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14 pages, 5564 KiB

Open AccessArticle

Validation of a Robotic Testbench for Evaluating Biomechanical Effects of Implant Rotation in Total Knee Arthroplasty on a Cadaveric Specimen

by Nikolas Wilhelm, Constantin von Deimling, Sami Haddadin, Claudio Glowalla and Rainer Burgkart

Sensors 2023, 23(17), 7459; https://doi.org/10.3390/s23177459 - 27 Aug 2023

Cited by 1 | Viewed by 1411

Abstract

In this study, we developed and validated a robotic testbench to investigate the biomechanical compatibility of three total knee arthroplasty (TKA) configurations under different loading conditions, including varus–valgus and internal–external loading across defined flexion angles. The testbench captured force–torque data, position, and quaternion [...] Read more.

In this study, we developed and validated a robotic testbench to investigate the biomechanical compatibility of three total knee arthroplasty (TKA) configurations under different loading conditions, including varus–valgus and internal–external loading across defined flexion angles. The testbench captured force–torque data, position, and quaternion information of the knee joint. A cadaver study was conducted, encompassing a native knee joint assessment and successive TKA testing, featuring femoral component rotations at −5°, 0°, and +5° relative to the transepicondylar axis of the femur. The native knee showed enhanced stability in varus–valgus loading, with the +5° external rotation TKA displaying the smallest deviation, indicating biomechanical compatibility. The robotic testbench consistently demonstrated high precision across all loading conditions. The findings demonstrated that the TKA configuration with a +5° external rotation displayed the minimal mean deviation under internal–external loading, indicating superior joint stability. These results contribute meaningful understanding regarding the influence of different TKA configurations on knee joint biomechanics, potentially influencing surgical planning and implant positioning. We are making the collected dataset available for further biomechanical model development and plan to explore the 6 Degrees of Freedom (DOF) robotic platform for additional biomechanical analysis. This study highlights the versatility and usefulness of the robotic testbench as an instrumental tool for expanding our understanding of knee joint biomechanics. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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Figure 1
Testbench architecture consisting of an optical measuring system (I), the 6 DOF Robot Stäubli RX90B (II) with an additional 6 DOF force–torque sensor for external load acquisition of the respective joint, CT imaging (III), and the central evaluation system (IV). Full article ">Figure 2
Sequential steps of the modified total knee arthroplasty (TKA) procedure on a cadaveric knee joint: (a) opening of the joint capsule to expose the femur, tibia, and patella; (b) resection of the proximal tibia; (c) resection of the distal femur; (d) implantation of the tibial component and femoral adapter; and (e) switching femoral shields with different internal–external rotations (−5°, 0°, and +5°) during biomechanical testing (f). Full article ">Figure 3
(a) 3D Slicer segmentation of the cadaveric knee joint, demonstrating the tibia, fibula, patella, and femur, along with the femoral (upper blue spheres) and tibial (lower blue spheres) trackers. (b) Tailored femoral component designs modeled on Triathlon CR ([<a href="#B37-sensors-23-07459" class="html-bibr">37</a>] GmbH, Duisburg, Germany) implants, resized and rotated to −5° (internal), 0° (neutral), and +5° (external). The femoral components are displayed on the transverse (axial) plane, viewed distally, as indicated by the direction of the black arrow in (a). Full article ">Figure 4
Visualization of the complete test setup (a), the synchronization and movement of the femur tracker (femur, blue) and the end-effector of the robot (eff, orange) (b), as well as the acquired force (c) and torque (d) data during the test of the native knee. Full article ">Figure 5
Comparison of the maximum deviation angles and their corresponding standard deviations for (a) varus, (b) valgus, (c) internal, and (d) external loading at 5 Nm and three repetitions of the test. The measurements were taken at distinct flexion angles of 10°, 20°, 30°, 60°, and 90° for both the native knee and total knee arthroplasty (TKA) configurations with 0°, −5°, and +5° implant rotations. Full article ">Figure 6
Kiviat Diagram of the Total Deviation Angle (TDA) as a measure for instability in internal/external and varus/valgus loading for native knee and various TKA variations (neutral 0°, −5° internal, and +5° external) across different flexion angles under 5 Nm loading conditions. (a) Varus/valgus loading and (b) internal/external loading. The presented results display the sum of the deviations for varus/valgus and internal/external loading, illustrating the impact of different TKA variation strategies on knee joint biomechanics. Full article ">

