Kineto-dynamic modeling of human upper limb for robotic manipulators and assistive applications

The sensory-motor architecture of human upper limb and hand is characterized by a complex inter-relation of multiple elements, such as ligaments, muscles, and joints. Nonetheless, humans are able to generate coordinated and meaningful motor actions to interact-and eventually explore-the external environment. Such a complexity reduction is usually studied within the framework of synergistic control, whose focus has been mostly limited on human grasping and manipulation. Little attention has been devoted to the spatio-temporal characterization of human upper limb kinematic strategies and how the purposeful exploitation of the environmental constraints shapes human execution of manipulative actions. In this chapter, we report results on the evidence of a synergistic control of human upper limb and during manipulation with the environment. We propose functional analysis to characterize main spatio-temporal coordinated patterns of arm joints. Furthermore, we study how the environment influences human grasping synergies. The effect of cutaneous impairment is also evaluated. Applications to the design and control of robotic and assistive devices are finally discussed

Modeling Human Motor Skills to Enhance Robots’ Physical Interaction

The need for users’ safety and technology acceptability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions and interactions. In this manuscript, we present a research approach that moves from the understanding of human movements and derives usefull guidelines for the planning of arm movements and the learning of skills for physical interaction of robots with the surrounding environment

A technical framework for human-like motion generation with autonomous anthropomorphic redundant manipulators

The need for users' safety and technology accept-ability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions. Classic solutions for anthropomorphic movement generation usually rely on optimization procedures, which build upon hypotheses devised from neuroscientific literature, or capitalize on learning methods. However, these approaches come with limitations, e.g. limited motion variability or the need for high dimensional datasets. In this work, we present a technique to directly embed human upper limb principal motion modes computed through functional analysis in the robot trajectory optimization. We report on the implementation with manipulators with redundant anthropomorphic kinematic architectures - although dissimilar with respect to the human model used for functional mode extraction - via Cartesian impedance control. In our experiments, we show how human trajectories mapped onto a robotic manipulator still exhibit the main characteristics of human-likeness, e.g. low jerk values. We discuss the results with respect to the state of the art, and their implications for advanced human-robot interaction in industrial co-botics and for human assistance

Association between serum Mg2+ concentrations and cardiovascular organ damage in a cohort of adult subjects

Magnesium (Mg2+) levels are associated with insulin resistance, hypertension, atherosclerosis, and type 2 diabetes (T2DM). We evaluated the clinical utility of physiological Mg2+ in assessing subclinical cardiovascular organ damage including increased carotid artery intima-media thickness (c-IMT) and left ventricular mass index (LVMI) in a cohort of well-characterized adult non-diabetic individuals. Age-and gender-adjusted correlations between Mg2+ and metabolic parameters showed that Mg2+ circulating levels were correlated negatively with body mass index (BMI), fasting glucose, and 2h-oral glucose tolerance test (OGTT) glucose. Similarly, Mg2+ levels were significantly and negatively related to c-IMT and LVMI. A multivariate regression analysis revealed that age (β = 0.440; p < 0.0001), BMI (β = 0.225; p < 0.0001), and Mg2+ concentration (β = −0.122; p < 0.01) were independently associated with c-IMT. Age (β = 0.244; p = 0.012), Mg2+ (β = −0.177; p = 0.019), and diastolic blood pressure (β = 0.184; p = 0.038) were significantly associated with LVMI in women, while age (β = 0.211; p = 0.019), Mg2+ (β = −0.171; p = 0.038) and the homeostasis model assessment index of insulin resistance (HOMA-IR) (β = −0.211; p = 0.041) were the sole variables associated with LVMI in men. In conclusion, our data support the hypothesis that the assessment of Mg2+ as part of the initial work-up might help unravel the presence of subclinical organ damage in subjects at increased risk of cardiovascular complications

A Synergistic Behavior Underpins Human Hand Grasping Force Control During Environmental Constraint Exploitation

Despite the complex nature of human hands, neuroscientific studies suggested a simplified kinematic control underpinning motion generation, resulting in principal joint angle co-variation patterns, usually called postural hand synergies. Such a low dimensional description was observed in common grasping tasks, and was proven to be preserved also for grasps performed by exploiting the external environment (e.g., picking up a key by sliding it on a table). In this paper, we extend this analysis to the force domain. To do so, we performed experiments with six subjects, who were asked to grasp objects from a flat surface while force/torque measures were acquired at fingertip level through wearable sensors. The set of objects was chosen so that participants were forced to interact with the table to achieve a successful grasp. Principal component analysis was applied to force measurements to investigate the existence of co-variation schemes, i.e. a synergistic behavior. Results show that one principal component explains most of the hand force distribution. Applications to clinical assessment and robotic sensing are finally discussed

