Computational joint action: Dynamical models to understand the development of joint coordination.

Coordinating with others is part of our everyday experience. Previous studies using sensorimotor coordination games suggest that human dyads develop coordination strategies that can be interpreted as Nash equilibria. However, if the players are uncertain about what their partner is doing, they develop coordination strategies which are robust to the actual partner's actions.

Comparison of Different Telemedicine Services to Pre-Evaluate Their Use in a New "Computational Hospital".

Future healthcare is transitioning toward a decentralization of patient care, in which personal care is increasingly moved at the patient home and surrounding areas, while hospitals concentrate primarily on procedures that cannot be performed elsewhere, such as surgeries or outpatient examinations. The healthcare system in the Liguria region (Italy) is currently developing a new Center for Computational and Technological Medicine (CMCT), which is intended to facilitate and support this transition. As a component of the strategic planning and design process, this study examines the development and organization of telemedicine services across a range of chosen Italian and European institutions that share similarities with CMCT in terms of scope and scale.

UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images.

Purpose: The aim of this work is the development and characterization of a model observer (MO) based on convolutional neural networks (CNNs), trained to mimic human observers in image evaluation in terms of detection and localization of low-contrast objects in CT scans acquired on a reference phantom. The final goal is automatic image quality evaluation and CT protocol optimization to fulfill the ALARA principle.

Approach: Preliminary work was carried out to collect localization confidence ratings of human observers for signal presence/absence from a dataset of 30,000 CT images acquired on a PolyMethyl MethAcrylate phantom containing inserts filled with iodinated contrast media at different concentrations.

Computational modeling of human multisensory spatial representation by a neural architecture.

Our brain constantly combines sensory information in unitary percept to build coherent representations of the environment. Even though this process could appear smooth, integrating sensory inputs from various sensory modalities must overcome several computational issues, such as recoding and statistical inferences problems. Following these assumptions, we developed a neural architecture replicating humans' ability to use audiovisual spatial representations.

Unsupervised Synthetic Acoustic Image Generation for Audio-Visual Scene Understanding.

Acoustic images are an emergent data modality for multimodal scene understanding. Such images have the peculiarity of distinguishing the spectral signature of the sound coming from different directions in space, thus providing a richer information as compared to that derived from single or binaural microphones. However, acoustic images are typically generated by cumbersome and costly microphone arrays which are not as widespread as ordinary microphones.

Artificial Partners to Understand Joint Action: Representing Others to Develop Effective Coordination.

In the last years, artificial partners have been proposed as tools to study joint action, as they would allow to address joint behaviors in more controlled experimental conditions. Here we present an artificial partner architecture which is capable of integrating all the available information about its human counterpart and to develop efficient and natural forms of coordination. The model uses an extended state observer which combines prior information, motor commands and sensory observations to infer the partner's ongoing actions (partner model).

Versatile and non-versatile occupational back-support exoskeletons: A comparison in laboratory and field studies.

Assistive strategies for occupational back-support exoskeletons have focused, mostly, on lifting tasks. However, in occupational scenarios, it is important to account not only for lifting but also for other activities. This can be done exploiting human activity recognition algorithms that can identify which task the user is performing and trigger the appropriate assistive strategy.

Self-operated stimuli improve subsequent visual motion integration.

Evidences of perceptual changes that accompany motor activity have been limited primarily to audition and somatosensation. Here we asked whether motor learning results in changes to visual motion perception. We designed a reaching task in which participants were trained to make movements along several directions, while the visual feedback was provided by an intrinsically ambiguous moving stimulus directly tied to hand motion.

Addressing signal alterations induced in CT images by deep learning processing: A preliminary phantom study.

Purpose: We investigate, by an extensive quality evaluation approach, performances and potential side effects introduced in Computed Tomography (CT) images by Deep Learning (DL) processing.

Method: We selected two relevant processing steps, denoise and segmentation, implemented by two Convolutional Neural Networks (CNNs) models based on autoencoder architecture (encoder-decoder and UNet) and trained for the two tasks. In order to limit the number of uncontrolled variables, we designed a phantom containing cylindrical inserts of different sizes, filled with iodinated contrast media.

Haptic vs sensorimotor training in the treatment of upper limb dysfunction in multiple sclerosis: A multi-center, randomised controlled trial.

Background: In multiple sclerosis (MS) exercise improves upper limb functions, but it is unclear what training types are more effective.

Objective: This study compares robot-assisted training based on haptic or sensorimotor exercise.

Methods: 41clinically definite MS subjects with upper limb impairment were randomised into two groups: (i) Haptic and (ii) Sensorimotor.

A dynamic model of hand movements for proportional myoelectric control of a hand prosthesis.

In myo-controlled prosthetic hands, surface electromyographic signals are used to operate the hand actuators. A pre-requisite for effective control is that the intended movement is decoded from muscle activity. Simpler approaches use pattern recognition techniques, which assume a finite set of possible actions.