17 pages, 10382 KiB

Open AccessArticle

Hold My Hand: Development of a Force Controller and System Architecture for Joint Walking with a Companion Robot

by Enrique Coronado, Toshifumi Shinya and Gentiane Venture

Sensors 2023, 23(12), 5692; https://doi.org/10.3390/s23125692 - 18 Jun 2023

Viewed by 1773

Abstract

In recent years, there has been a growing interest in the development of robotic systems for improving the quality of life of individuals of all ages. Specifically, humanoid robots offer advantages in terms of friendliness and ease of use in such applications. This [...] Read more.

In recent years, there has been a growing interest in the development of robotic systems for improving the quality of life of individuals of all ages. Specifically, humanoid robots offer advantages in terms of friendliness and ease of use in such applications. This article proposes a novel system architecture that enables a commercial humanoid robot, specifically the Pepper robot, to walk side-by-side while holding hands, and communicating by responding to the surrounding environment. To achieve this control, an observer is required to estimate the force applied to the robot. This was accomplished by comparing joint torques calculated from the dynamics model to actual current measurements. Additionally, object recognition was performed using Pepper’s camera to facilitate communication in response to surrounding objects. By integrating these components, the system has demonstrated its capability to achieve its intended purpose. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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17 pages, 2040 KiB

Open AccessArticle

Behavioural Models of Risk-Taking in Human–Robot Tactile Interactions

by Qiaoqiao Ren, Yuanbo Hou, Dick Botteldooren and Tony Belpaeme

Sensors 2023, 23(10), 4786; https://doi.org/10.3390/s23104786 - 16 May 2023

Viewed by 1623

Abstract

Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change [...] Read more.

Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change how much people are willing to take risks. This study further develops our understanding of the relationship between human risk-taking behaviour, the physiological responses by the user, and the intensity of the tactile interaction with a social robot. We used data collected with physiological sensors during the playing of a risk-taking game (the Balloon Analogue Risk Task, or BART). The results of a mixed-effects model were used as a baseline to predict risk-taking propensity from physiological measures, and these results were further improved through the use of two machine learning techniques—support vector regression (SVR) and multi-input convolutional multihead attention (MCMA)—to achieve low-latency risk-taking behaviour prediction during human–robot tactile interaction. The performance of the models was evaluated based on mean absolute error (MAE), root mean squared error (RMSE), and R squared score (

R^{2}

), which obtained the optimal result with MCMA yielding an MAE of 3.17, an RMSE of 4.38, and an

R^{2}

of 0.93 compared with the baseline of 10.97 MAE, 14.73 RMSE, and 0.30

R^{2}

. The results of this study offer new insights into the interplay between physiological data and the intensity of risk-taking behaviour in predicting human risk-taking behaviour during human–robot tactile interactions. This work illustrates that physiological activation and the intensity of tactile interaction play a prominent role in risk processing during human–robot tactile interaction and demonstrates that it is feasible to use human physiological data and behavioural data to predict risk-taking behaviour in human–robot tactile interaction. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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14 pages, 3209 KiB

Open AccessArticle

A Predictable Obstacle Avoidance Model Based on Geometric Configuration of Redundant Manipulators for Motion Planning

by Fengjia Ju, Hongzhe Jin, Binluan Wang and Jie Zhao

Sensors 2023, 23(10), 4642; https://doi.org/10.3390/s23104642 - 10 May 2023

Cited by 2 | Viewed by 1520

Abstract

When a manipulator works in dynamic environments, it may be affected by obstacles and may cause danger to people around. This requires the manipulator to be able to plan the obstacle avoidance motion in real time. Therefore, the problem solved in this paper [...] Read more.

