. A new generation of wearable goniometers based on knitted piezoresistive fabric (KPF) technology is presented., Methods: KPF single-and double-layer devices were designed and characterized under stretching and bending to work as strain sensors and goniometers. The theoretical working principle and the derived electromechanical model, previously proved for carbon elastomer sensors, were generalized to KPF. The devices were used to correlate angles and piezoresistive fabric behaviour, to highlight the differences in terms of performance between the single layer and the double layer sensors. A fast calibration procedure is also proposed., Results: The proposed device was tested both in static and dynamic conditions in comparison with standard electrogoniometers and inertial measurement units respectively. KPF goniometer capabilities in angle detection were experimentally proved and a discussion of the device measurement errors of is provided. The paper concludes with an analysis of sensor accuracy and hysteresis reduction in particular configurations., Conclusions: Double layer KPF goniometers showed a promising performance in terms of angle measurements both in quasi-static and dynamic working mode for velocities typical of human movement. A further approach consisting of a combination of multiple sensors to increase accuracy via sensor fusion technique has been presented.

The objective of the INTERACTION Eu project is to develop and validate an unobtrusive and modular system for monitoring daily life activities, physical interactions with the environment and for training upper and lower extremity motor function in stroke subjects. This paper describes the development and preliminary testing of the project sensing platform made of sensing shirt, trousers, gloves and shoes. Modular prototypes were designed and built considering the minimal set of inertial, force and textile sensors that may enable an efficient monitoring of stroke patients. The single sensing elements are described and the results of their preliminary lab-level testing are reported.

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.

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.

Conductive elastomers are a novel strain sensing technology which can be unobtrusively embedded into a garment's fabric, allowing a new type of sensorized cloths for motion analysis. A possible application for this technology is remote monitoring and control of motor rehabilitation exercises. The present work describes a sensorized shirt for upper limb posture recognition. Supervised learning techniques have been employed to compare classification models for the analysis of strains, simultaneously measured at multiple points of the shirt. The instantaneous position of the limb was classified into a finite set of predefined postures, and the movement was decomposed in an ordered sequence of discrete states. The amount of information given by the observation of each sensor during the execution of a specific exercise was quantitatively estimated by computing the information gain for each sensor, which in turn allows the data-driven optimization of the garment. Real-time feedback on exercise progress can also be provided by reconstructing the sequence of consecutive positions assumed by the limb.

Kinesthetic and haptic interfaces between humans and machines are currently under development in a truly wearable form, using innovative technologies based on electroactive polymers. The integration of electroactive polymeric materials into wearable garments is becoming a viable mean to confer the garment strain sensing and actuation properties. In this paper, the implementation and testing of fabric-based wearable interfaces for the upper limb endowed with spatially redundant strain sensing are reported. Electroactive polymer actuators, which we are currently investigating, are discussed with emphasis given to their unique capabilities in the phenomenological mimicking of skeletal muscle actuation and control. Finally, current work in preliminary evaluation of prototypes in the field of post-stroke rehabilitation is also briefly presented.

Stroke and other neurological accidents account for a wide fraction of the healthcare costs in industrialised societies. The last step in the chain of recovery from a neurological event often includes motor rehabilitation. While current motion-sensing technologies are inadequate for automated monitoring of rehabilitation exercises at home, conductive elastomers are a novel strain-sensing technology which can be embedded unobtrusively into a garment's fabric. A sensorized garment was realized to simultaneously measure the strains at multiple points of a shirt covering the thorax and upper limb. Supervised learning techniques were employed to analyse the strain measures in order to reconstruct upper-limb posture and provide real-time feedback on exercise progress.

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.

BACKGROUND: Monitoring 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. METHODS: This 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. RESULTS: Data obtained by means of the sensing garment are analyzed and compared with the ones recorded using a traditional movement tracking system. CONCLUSION: The 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.

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. In the present work a collection of innovative and unobtrusive garments able to detect the posture and the movement of the human body are introduced. 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 the posture and movement. Several new algorithms devoted to the device operation are presented and tested. Data derived from the sensing garment are analyzed and compared with the data derived from a traditional movement tracking system.

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.

Monitoring body kinematics and analyzing posture and gesture is an area of major importance in bioengineering and several other connected disciplines such as rehabilitation, sport medicine and ergonomics. Recent developments of new smart materials consent the realization of a new generation of garments with distributed sensors. What we present here is a sensing glove able to detect the posture and movements of the hand.

Artificial sensory motor systems are now under development in a truly wearable form using an innovative technology based on electroactive polymers. The integration of electroactive polymeric materials into wearable garments endorses them with strain sensing and mechanical actuation properties. The methodology underlying the design of haptic garments has necessarily to rely on knowledge of biological perceptual and motor processes which is, however, scattered and fragmented. Notwithstanding, the combined use of new polymeric electroactive materials in the form of fibers and fabrics with emerging concepts of biomimetic nature in sensor data analysis, pseudomuscular actuator control and biomechanical design may not only provide new avenues toward the realization of truly wearable kinesthetic and haptic interfaces, but also clues and instruments to better comprehend human manipulative and gestual functions. In this talk the conception, early stage implementation and preliminary testing of a fabric-based wearable interface endowed with spatially redundant strain sensing and distributed actuation are illustrated with reference to a wearable upper limb artificial kinesthesia system, intended to be used in telerehabilitation of post stroke patient.