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Images acquired by RGB cameras on Unmanned Aerial Vehicles (UAVs) can be particularly useful to detect crowd in urban areas when restrictive conditions are imposed for the sake of public safety or health , such as during the Covid 19 pandemic. Together with acquired images, opportune pattern recognition techniques have to be considered to extract useful information. In this framework, features capturing the semantic rich information encompassed in VHR images have to be computed. In particular, Deep Neural Networks (DNN) have been recently proved to be able to extract useful features from data [1]. Moreover, in a transfer learning approach, a DNN pre-trained on a data set can be used to extract opportune features, named deep-features, from another data set, belonging to a different applicative domain [1], [2]. Here, a transfer learning technique is presented to produce change maps, detecting how people gathering increases, from VHR images. It is based on deep-features computed by using some pre-trained convolutional layers of AlexNet. The proposed methodology has been tested on a data set composed of several synthetic VHR images, that simulate crowd collecting in a park, as they can be acquired by RGB camera on a UAV flying at 10 meters height from the ground. The experimental results show that the proposed technique is able to efficiently detect change due to new people incoming in the scene or people that get away, with a low computational cost and in a near-real time operative mode.

Questo documento contiene una breve descrizione dello stato dell'arte delle piattaforme e della relativa sensoristica per sistemi aerei a pilotaggio remoto (APR) nell'ambito dell'uso civile e del monitoraggio del territorio. Non essendo praticabile ed anche di scarso interesse la stesura di un elenco esaustivo dei prodotti commerciali - continuamente in evoluzione - si è preferito indicare i più recenti lavori in letteratura che rappresentano una rassegna dell'uso dei sistemi APR in vari ambiti scientifici ed applicativi, e le caratteristiche da considerare in diversi domini. Oltre a questo, si è delineato un sommario della corrente normativa che regola il settore e delle classificazioni delle varie piattaforme, normalmente adottate in letteratura. Infine, vengono presentate caratteristiche dei sensori di riferimento che saranno considerati nel progetto, con esempi di applicazioni degli stessi in domini coerenti con quelli dei task previsti.

La rilevazione dei cambiamenti ambientali da dati telerilevati è ampiamente usata in molti contesti applicativi, sia per effettuare un controllo periodico e sistematico del territorio (variazione dell'estensione abitativa, monitoraggio delle zone vegetate, etc.) sia per la gestione ed il controllo di situazioni di emergenza dovute al verificarsi di disastri naturali, quali frane, alluvioni, incendi [1]. Per catturare qualsiasi cambiamento ambientale, è indispensabile considerare immagini multi-temporali della stessa area geografica [2]. L'obiettivo dell'attività 3.2 è stato quello di progettare e realizzare una metodologia, matematicamente e statisticamente ben fondata, in grado di fondere immagini multi-temporali relative alla stessa area geografica con l'obiettivo di rilevare in modo automatico i cambiamenti a terra.

Questo documento contiene una breve descrizione dello stato dell'arte delle piattaforme e della relativa sensoristica per sistemi aerei a pilotaggio remoto (APR) nell'ambito dell'uso civile e del monitoraggio del territorio. Non essendo praticabile ed anche di scarso interesse la stesura di un elenco esaustivo dei prodotti commerciali - continuamente in evoluzione - si è preferito indicare i più recenti lavori in letteratura che rappresentano una rassegna dell'uso dei sistemi APR in vari ambiti scientifici ed applicativi, e le caratteristiche da considerare in diversi domini. Oltre a questo, si è delineato un sommario della corrente normativa che regola il settore e delle classificazioni delle varie piattaforme, normalmente adottate in letteratura. Infine, vengono presentate caratteristiche dei sensori di riferimento che saranno considerati nel progetto, con esempi di applicazioni degli stessi in domini coerenti con quelli dei task previsti.

Modulation of arm mechanical impedance is a fundamental aspect for interaction with the external environment and its regulation is essential for stability preservation during manipulation. Even though past research on human arm movements has suggested that models of human finger impedance would benefit the study of neural control mechanisms and the design of novel hand prostheses, relatively few studies have focused on finger and hand impedance. This article touches on the two main aspects of this research topic: first it introduces a mechanical refinement of a device that can be used to effectively measure finger impedance during manipulation tasks; then, it describes a pilot study aimed at identifying the inertia of the finger and the viscous and elastic properties of finger muscles. The proposed wearable exoskeleton, which has been designed to measure finger posture and impedance modulation while leaving the palm free, is capable of applying fast displacements while monitoring the interaction forces between the human finger and the robotic links. Moreover, due to the relatively small inertia of the fingers, it allows us to meet some stringent specifications, performing relatively large displacements (~45°) before the stretch reflex intervenes (~25 ms). The results of measurements on five subjects show that inertia, damping, and stiffness can be effectively identified and that the parameters obtained are comparable with values from previous studies.

