As the technology moves towards more human-like bionic limbs, it is necessary to develop a feedback system that provides active touch feedback to a user of a prosthetic hand. Most of the contemporary sensory substitution methods comprise simple position and force sensors combined with few discrete stimulation units, and hence they are characterized with a limited amount of information that can be transmitted by the feedback. The present study describes a novel system for tactile feedback integrating advanced multipoint sensing (electronic skin) and stimulation (matrix electrodes). The system comprises a flexible sensing array (16 sensors) integrated on the index finger of a Michelangelo prosthetic hand mockup, embedded interface electronics and multichannel stimulator connected to a flexible matrix electrode (24 pads). The developed system conveys contact information (binary detections) to the user. To demonstrate the feasibility, the system was tested in six able-bodied subjects who were asked to recognize static patterns (contact position) with two different spatial resolutions and dynamic movement patterns (i.e., sliding along and/or across the finger) presented on the electronic skin. The experiments demonstrated that the system successfully translated the mechanical interaction into electrotactile profiles, which the subjects could recognize with good performance. The success rates (mean $\pm$ standard deviation) for the static patterns were 91 $\pm$ 4% and 58 $\pm$ 10% for low and high spatial resolution, respectively, while the success rate for sliding touch was 94 $\pm$ 4%. These results demonstrate that the developed system is an important step towards a new generation of tactile feedback interfaces that can provide high-bandwidth connection between the user and his/her bionic limb. Such systems would allow mimicking spatially distributed natural feedback, thereby facilitating the control and embodiment of the artificial device into the user body scheme.

Embedding Machine Learning enables integrating intelligence in recent application domains such as Internet of Things, portable healthcare systems, and wearable devices. This paper presents an assessment of approximate computing methods at algorithmic, architecture, and circuit levels and draws perspectives for further developments and applications. The main goal is to investigate how approximate computing may reduce the complexity and enable the feasibility of embedded Machine Learning (ML) systems. Though ML is a powerful paradigm for applications in the perceptual domain (i.e. vision, touch, hearing, etc.), their computational complexity is very high and consequently real time operation and ultra-low power are still very challenging objectives. On the other hand, approximate computing has emerged as an effective solution to reduce hardware complexity, time latency and to increase energy efficiency.

Tactile sensors are widely employed to enable the sense of touch for applications such as robotics and prosthetics. In addition to the selection of an appropriate sensing material, the performance of the tactile sensing system is conditioned by its interface electronic system. On the other hand, due to the need to embed the tactile sensing system into a prosthetic device, strict requirements such as small size and low power consumption are imposed on the system design. This paper presents the experimental assessment and characterization of an interface electronic system for piezoelectric tactile sensors for prosthetic applications. The interface electronic is proposed as part of a wearable system intended to be integrated into an upper limb prosthetic device. The system is based on a low power arm-microcontroller and a DDC232 device. Electrical and electromechanical setups have been implemented to assess the response of the interface electronic with PVDF-based piezoelectric sensors. The results of electrical and electromechanical tests validate the correct functionality of the proposed system.

This paper presents a tactile sensing glove based on piezoelectric sensors, embedded electronics, and a machine learning-based approach for texture discrimination. Various time and frequency features were extracted and evaluated through machine learning algorithms including support vector machines, Single-layer Feed-Forward Neural Network, and 1-D Convolution Neural Network. Six naturalistic surface textures were explored by simply sliding the index finger of seven participants on the surfaces with and without the tactile glove. Results showed that by employing an SVM classifier trained on both time and frequency features, a discrimination accuracy of 87% is achieved while utilizing only two sensors. Furthermore, this discrimination behavior was found to exceed the participants (76.79%) when attempting to discriminate the textures using their somatosensory system. This study demonstrated the capability of the tactile sensing glove in extracting tactile information opening up interesting perspectives for wearable feedback systems for post-stroke rehabilitation.

Several algorithms are currently provided to alleviate effects of range overlaid echoes and velocity aliasing in the radar. In this paper, a novel and simple, yet very effective method based on Barker code is presented to solve the range ambiguity problem in high PRF radars. The novelty of this algorithm is derived from the fact that each transmitted pulse is coded with a different circular shifted version of Barker code of length four. Encoding the transmitted pulses in such a way provides the receiver of the potential to resolve up to four overlaid pulses. The direct consequence of this capability is the augmentation of the unambiguous range up to four times. On the top of that, the traditional goal of using the Barker code namely, enhancing the range resolution of the radar is still maintained. To prove the superiority of our algorithm over the existing algorithms in the literature, we conduct some simulations in the context of weather radars. The results indicate obviously that our algorithm outperforms the SZ phase coding technique. The latter has been considered as the one that yields the best performance among its competitors within the family of phase coding techniques.

This paper presents a wearable system prototype based on commercial off-the-shelf components for sensory substitution for prosthetics. It provides a wireless portable interface electronics for tactile sensor array. The system transmits the acquired tactile information to the prosthetic user through Electrotactile stimulation. An experimental setup has been carried out to validate the proposed prototype: results demonstrate the correct functionality when different input stimuli have been tested and conveyed to the stimulator. The proposed system overcomes similar state of art solutions dealing with higher number of input channels maintaining the real time functionality.