Incomplete information about the partner affects the development of collaborative strategies in joint action.

Physical interaction with a partner plays an essential role in our life experience and is the basis of many daily activities. When two physically coupled humans have different and partly conflicting goals, they face the challenge of negotiating some type of collaboration. This requires that both participants understand their partner's state and current actions.

Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond.

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability.

Haptic communication between humans is tuned by the hard or soft mechanics of interaction.

To move a hard table together, humans may coordinate by following the dominant partner's motion [1-4], but this strategy is unsuitable for a soft mattress where the perceived forces are small. How do partners readily coordinate in such differing interaction dynamics? To address this, we investigated how pairs tracked a target using flexion-extension of their wrists, which were coupled by a hard, medium or soft virtual elastic band. Tracking performance monotonically increased with a stiffer band for the worse partner, who had higher tracking error, at the cost of the skilled partner's muscular effort.

Computational rehabilitation of neglect: Using state-space models to understand the recovery mechanisms.

Unilateral spatial neglect is a neuropsychological syndrome often observed in right hemisphere stroke patients. The symptoms differ from subject to subject. A few rehabilitation approaches, e.

Effect of Position- and Velocity-Dependent Forces on Reaching Movements at Different Speeds.

The speed of voluntary movements is determined by the conflicting needs of maximizing accuracy and minimizing mechanical effort. Dynamic perturbations, e.g.

Effect of interface type in the VR-based acquisition of pedestrian skills in persons with ASD.

Possession of `social' skills is crucial for persons with autism spectrum disorders (ASD) to maintain a certain independence and a better quality of life, and interaction with virtual environments seems an effective learning aid. In a previous study, we reported that in adults with ASD interaction with a virtual environment (a virtual city) is beneficial to the acquisition of pedestrian skills (street crossing and street navigation). Interaction was based on a gesture-based interface (Microsoft Kinect).

Modulation of motor performance by a monetary incentive: A pilot study.

It is commonly acknowledged that movement performance is determined by a trade-off between accuracy requirements and energetic expenditure. However, their relative weights are subjective and depend on the perceived benefit (or cost) associated to successful movement completion. A deeper knowledge on how this trade-off affects motor behavior may suggest ways to manipulate it in pathologies, like Parkinson's disease, in which the mechanisms underlying the selection of motor response are believed to be defective.

Natural interfaces and virtual environments for the acquisition of street crossing and path following skills in adults with Autism Spectrum Disorders: a feasibility study.

Background: Lack of social skills and/or a reduced ability to determine when to use them are common symptoms of Autism Spectrum Disorder (ASD). Here we examine whether an integrated approach based on virtual environments and natural interfaces is effective in teaching safety skills in adults with ASD. We specifically focus on pedestrian skills, namely street crossing with or without traffic lights, and following road signs.

Adaptive training with full-body movements to reduce bradykinesia in persons with Parkinson's disease: a pilot study.

Background: Bradykinesia (slow movements) is a common symptom of Parkinson's disease (PD) and results in reduced mobility and postural instability. The objective of this study is to develop and demonstrate a technology-assisted exercise protocol that is specifically aimed at reducing bradykinesia.

Methods: Seven persons with PD participated in this study.

Management of postoperative complications: general approach.

The goal of postoperative management is to promote early mobility and avoid postoperative complications, recognizing the potentially devastating impact of complications on elderly patients with hip fracture. The recommended approach involves early mobilization; freedom from tethers (indwelling urinary catheters and other devices); effective pain control; treating malnutrition; preventing pressure ulcers; reducing risk for pulmonary, urinary, and wound infections; and managing cognition. This carefully structured and patient-centered management provides older, vulnerable patients their best chance of returning to their previous level of functioning as quickly and safety as possible.

Reward-based learning of a redundant task.

Motor skill learning has different components. When we acquire a new motor skill we have both to learn a reliable action-value map to select a highly rewarded action (task model) and to develop an internal representation of the novel dynamics of the task environment, in order to execute properly the action previously selected (internal model). Here we focus on a 'pure' motor skill learning task, in which adaptation to a novel dynamical environment is negligible and the problem is reduced to the acquisition of an action-value map, only based on knowledge of results.

Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level.

Computational models of neuromotor recovery after a stroke might help to unveil the underlying physiological mechanisms and might suggest how to make recovery faster and more effective. At least in principle, these models could serve: (i) To provide testable hypotheses on the nature of recovery; (ii) To predict the recovery of individual patients; (iii) To design patient-specific "optimal" therapy, by setting the treatment variables for maximizing the amount of recovery or for achieving a better generalization of the learned abilities across different tasks. Here we review the state of the art of computational models for neuromotor recovery through exercise, and their implications for treatment.