Understanding Human Manipulation with the Environment: A Novel Taxonomy for Video Labelling

In recent years, the spread of data-driven approaches for robotic grasp synthesis has come with the increasing need for reliable datasets, which can be built e.g. through video labelling. To this goal, it is important to define suitable rules to characterize the main human grasp types, for easily identifying them in video streams. In this work, we present a novel taxonomy that builds upon the related state of the art, but it is specifically thought for video labelling. It focuses on the interaction of the hand with the environment and accounts for pre-contact phases, bi-manual grasps as well as non-prehensile strategies. This study is complemented with a dataset of labelled videos of subjects performing activities of daily living, for a total of nine hours, and the description of MatLab tools for labelling new videos. Both hands were labelled at any time. We used these labelled data for performing a preliminary statistical description of the occurrences of the here proposed class types

Deep learning techniques for modelling human manipulation and its translation for autonomous robotic grasping with soft end-effectors

One of the key enablers for the extraordinary dexterity of human hands is their compliance and capability to purposefully adapt with the environment and to multiply their manipulation possibilities. This observation has also produced a significant paradigm shift for the design of robotic hands, leading to the avenue of soft endeffectors that embed elastic and deformable elements directly in their mechanical architecture. This shift has also determined a perspective change for the control and planning of the grasping phases, with respect to (w.r.t.) the classical approach used with rigid grippers. Indeed, instead of targeting an accurate analysis of the contact points on the object, an approximated estimation of the relative hand-object pose is sufficient to generate successful grasps, exploiting the intrinsic adaptability of the robotic systems to overcome local uncertainties. This chapter reports on deep learning (DL) techniques used to model human manipulation and to successfully translate these modelling outcomes for enabling soft artificial hands to autonomous grasp objects with the environment. Chapter Contents: • 1.1 Introduction • 1.2 Investigation of the human example • 1.2.1 Methods • 1.2.2 Experiments • 1.2.2.1 Evaluation on ECE data set • 1.3 Autonomous grasping with anthropomorphic soft hands • 1.3.1 High level: deep classifier • 1.3.1.1 Object detection • 1.3.1.2 Primitive classification • 1.3.2 Transferring grasping primitives to robots • 1.3.3 Experimental setup • 1.3.3.1 Approach phase • 1.3.3.2 Grasp phase • 1.3.3.3 Control strategy • 1.3.4 Results • 1.4 Discussion and conclusions • Acknowledgement • References

A User-Centered Approach to Artificial Sensory Substitution for Blind People Assistance

Artificial sensory substitution plays a crucial role in different domains, including prosthetics, rehabilitation and assistive technologies. The sense of touch has historically represented the ideal candidate to convey information on the external environment, both contact-related and visual, when the natural action-perception loop is broken or not available. This is particularly true for blind people assistance, in which touch elicitation has been used to make content perceivable (e.g. Braille text or graphical reproduction), or to deliver informative cues for navigation. However, despite the significant technological advancements for what concerns both devices for touch-mediated access to alphanumeric stimuli, and technology-enabled haptic navigation supports, the majority of the proposed solutions has met with scarce acceptance in end users community. Main reason for this, in our opinion, is the poor involvement of the blind people in the design process. In this work, we report on a user-centric approach that we successfully applied for haptics-enabled systems for blind people assistance, whose engineering and validation have received significant inputs from the visually-impaired people. We also present an application of our approach to the design of a single-cell refreshable Braille device and to the development of a wearable haptic system for indoor navigation. After a summary of our previous results, we critically discuss next avenues and propose novel solutions for touch-mediated delivery of information for navigation, whose implementation has been totally driven by the feedback collected from real end-users

Design and Validation of the Readable Device: A Single-Cell Electromagnetic Refreshable Braille Display

Blindness represents one of the major disabling societal causes, impacting the life of visually impaired people and their families. For what concerns the access to written information, one of the main tools used by blind people is the traditional Braille code. This is the reason why in the recent years, there has been a technological effort to develop refreshable Braille devices. These consist of multiple physical dots that dynamically change their configuration to reproduce different sequences of the letters in Braille code. Although promising, these approaches have many drawbacks, which are mainly related to costs, design complexity, portability, and power consumption. Of note, while many solutions have been proposed for multi-cell devices, the investigation of the potentialities of single-cell refreshable systems has received little attention so far. This investigation could offer effective and viable manners to overcome the aforementioned drawbacks, likely fostering a widespread adoption of such assistive technologies with end-users. In this article, we present the design and characterization of a new cost-effective single-cell Electromagnetic Refreshable Braille Display, the Readable system. We also report on tests performed with blindfolded and blind expert Braille code readers. Results demonstrate the effectiveness of our device in correctly reproducing alphanumeric content, opening promising perspectives in every-day life applications