A neurorobotics approach to behaviour selection based on human activity recognition

Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia-thalamus-cortex (BG-T-C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.

Learning robot differential movements using a new educational robotics simulation tool

The study of robotics has become a popular course among many educational programs, especially as a technical elective. A significant part of this course involves having the students learn how to program the movement of a robotic arm by controlling the velocity of its individual joint motors, a topic referred to as joint programming. They must learn how to develop algorithms to move the end effector of the arm by controlling the instantaneous velocity or some similar aspect, of each joint motor. To support this learning activity, physical or virtual robotic arms are typically employed. Visual observation of the movement of the arm provides feedback to the correctness of the student's joint programming algorithms. A problem arises with supporting the student in learning how to move the robotic arm with precise velocity along some path, a subtopic of joint programming referred to as differential movements. To develop this knowledge, the student must produce and test differential movement algorithms and have the capability to verify its correctness. Regardless of the type of arm used, physical or virtual, the human eye cannot notice the difference between a correct or incorrect movement of the end effector as this will involve noticing small differences in velocities. This study found that by simulating the process of spray painting on a virtual canvas, the correctness of a differential movement algorithm may be accessed by observing the resulting paint on the canvas as opposed to observing the movement of the arm. A model of a set of spray-painting equipment and a canvas was added to an existing virtual robotic arm educational tool and used in an Introduction to Robotics class offered at Florida Gulf Coast University in Spring 2019 and Spring 2020. The class offered in Spring 2019 used the virtual arm but without the spray-painting feature while the class offered in Spring 2020 used the new spray-painting feature that was added to the virtual arm. Exam results show that 59.4% of the students that used the new feature scored at least an 85% on the corresponding differential movements exam question compared to only 5.6% of the class that did not use the added spray-painting feature. The differential movement exam question simply asked the student to produce a differential movements algorithm to move the arm with a specified velocity alone a straight line.

Robot and mechanical testing of a specialist manual toothbrush for cleaning efficacy and improved force control

Background

Toothbrushes require flexibility to access all dental surfaces and remove plaque effectively, but they should also aim to prevent or limit overbrushing and consequent damage to teeth and gums. In two studies, the physical properties and cleaning performance of specialist test toothbrushes with flexible necks were compared to a reference rigid-necked toothbrush.

Methods

In Study 1, a universal testing machine (Instron E 10,000) with a specially designed setup was used to test the deflection behaviour of toothbrush head and neck. Untufted toothbrushes were fixed in a custom holder and force was applied to the head while the deflection was measured. In Study 2, one control and five test toothbrushes were assessed using a robot system to simulate the cleaning of artificial plaque from defined surfaces of artificial replicated human teeth in a model oral cavity (typodonts).

Results

Study 1 showed that the flexible-neck toothbrush deflected 2 to 2.5 times more than the rigid-neck reference toothbrush when same force was applied to the toothbrush head. Study 2 revealed that all five test toothbrushes showed statistically superior simulated plaque removal to the reference toothbrush. This superiority was observed for all test toothbrushes employing horizontal and rotating brushing action (all p = 0.001) but only three of the five toothbrushes when vertical brushing was employed (all p = 0.001). Cleaning efficacy of the test toothbrushes was demonstrated both interdentally and at the gumline locations. The Complete Protection toothbrush showed the most effective cleaning performance followed by the Repair and Protect and Rapid Relief toothbrushes.

Conclusion

The addition of a flexible-neck component to the toothbrush designs helped to reduce stiffness and may allow more effective cleaning compared to rigid designs with controlled force distribution on the teeth and gums. This may help to provide plaque control at all potential risk areas in an in vitro robot model and could support good oral hygiene in-use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02211-4.

The value of a 1-day multidisciplinary robot surgery training for novice robot surgeons

INTRODUCTION

To fulfil the need for a basic level of competence in robotic surgery (Brinkman et al., Surg Endosc Other Interv Tech 31(1):281-287, 2017; Dutch Health inspectorate (Inspectie voor de gezondheidszorg), Insufficient carefulness at the introduction of surgical robots (in Dutch: Onvoldoende zorgvuldigheid bij introductie van operatierobots), Igz, Utrecht, 2010), the NIVEL (Netherlands Institute for Healthcare Research) developed the 'Basic proficiency requirements for the safe use of robotic surgery' (BPR). Based on the BPR a 1-day robotic surgery training was organised to answer the following research questions: (1) Are novice robot surgeons able to accurately self-assess their knowledge and dexterity skills? (2) Is it possible to include the teaching of all BPRs in a 1-day training?

MATERIALS AND METHODS

Based on the BPR, a robot surgery course was developed for residents and specialists (surgery, gynaecology and urology). In preparation, the participants completed an online e-module. The 1-day training consisted of a practical part on robot set-up, a theoretical section, and hands-on exercises on virtual reality robot simulators. Multiple online questionnaire was filled out by the participants at the end of the training to evaluate the perceived educational value of the course and to self-assess the degree to which BPRs were reached.

RESULTS

20 participants completed the training during the conference of the Dutch Association for Endoscopic Surgery (NVEC) in 2017. Participants indicated nearly all competency requirements were mastered at the end of the training. The competency requirements not mastered were, however, critical requirements for the safe use of the surgical robot. Skill simulation results show a majority of participants are unable to reach a proficient simulation score in basic skill simulation exercises.

CONCLUSION

Results show novice robot surgeons are too positive in the self-assessment of their own dexterity skills after a 1-day training. Self-assessment revealed uncertainty of the obtained knowledge level on requirements for the safe use of the surgical robot. Basic courses on robotic training should inform trainees about their results to enhance learning and inform them of their competence levels.