A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies.

This paper proposes the use of soft materials for building robotic grippers for delicate and safe interactions. The work includes concept design, fabrication and first assessment and characterization of the proposed soft gripper, a novel robotic end-effector entirely made up of elastomeric material. As a significant case study, it has been specifically adapted as a grasping tool in Minimally Invasive Surgery, but its design has been conceived in such a way that its dimension can be easily scaled, to find application in all those fields where a safe interaction with fragile items or human co-workers is needed. Moreover, the process is flexible for including further features to enrich its behaviour.

This paper introduces HighChest, an innovative smart freezer designed to promote energy efficient behavior and the responsible use of food. Introducing a novel human–machine interface (HMI) design developed through assessment phases and a user involvement stage, HighChest is state of the art, featuring smart services that exploit embedded sensors and Internet of things functionalities, which enhance the local capabilities of the appliance. The industrial design thinking approach followed for the advanced HMI is intended to maximize the social impact of the food management service, enhancing both the user experience of the product and the user’s willingness to adopt eco- and energy-friendly behaviors. The sensor equipment realizes automatic recognition of food by learning from the users, as well as automatic localization inside the deposit space. Moreover, it provides monitoring of the appliance’s usage, avoiding temperature and humidity issues related to improper use. Experimental tests were conducted to evaluate the localization system, and the results showed 100% accuracy for weights greater or equal to 0.5 kg. Drifts due to the lid opening and prolonged usage time were also measured, to implement automatic reset corrections.

The Industry 4.0 paradigm requires new technologies and methods not only to improve the profitability and the quality of the industrial production and products, but also new strategies to reduce the social and environmental impact of the production process. Many line manufacturing chains unbox and assembly components to create products, but create a large amount of waste that sometimes can't be recycled because of the exposure to contaminants. When it comes to the automotive industry, mineral oils may contaminate plastic packaging and cardboard boxes during manufacturing, making hard to recycle them. In this paper we propose a proof of concept of a packaging sorting system based on NIR spectroscopy, to automate sorting and get high quality outputs for the recycling of cardboard package boxes. S pectral datasets have been pre-processed and dimensionally reduced using PCA. A S VM algorithm has been trained to distinguish between oil contaminated and non contaminated materials. Two NIR spectrometers with sensing range 640-1050 nm and 950-1650 nm have been used and evaluated, to select the proper sensor configuration. Eventually, the system classification accuracy was respectively up to the 98,68% and 98,64% using the 950-1650 nm and the 640-1050 nm spectrometers, demonstrating the opportunity to detect mineral oil contamination on boxes.

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