Alessandro Tognetti

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.

SoC CMOS UWB Pulse Radar Sensor for Contactless Respiratory Rate Monitoring

An ultra wideband (UWB) system-on-chip radar sensor for respiratory rate monitoring has been realized in 90 nm CMOS technology and characterized experimentally. The radar testchip has been applied to the contactless detection of the respiration activity of adult and baby. The field operational tests demonstrate that the UWB radar sensor detects the respiratory rate of person under test (adult and baby) associated with sub-centimeter chest movements, allowing the continuous-time non-invasive monitoring of hospital patients and other people at risk of obstructive apneas such as babies in cot beds, or other respiratory diseases.

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).

Body segment position reconstruction and posture classification by smart textiles

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 transduction and 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.

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

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%.

New generation of wearable goniometers for motion capture systems

BackgroundMonitoring 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.MethodsKPF 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.ResultsThe 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.ConclusionsDouble 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.

Electroactive fabrics for distributed, conformable and interactive systems

Posture and gesture analysis and body kinematics monitoring is a field of increasing interest in bioengineering and several connected disciplines. We propose wearable systems able to read and record posture and movements of a subject wearing them. We used strain gage sensors, deposited directly onto textile fibers realizing, in contrast with different strategies, truly wearable and unintrusive systems.

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.