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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 16681

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Prof. Dr. Ahmad Rad

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Guest Editor

School of Mechatronic Systems Engineering, Simon Fraser University, Burnaby, BC, Canada
Interests: social robots; autonomous systems; multi-modal perception; simultaneous localization and mapping; indoor navigation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Social robots are a dedicated subclass of autonomous and intelligent robots that are designed to interact, communicate, and collaborate with humans. This definition is broad and extends to companion robots, pet robots, and healthcare robots, to name a few. As such, social robotics research is interdisciplinary and multifaceted, attracting researchers from engineering to neuroscience to social sciences, encompassing diverse research areas including machine learning, navigation, perception, sentience, affective competing, the Internet of Things (IoT), ethics, and safety.

The current coronavirus pandemic and its toll on healthcare workers can be used as an incentive to researchers to address the design of social robots specifically for the healthcare sector. We welcome submissions devoted to this cause that includes but is not limited to the following areas:

Indoor navigation (hospitals)
Multi-modal perception
Robotics nurse
Robotics companion
Affective computing
SLAM in dynamic settings
Behavioristic robotics
Ethics and safety
Object and place recognition
Human–robot collaboration
Disinfecting robots

Prof. Dr. Ahmad Rad
Guest Editor

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Keywords

Social robots
Indoor navigation
Multi-modal perception
Robotics companion

Published Papers (5 papers)

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Research

45 pages, 9830 KiB

Open AccessArticle

Anthropomorphic Robotic Eyes: Structural Design and Non-Verbal Communication Effectiveness

by Marko Penčić, Maja Čavić, Dragana Oros, Petar Vrgović, Kalman Babković, Marko Orošnjak and Dijana Čavić

Sensors 2022, 22(8), 3060; https://0-doi-org.brum.beds.ac.uk/10.3390/s22083060 - 15 Apr 2022

Cited by 3 | Viewed by 4082

Abstract

This paper shows the structure of a mechanical system with 9 DOFs for driving robot eyes, as well as the system’s ability to produce facial expressions. It consists of three subsystems which enable the motion of the eyeballs, eyelids, and eyebrows independently to the rest of the face. Due to its structure, the mechanical system of the eyeballs is able to reproduce all of the motions human eyes are capable of, which is an important condition for the realization of binocular function of the artificial robot eyes, as well as stereovision. From a kinematic standpoint, the mechanical systems of the eyeballs, eyelids, and eyebrows are highly capable of generating the movements of the human eye. The structure of a control system is proposed with the goal of realizing the desired motion of the output links of the mechanical systems. The success of the mechanical system is also rated on how well it enables the robot to generate non-verbal emotional content, which is why an experiment was conducted. Due to this, the face of the human-like robot MARKO was used, covered with a face mask to aid in focusing the participants on the eye region. The participants evaluated the efficiency of the robot’s non-verbal communication, with certain emotions achieving a high rate of recognition. Full article

(This article belongs to the Special Issue Social Robots in Healthcare)

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15 pages, 1648 KiB

Open AccessArticle

A Preliminary Study on Realizing Human–Robot Mental Comforting Dialogue via Sharing Experience Emotionally

by Changzeng Fu, Qi Deng, Jingcheng Shen, Hamed Mahzoon and Hiroshi Ishiguro

Sensors 2022, 22(3), 991; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030991 - 27 Jan 2022

Cited by 7 | Viewed by 2715

Abstract

Mental health issues are receiving more and more attention in society. In this paper, we introduce a preliminary study on human–robot mental comforting conversation, to make an android robot (ERICA) present an understanding of the user’s situation by sharing similar emotional experiences to enhance the perception of empathy. Specifically, we create the emotional speech for ERICA by using CycleGAN-based emotional voice conversion model, in which the pitch and spectrogram of the speech are converted according to the user’s mental state. Then, we design dialogue scenarios for the user to talk about his/her predicament with ERICA. In the dialogue, ERICA shares other people’s similar predicaments and adopts a low-spirit voice to express empathy to the interlocutor’s situation. At the end of the dialogue, ERICA tries to encourage with a positive voice. Subsequently, questionnaire-based evaluation experiments were conducted with the recorded conversation. In the questionnaire, we use the Big Five scale to evaluate ERICA’s personality. In addition, the perception of emotion, empathy, and encouragement in the dialogue are evaluated. The results show that the proposed emotional expression strategy helps the android robot better present low-spirit emotion, empathy, the personality of extroversion, while making the user better feel the encouragement. Full article

