6 research outputs found
Gaze-based Attention Recognition for Human-Robot Collaboration
Attention (and distraction) recognition is a key factor in improving
human-robot collaboration. We present an assembly scenario where a human
operator and a cobot collaborate equally to piece together a gearbox. The setup
provides multiple opportunities for the cobot to adapt its behavior depending
on the operator's attention, which can improve the collaboration experience and
reduce psychological strain. As a first step, we recognize the areas in the
workspace that the human operator is paying attention to, and consequently,
detect when the operator is distracted. We propose a novel deep-learning
approach to develop an attention recognition model. First, we train a
convolutional neural network to estimate the gaze direction using a publicly
available image dataset. Then, we use transfer learning with a small dataset to
map the gaze direction onto pre-defined areas of interest. Models trained using
this approach performed very well in leave-one-subject-out evaluation on the
small dataset. We performed an additional validation of our models using the
video snippets collected from participants working as an operator in the
presented assembly scenario. Although the recall for the Distracted class was
lower in this case, the models performed well in recognizing the areas the
operator paid attention to. To the best of our knowledge, this is the first
work that validated an attention recognition model using data from a setting
that mimics industrial human-robot collaboration. Our findings highlight the
need for validation of attention recognition solutions in such full-fledged,
non-guided scenarios.Comment: Accepted to PETRA 202
Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review
Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice
Towards social embodied cobots: The integration of an industrial cobot with a social virtual agent
The integration of the physical capabilities of an industrial collaborative
robot with a social virtual character may represent a viable solution to
enhance the workers' perception of the system as an embodied social entity and
increase social engagement and well-being at the workplace. An online study was
setup using prerecorded video interactions in order to pilot potential
advantages of different embodied configurations of the cobot-avatar system in
terms of perceptions of Social Presence, cobot-avatar Unity and Social Role of
the system, and explore the relation of these. In particular, two different
configurations were explored and compared: the virtual character was displayed
either on a tablet strapped onto the base of the cobot or on a large TV screen
positioned at the back of the workcell. The results imply that participants
showed no clear preference based on the constructs, and both configurations
fulfill these basic criteria. In terms of the relations between the constructs,
there were strong correlations between perception of Social Presence, Unity and
Social Role (Collegiality). This gives a valuable insight into the role of
these constructs in the perception of cobots as embodied social entities, and
towards building cobots that support well-being at the workplace
The effects of robotic assistance on upper limb spatial muscle synergies in healthy people during planar upper-limb training
BackgroundRobotic rehabilitation is a commonly adopted technique used to restore motor functionality of neurological patients. However, despite promising results were achieved, the effects of human-robot interaction on human motor control and the recovery mechanisms induced with robot assistance can be further investigated even on healthy subjects before translating to clinical practice. In this study, we adopt a standard paradigm for upper-limb rehabilitation (a planar device with assistive control) with linear and challenging curvilinear trajectories to investigate the effect of the assistance in human-robot interaction in healthy people.MethodsTen healthy subjects were instructed to perform a large set of radial and curvilinear movements in two interaction modes: 1) free movement (subjects hold the robot handle with no assistance) and 2) assisted movement (with a force tunnel assistance paradigm). Kinematics and EMGs from representative upper-limb muscles were recorded to extract phasic muscle synergies. The free and assisted interaction modes were compared assessing the level of assistance, error, and muscle synergy comparison between the two interaction modes.ResultsIt was found that in free movement error magnitude is higher than with assistance, proving that task complexity required assistance also on healthy controls. Moreover, curvilinear tasks require more assistance than standard radial paths and error is higher. Interestingly, while assistance improved task performance, we found only a slight modification of phasic synergies when comparing assisted and free movement.ConclusionsWe found that on healthy people, the effect of assistance was significant on task performance, but limited on muscle synergies. The findings of this study can find applications for assessing human-robot interaction and to design training to maximize motor recovery
A Planar Parallel Device for Neurorehabilitation
The patient population needing physical rehabilitation in the upper extremity is constantly increasing. Robotic devices have the potential to address this problem, however most of the rehabilitation robots are technically advanced and mainly designed for clinical use. This paper presents the development of an affordable device for upper-limb neurorehabilitation designed for home use. The device is based on a 2-DOF five-bar parallel kinematic mechanism. The prototype has been designed so that it can be bound on one side of a table with a clamp. A kinematic optimization was performed on the length of the links of the manipulator in order to provide the optimum kinematic behaviour within the desired workspace. The mechanical structure was developed, and a 3D-printed prototype was assembled. The prototype embeds two single-point load cells to measure the force exchanged with the patient. Rehabilitation-specific control algorithms are described and tested. Finally, an experimental procedure is performed in order to validate the accuracy of the position measurements. The assessment confirms an acceptable level of performance with respect to the requirements of the application under analysis
Employing Socially Interactive Agents for Robotic Neurorehabilitation Training
In today's world, many patients with cognitive impairments and motor
dysfunction seek the attention of experts to perform specific conventional
therapies to improve their situation. However, due to a lack of
neurorehabilitation professionals, patients suffer from severe effects that
worsen their condition. In this paper, we present a technological approach for
a novel robotic neurorehabilitation training system. It relies on a combination
of a rehabilitation device, signal classification methods, supervised machine
learning models for training adaptation, training exercises, and socially
interactive agents as a user interface. Together with a professional, the
system can be trained towards the patient's specific needs. Furthermore, after
a training phase, patients are enabled to train independently at home without
the assistance of a physical therapist with a socially interactive agent in the
role of a coaching assistant.Comment: The 5th Workshop on Behavior Adaptation Interaction and Learning for
Assistive Robotics (BAILAR