Low-Cost Recognition of Plastic Waste Using Deep Learning and a Multi-Spectral Near-Infrared Sensor.

This work presents an approach for the recognition of plastics using a low-cost spectroscopy sensor module together with a set of machine learning methods. The sensor is a multi-spectral module capable of measuring 18 wavelengths from the visible to the near-infrared. Data processing and analysis are performed using a set of ten machine learning methods (Random Forest, Support Vector Machines, Multi-Layer Perceptron, Convolutional Neural Networks, Decision Trees, Logistic Regression, Naive Bayes, k-Nearest Neighbour, AdaBoost, Linear Discriminant Analysis). An experimental setup is designed for systematic data collection from six plastic types including PET, HDPE, PVC, LDPE, PP and PS household waste. The set of computational methods is implemented in a generalised pipeline for the validation of the proposed approach for the recognition of plastics. The results show that Convolutional Neural Networks and Multi-Layer Perceptron can recognise plastics with a mean accuracy of 72.50% and 70.25%, respectively, with the largest accuracy of 83.5% for PS plastic and the smallest accuracy of 66% for PET plastic. The results demonstrate that this low-cost near-infrared sensor with machine learning methods can recognise plastics effectively, making it an affordable and portable approach that contributes to the development of sustainable systems with potential for applications in other fields such as agriculture, e-waste recycling, healthcare and manufacturing.

Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends.

Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.

A Frequency Modulation-Based Taxel Array: A Bio-Inspired Architecture for Large-Scale Artificial Skin.

This work introduces an array prototype based on a Frequency Modulation (FM) encoding architecture to transfer multiple sensor signals on a single wire. The use case presented adopts Hall-effect sensors as an example to represent a much larger range of sensor types (e.g., proximity and temperature). This work aims to contribute to large area artificial skin systems which are a key element to enhance robotic platforms. Artificial skin will allow robotic platforms to have spatial awareness which will make interaction with objects and users safe. The FM-based architecture has been developed to address limitations in large-scale artificial skin scalability. Scalability issues include power requirements; number of wires needed; as well as frequency, density, and sensitivity bottlenecks. In this work, eight sensor signals are simultaneously acquired, transferred on a single wire and decoded in real-time. The overall taxel array current consumption is 36 mA. The work experimentally validates and demonstrates that different input signals can be effectively transferred using this approach minimizing wiring and power consumption of the taxel array. Four different tests using single as well as multiple stimuli are presented. Observations on performances, noise, and taxel array behaviour are reported. The results show that the taxel array is reliable and effective in detecting the applied stimuli.

A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing.

The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing tasks. An intriguing possibility for the control of complex robotic systems would be to develop an artificial cerebellar chip with multiple zones that could be similarly connected to a variety of subsystems to optimize performance. The novel aim of this paper, therefore, is to propose and investigate a multizone cerebellar chip applied to a range of tasks in robot adaptive control and sensorimotor processing. The multizone cerebellar chip was evaluated using a custom robotic platform consisting of an array of tactile sensors driven by dielectric electroactive polymers mounted upon a standard industrial robot arm. The results demonstrate that the performance in each task was improved by the concurrent, stable learning in each cerebellar zone. This paper, therefore, provides the first empirical demonstration that a synthetic, multizone, cerebellar chip could be embodied within existing robotic systems to improve performance in a diverse range of tasks, much like the cerebellum in a biological system.

Tactile Signatures and Hand Motion Intent Recognition for Wearable Assistive Devices.

Within the field of robotics and autonomous systems where there is a human in the loop, intent recognition plays an important role. This is especially true for wearable assistive devices used for rehabilitation, particularly post-stroke recovery. This paper reports results on the use of tactile patterns to detect weak muscle contractions in the forearm while at the same time associating these patterns with the muscle synergies during different grips. To investigate this concept, a series of experiments with healthy participants were carried out using a tactile arm brace (TAB) on the forearm while performing four different types of grip. The expected force patterns were established by analysing the muscle synergies of the four grip types and the forearm physiology. The results showed that the tactile signatures of the forearm recorded on the TAB align with the anticipated force patterns. Furthermore, a linear separability of the data across all four grip types was identified. Using the TAB data, machine learning algorithms achieved a 99% classification accuracy. The TAB results were highly comparable to a similar commercial intent recognition system based on a surface electromyography (sEMG) sensing.

Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomer-based artificial muscle.

Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training.

Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

Seeing by touch: evaluation of a soft biologically-inspired artificial fingertip in real-time active touch.

Effective tactile sensing for artificial platforms remains an open issue in robotics. This study investigates the performance of a soft biologically-inspired artificial fingertip in active exploration tasks. The fingertip sensor replicates the mechanisms within human skin and offers a robust solution that can be used both for tactile sensing and gripping/manipulating objects. The softness of the optical sensor's contact surface also allows safer interactions with objects. High-level tactile features such as edges are extrapolated from the sensor's output and the information is used to generate a tactile image. The work presented in this paper aims to investigate and evaluate this artificial fingertip for 2D shape reconstruction. The sensor was mounted on a robot arm to allow autonomous exploration of different objects. The sensor and a number of human participants were then tested for their abilities to track the raised perimeters of different planar objects and compared. By observing the technique and accuracy of the human subjects, simple but effective parameters were determined in order to evaluate the artificial system's performance. The results prove the capability of the sensor in such active exploration tasks, with a comparable performance to the human subjects despite it using tactile data alone whereas the human participants were also able to use proprioceptive cues.

Design and development of a sensorized wireless toy for measuring infants' manual actions.

The development of grasping is an important milestone that infants encounter during the first months of life. Novel approaches for measuring infants' manual actions are based on sensorized platform usable in natural settings, such as instrumented wireless toys that could be exploited for diagnosis and rehabilitation purposes. A new sensorized wireless toy has been designed and developed with embedded pressure sensors and audio-visual feedback. The fulfillment of clinical specifications has been proved through mechanical and electrical characterization. Infants showed a good grade of acceptance to such kind of tools, as confirmed by the results of preliminary tests that involved nine healthy infants: the dimensions fulfill infants' anthropometrics, the device is robust and safe, the acquired signals are in the expected range and the wireless communication is stable. Although achieved only through preliminary tests, such results confirm the hypothesis that this typology of instrumented toys could be useful for quantitative monitoring and measuring infants' motor development and ready to be evaluated for assessing motor skills through appropriate clinical trials.

Wearable and implantable pancreas substitutes.

A lifelong-implanted and completely automated artificial or bioartificial pancreas (BAP) is the holy grail for type 1 diabetes treatment, and could be a definitive solution even for other severe pathologies, such as pancreatitis and pancreas cancer. Technology has made several important steps forward in the last years, providing new hope for the realization of such devices, whose feasibility is strictly connected to advances in glucose sensor technology, subcutaneous and intraperitoneal insulin pump development, the design of closed-loop control algorithms for mechatronic pancreases, as well as cell and tissue engineering and cell encapsulation for biohybrid pancreases. Furthermore, smart integration of the mentioned components and biocompatibility issues must be addressed, bearing in mind that, for mechatronic pancreases, it is most important to consider how to recharge implanted batteries and refill implanted insulin reservoirs without requiring periodic surgical interventions. This review describes recent advancements in technologies and concepts related to artificial and bioartificial pancreases, and assesses how far we are from a lifelong-implanted and self-working pancreas substitute that can fully restore the quality of life of a diabetic (or other type of) patient.

A novel strategy for long-term implantable artificial pancreas.

Technology has recently changed type 1 diabetes treatment by introducing several advancements able to improve patients' quality of life. However, despite of several decades of research efforts, the dream of a fully-automated implanted artificial pancreas is quite far from its realization. The need for periodically restoring the implanted battery charge and refilling the implanted insulin reservoir are the main issues, for which invasive surgery, transcutaneous catheters or external portable devices are presently the only solutions. In this paper we propose a novel approach to these issues, describing a totally implanted closed-loop artificial pancreas with a wireless battery charger and a non-invasive strategy for insulin refilling, based on sensorized swallowable "insulin carrier" capsules. Such system has the potential to represent a final solution for diabetes treatment, by fully restoring patients' quality of life.