(This article belongs to the Special Issue Social Robots in Healthcare)

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17 pages, 1217 KiB

Open AccessArticle

Social Robots for Evaluating Attention State in Older Adults

by Yi-Chen Chen, Su-Ling Yeh, Tsung-Ren Huang, Yu-Ling Chang, Joshua O. S. Goh and Li-Chen Fu

Sensors 2021, 21(21), 7142; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217142 - 28 Oct 2021

Cited by 3 | Viewed by 2526

Abstract

Sustained attention is essential for older adults to maintain an active lifestyle, and the deficiency of this function is often associated with health-related risks such as falling and frailty. The present study examined whether the well-established age-effect on reducing mind-wandering, the drift to internal thoughts that are seen to be detrimental to attentional control, could be replicated by using a robotic experimenter for older adults who are not as familiar with online technologies. A total of 28 younger and 22 older adults performed a Sustained Attention to Response Task (SART) by answering thought probes regarding their attention states and providing confidence ratings for their own task performances. The indices from the modified SART suggested a well-documented conservative response strategy endorsed by older adults, which were represented by slower responses and increased omission errors. Moreover, the slower responses and increased omissions were found to be associated with less self-reported mind-wandering, thus showing consistency with their higher subjective ratings of attentional control. Overall, this study demonstrates the potential of constructing age-related cognitive profiles with attention evaluation instruction based on a social companion robot for older adults at home. Full article

(This article belongs to the Special Issue Social Robots in Healthcare)

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19 pages, 2968 KiB

Open AccessArticle

Personal Resilience Can Be Well Estimated from Heart Rate Variability and Paralinguistic Features during Human–Robot Conversations

by Shin-Min Hsu, Sue-Huei Chen and Tsung-Ren Huang

Sensors 2021, 21(17), 5844; https://0-doi-org.brum.beds.ac.uk/10.3390/s21175844 - 30 Aug 2021

Cited by 6 | Viewed by 3362

Abstract

Mental health is as crucial as physical health, but it is underappreciated by mainstream biomedical research and the public. Compared to the use of AI or robots in physical healthcare, the use of AI or robots in mental healthcare is much more limited in number and scope. To date, psychological resilience—the ability to cope with a crisis and quickly return to the pre-crisis state—has been identified as an important predictor of psychological well-being but has not been commonly considered by AI systems (e.g., smart wearable devices) or social robots to personalize services such as emotion coaching. To address the dearth of investigations, the present study explores the possibility of estimating personal resilience using physiological and speech signals measured during human–robot conversations. Specifically, the physiological and speech signals of 32 research participants were recorded while the participants answered a humanoid social robot’s questions about their positive and negative memories about three periods of their lives. The results from machine learning models showed that heart rate variability and paralinguistic features were the overall best predictors of personal resilience. Such predictability of personal resilience can be leveraged by AI and social robots to improve user understanding and has great potential for various mental healthcare applications in the future. Full article

(This article belongs to the Special Issue Social Robots in Healthcare)

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18 pages, 11655 KiB

Open AccessArticle

Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots

by Samuel-Felipe Baltanas, Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez

Sensors 2021, 21(2), 659; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020659 - 19 Jan 2021

Cited by 3 | Viewed by 3028

Abstract

Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assistance like education and healthcare, or assisting humans in everyday tasks. These unconstrained environments present additional difficulties for face recognition, extreme head pose variability being one of the most challenging. In this paper, we address this issue and make a fourfold contribution. First, it has been designed a tool for gathering an uniform distribution of head pose images from a person, which has been used to collect a new dataset of faces, both presented in this work. Then, the dataset has served as a testbed for analyzing the detrimental effects this problem has on a number of state-of-the-art methods, showing their decreased effectiveness outside a limited range of poses. Finally, we propose an optimization method to mitigate said negative effects by considering key pose samples in the recognition system’s set of known faces. The conducted experiments demonstrate that this optimized set of poses significantly improves the performance of a state-of-the-art, cutting-edge system based on Multitask Cascaded Convolutional Neural Networks (MTCNNs) and ArcFace. Full article

(This article belongs to the Special Issue Social Robots in Healthcare)

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