Giovanni Saggio

Resistive flex sensors: a survey

Resistive flex sensors can be used to measure bending or flexing with relatively little effort and a relativelylow budget. Their lightness, compactness, robustness, measurement effectiveness and low power consumption make these sensors useful for manifold applications in diverse fields. Here, we provide a comprehensive survey of resistive flex sensors, taking into account their working principles, manufacturing aspects, electrical characteristics and equivalent models, useful front-end conditioning circuitry, and physic-bio-chemical aspects. Particular effort is devoted to reporting on and analyzing several applications of resistive flex sensors, related to the measurement of body position and motion, and to the implementation of artificial devices. In relation to the human body, we consider the utilization of resistive flex sensors for the measurement of physical activity and for the development of interaction/interface devices driven by human gestures. Concerning artificial devices, we deal with applications related to the automotive field, robots, orthosis and prosthesis, musical instruments and measuring tools. The presented literature is collected from different sources, including bibliographic databases, company press releases, patents, master’s theses and PhD theses.

Long term measurement of human joint movements for health care and rehabilitation purposes

This paper presents a novel human joint motion recording method, The recorded data are sent to the receiver, which is placed in the close proximity or in the same room with the patient, via a wireless short-range communications system that guarantees 3 days of battery life. This method exploits commercially available bend sensors to convert mechanical human joint movements into electric signals which are then acquired, pre-processed, wireless transmitted and post-processed. We propose a novel way of sensors application, underlying advantages and drawbacks which could be drastically reduced by electronic circuitry anyway. The network configuration and the specific air interface are chosen to satisfy system requirements in terms of data rates, battery autonomy, and mobility.

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

A novel application method for wearable bend sensors

Bend sensors fundamental characteristic is to furnish an electrical resistance value related to the angle they are bent. This feature can be successfully exploited to realize wearable systems capable to measure human static and dynamic postures. In particular some efforts have been made to determine finger joint movements of human hands and it has been demonstrated the feasibility of using the so called data glove system as a goniometric device. The repeatability of such system is quite good for general purposes but it is still not sufficient for specific applications (for instance in virtual surgery). So here we introduce a novel application method of bend sensors and demonstrate how it can be useful to improve the system repeatability.

Wireless data glove system developed for HMI

Human Machine Interfaces support users to interact or simply control any kind of devices founded on machinery basis. Very simple and common interfaces are represented by the mouse and keyboard tools by which a user interact with the personal computer “machine”. It is however evident how these tools can be particularly “limited” since they “act” only in a 2D superficial environment and cannot provide an immersive experience. So in the latter years new kind of interfaces have been investigated in order to expand the user capabilities in a 3D space, then increasing the realism degree too. In this paper we deal with a new kind of these interfaces. In fact we developed a sensorized glove capable to measure all human hand Degree of Freedom (DoF), “translating” them into commands for personal computers.

Mechanical modeling of bend sensors exploited to measure human joint movements

Flexibility, lightness, wearability and cheapness are the most important features for a successful adoption of bend sensors, being they fundamental elements to realize systems able to convert static positions and movements into electrical signals. Even though these sensors have been employed for many different applications, the focal aspect of their mechanical modeling is still not adequately considered. For such a reason the aim of this paper is to fill a lack concerning the method of bend sensors mechanical characterization and modeling. The results have been exploited to realize an instrumented glove able to measure finger joints movements.

A fuzzy integral ensemble method in visual P300 Brain-Computer Interface

We evaluate the possibility of application of combination of classifiers using fuzzy measures and integrals to Brain-Computer Interface (BCI) based on electroencephalography. In particular, we present an ensemble method that can be applied to a variety of systems and evaluate it in the context of a visual P300-based BCI. Offline analysis of data relative to 5 subjects lets us argue that the proposed classification strategy is suitable for BCI. Indeed, the achieved performance is significantly greater than the average of the base classifiers and, broadly speaking, similar to that of the best one. Thus the proposed methodology allows realizing systems that can be used by different subjects without the need for a preliminary configuration phase in which the best classifier for each user has to be identified. Moreover, the ensemble is often capable of detecting uncertain situations and turning them from misclassifications into abstentions, thereby improving the level of safety in BCI for environmental or device control.

Characterization of piezoresistive sensors for goniometric glove in hand prostheses

Piezoresistive sensors can be successfully adopted in many field where bend angles need to be measured. In particular, attention must be paid for increase the sensor numbers, applied to body-sample, which give parallel information on the movement activity. The possibility to capture information from the sensors adopting wireless technology can allow the increasing of sensor number, and the removing of wire ties between sensors and the central processor unit in normal human motion. We utilized these sensors to develop an instrumented glove to measure human joint fingers. The great advantage in applications of piezoresistive sensors rely on their pliability, sensitivity and cheapness. In any case, for effective results, it is mandatory a complete electrical sensor characterization, which lacks in literature and therefore it is the aim of this work.

Modeling Wearable Bend Sensor Behavior for Human Motion Capture

The possibilities offered by variable resistance bend sensors, applied as wearable devices on body garments, to recover human joint bend angles for body segment movement tracking, have been investigated, underlying their advantages and drawbacks in real-time applications. Due to their pliability, sensitivity, and cheapness, they could be a valid alternative to movement analysis systems, based on optoelectronic devices or inertial electronic sensors. This paper suggests a new method for sensor characterization under fast bend and extension movements, to extract few parameters of a synthetic model, which provide to the users the chance to foresee their electrical performance in different applications. The sensor and their extracted models were applied to register the human knee rotation during a gait cycle, either at slow speed (83 deg/s) for a walking pattern at 5 km/h, and at high speed (650 deg/s) for a running pattern of a sprinter at 10 m/s, and finally the finger joint rotations at their maximum angular velocity (900 deg/s). This was done for a twofold purpose: from one hand, to assess the model capability to predict the sensor performance, tracking human body segment rotations at different speed, without the need of measurement; from the other hand, to recover in real time the actual sensor rotation from its resistance measurement, especially in high speed applications, where its response is distorted. With this technique, the mean error decreases from 22.5° to 3.7° in the worst case.

Electrical resistance profiling of bend sensors adopted to measure spatial arrangement of the human body

Here we report some techniques we adopted to electrically characterize some commercially available bend sensors, in terms of their resistance variations when curved or angular shaped. This study has the aim of a correct exploitation of the bend sensors in order to adopt them for proper measures of the static postures and kinematics of the total human body, in regards for both the trunk and the limbs