A 10-17 DOF Sensory Gloves with Harvesting Capability for Smart Healthcare

We here present a 10-17 Degrees of Freedom (DoF) sensory gloves for Smart Healthcare implementing an energy harvesting architecture, aimed at enhancing the battery lasting when powering the electronics of the two different types of gloves, used to sense fingers movements. In particular, we realized a comparison in terms of measurement repeatability and reliability, as well as power consumption and battery lasting, between two sensory gloves implemented by means of different technologies. The first is a 3D printed glove with 10 DoF, featuring low-cost, low-effort fabrication and low-power consumption. The second is a classical Lycra® glove with 14 DoF suitable for a more detailed assessment of the hand postures, featuring a relatively higher cost and power consumption. An electronic circuitry was designed to gather and elaborate data from both types of sensory gloves, differing for number of inputs only. Both gloves are equipped with flex sensors and in addiction with the electronics (including a microcontroller and a transmitter) allow the control of hand virtual limbs or mechanical arts in surgical, military, space and civil applications.Six healthy subjects were involved in tests suitable to evaluate the performances of the proposed gloves in terms of repeatability, reproducibility and reliability. Particular effort was devoted to increase battery lasting for both glove-based systems, with the electronics relaying on Radio Frequency, Piezoelectric and Thermoelectric harvesters. The harvesting part was built and tested as a prototype discrete element board, that is interfaced with an external microcontroller and a radiofrequency transmitter board. Measurement results demonstrated a meaningful improvement in battery operation time up to 25%, considering different operating scenarios

A novel single-bias ultra-wideband monolithic pulse amplifier

A single-bias monolithic pulse amplifier realised in standard 0.5um GaAs MESFET technology is presented. The amplifier features broadband operation from quasi-DC to 5.5 Ghz. Measured performances of the stage include 55 ps rise time, together with a variable gain ranging from 18 to 5 dB depending on the imposed bias. Input-output match is better than 12 dB on the entire bandwidth

Shaping Resistive Bend Sensors to Enhance Readout Linearity

Resistive bend sensors have been increasingly used in different areas for their interesting property to change their resistance when bent. They can be employed in those systems where a joint rotation has to be measured, in particular biomedical systems, to measure human joint static and dynamic postures. In spite of their interesting properties, such as robustness, low price, and long life, the commercial bend sensors have a response which is not actually linear, as an electronic device to measure bend angles should be, to recover human posture without distortion. In this work, different interfaces for sensor device readout were analyzed and compared from the output response linearity point of view. In order to obtain a sensor characteristic as closer as possible to the ideal linear one, a way to calculate the sensor characteristic with a generalized resistive strip contour, starting from an empiric sheet resistance model, was developed, in order to find what is the more suitable nonuniform geometry

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 cas

Modeling and comparing the linear performance of non-uniform geometry bend sensors

Resistive bend sensors have been increasingly used in different areas for their interesting property to change their resistance when bent. They can be employed in those systems where a joint rotation has to be measured, such as in biomedical systems to measure human joint static and dynamic postures. In spite of their interesting properties the commercial bend sensors have a resistance vs. bent angle characteristic which is not actually ideal as a linear function, to measure bend angles, would be. In this work, we have developed a way to calculate the sensor resistance for different bending angles with a generalized strip contour, in order to predict how shaping it with different non-uniform geometries changes the resistance dependence on bending angles, and investigate what kind of strip geometry can lead to a more linear behavior

A Neural Network that helps building a Nonlinear Dynamical model of a Power Amplifier

Abstract. This paper presents a new neural network-based model that can be applied to characterize the nonlinear dynamical behavior of power amplifiers. We use a time-delayed feed-forward neural network to make an input-output timedomain characterization, that can provide also an analytical expression (as a Volterra Series model) to predict the amplifier response to multiple power levels. Simulation results that validate our proposal are presented.