Recent studies of human arm movements have suggested that the control of stiffness may be important both for maintaining stability and for achieving differences in movement accuracy. Several studies in the robotic field demonstrated that grasp stiffness is useful for modelling and controlling manipulators but, even though it is accredited that having models of the human finger impedance would be very desirable for the control of anthropomorphous robot's hands, relatively few studies have focused on finger and hand stiffness. To allow the measurement of such entities at the finger level, an appropriate device capable of applying fast force transients while at the same time be able to monitor the finger movements is required. The work presented in this paper is a very detailed report about the design of a new hand exoskeleton system that will be used in our future works to investigate the finger stiffness range in different grasping postures and conditions.

This paper presents the design of a direct driven under-actuated portable hand exoskeleton for rehabilitation. The design of the proposed Hand EXOskeleton SYStem (HEXOSYS) was driven by multi-objective optimisation strategy and inspiration from the human hand. The optimisation algorithm resulted in the choice of optimum link lengths of the device. The optimisation criteria are based on dexterity, isotropy and exertion of perpendicular forces on the finger digits. Furthermore, a series of experiments on the human hand using appropriate sensory instrumentation guided the selection of actuators thereby resulting in a rehabilitation device which is compatible with the human hand force capabilities. The provision of force as well as position feedback gives quantitative feedback to the therapist and would imply a more efficient rehabilitation process. The first prototype of the device has been designed and realized.

Background Forearm surface electromyography (EMG) has been in use since the Sixties to feed-forward control active hand prostheses in a more and more refined way. Recent research shows that it can be used to control even a dexterous polyarticulate hand prosthesis such as Touch Bionics's i-LIMB, as well as a multifingered, multi-degree-of-freedom mechanical hand such as the DLR II. In this paper we extend previous work and investigate the robustness of such fine control possibilities, in two ways: firstly, we conduct an analysis on data obtained from 10 healthy subjects, trying to assess the general applicability of the technique; secondly, we compare the baseline controlled condition (arm relaxed and still on a table) with a "Daily-Life Activity" (DLA) condition in which subjects walk, raise their hands and arms, sit down and stand up, etc., as an experimental proxy of what a patient is supposed to do in real life. We also propose a cross-subject model analysis, i.e., training a model on a subject and testing it on another one. The use of pre-trained models could be useful in shortening the time required by the subject/patient to become proficient in using the hand. Results A standard machine learning technique was able to achieve a real-time grip posture classification rate of about 97% in the baseline condition and 95% in the DLA condition; and an average correlation to the target of about 0.93 (0.90) while reconstructing the required force. Cross-subject analysis is encouraging although not definitive in its present state. Conclusion Performance figures obtained here are in the same order of magnitude of those obtained in previous work about healthy subjects in controlled conditions and/or amputees, which lets us claim that this technique can be used by reasonably any subject, and in DLA situations. Use of previously trained models is not fully assessed here, but more recent work indicates it is a promising way ahead.

The state-of-the-art in control of hand prosthetics is far from optimal. The main control interface is represented by surface electromyography (EMG): the activation potentials of the remnants of large muscles of the stump are used in a non-natural way to control one or, at best, two degrees-of-freedom. This has two drawbacks: first, the dexterity of the prosthesis is limited, leading to poor interaction with the environment; second, the patient undergoes a long training time. As more dexterous hand prostheses are put on the market, the need for a finer and more natural control arises. Machine learning can be employed to this end. A desired feature is that of providing a pre-trained model to the patient, so that a quicker and better interaction can be obtained. To this end we propose model adaptation with least-squares SVMs, a technique that allows the automatic tuning of the degree of adaptation. We test the effectiveness of the approach on a database of EMG signals gathered from human subjects. We show that, when pre-trained models are used, the number of training samples needed to reach a certain performance is reduced, and the overall performance is increased, compared to what would be achieved by starting from scratch.