When a manipulator works in dynamic environments, it may be affected by obstacles and may cause danger to people around. This requires the manipulator to be able to plan the obstacle avoidance motion in real time. Therefore, the problem solved in this paper is dynamic obstacle avoidance with the whole body of the redundant manipulator. The difficulty of this problem is how to model the manipulator to reflect the motion relationship between the manipulator and the obstacle. In order to describe accurately the occurrence conditions of the collision, we propose the triangular collision plane, a predictable obstacle avoidance model based on the geometric configuration of the manipulator. Based on this model, three cost functions, including the cost of the motion state, the cost of a head-on collision, and the cost of the approach time, are established and regarded as optimization objectives in the inverse kinematics solution of the redundant manipulator combined with the gradient projection method. The simulations and experiments on the redundant manipulator and the comparison with the distance-based obstacle avoidance point method show that our method improves the response speed of the manipulator and the safety of the system. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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20 pages, 4030 KiB

Open AccessArticle

Spatial Calibration of Humanoid Robot Flexible Tactile Skin for Human–Robot Interaction

by Sélim Chefchaouni Moussaoui, Rafael Cisneros-Limón, Hiroshi Kaminaga, Mehdi Benallegue, Taiki Nobeshima, Shusuke Kanazawa and Fumio Kanehiro

Sensors 2023, 23(9), 4569; https://doi.org/10.3390/s23094569 - 8 May 2023

Cited by 2 | Viewed by 3105

Abstract

Recent developments in robotics have enabled humanoid robots to be used in tasks where they have to physically interact with humans, including robot-supported caregiving. This interaction—referred to as physical human–robot interaction (pHRI)—requires physical contact between the robot and the human body; one way [...] Read more.

Recent developments in robotics have enabled humanoid robots to be used in tasks where they have to physically interact with humans, including robot-supported caregiving. This interaction—referred to as physical human–robot interaction (pHRI)—requires physical contact between the robot and the human body; one way to improve this is to use efficient sensing methods for the physical contact. In this paper, we use a flexible tactile sensing array and integrate it as a tactile skin for the humanoid robot HRP-4C. As the sensor can take any shape due to its flexible property, a particular focus is given on its spatial calibration, i.e., the determination of the locations of the sensor cells and their normals when attached to the robot. For this purpose, a novel method of spatial calibration using B-spline surfaces has been developed. We demonstrate with two methods that this calibration method gives a good approximation of the sensor position and show that our flexible tactile sensor can be fully integrated on a robot and used as input for robot control tasks. These contributions are a first step toward the use of flexible tactile sensors in pHRI applications. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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16 pages, 6539 KiB

Open AccessArticle

A Trade-Off between Complexity and Interaction Quality for Upper Limb Exoskeleton Interfaces

by Dorian Verdel, Guillaume Sahm, Olivier Bruneau, Bastien Berret and Nicolas Vignais

Sensors 2023, 23(8), 4122; https://doi.org/10.3390/s23084122 - 20 Apr 2023

Cited by 2 | Viewed by 2531

Abstract

Exoskeletons are among the most promising devices dedicated to assisting human movement during reeducation protocols and preventing musculoskeletal disorders at work. However, their potential is currently limited, partially because of a fundamental contradiction impacting their design. Indeed, increasing the interaction quality often requires [...] Read more.

Exoskeletons are among the most promising devices dedicated to assisting human movement during reeducation protocols and preventing musculoskeletal disorders at work. However, their potential is currently limited, partially because of a fundamental contradiction impacting their design. Indeed, increasing the interaction quality often requires the inclusion of passive degrees of freedom in the design of human-exoskeleton interfaces, which increases the exoskeleton’s inertia and complexity. Thus, its control also becomes more complex, and unwanted interaction efforts can become important. In the present paper, we investigate the influence of two passive rotations in the forearm interface on sagittal plane reaching movements while keeping the arm interface unchanged (i.e., without passive degrees of freedom). Such a proposal represents a possible compromise between conflicting design constraints. The in-depth investigations carried out here in terms of interaction efforts, kinematics, electromyographic signals, and subjective feedback of participants all underscored the benefits of such a design. Therefore, the proposed compromise appears to be suitable for rehabilitation sessions, specific tasks at work, and future investigations into human movement using exoskeletons. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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20 pages, 3775 KiB

Open AccessArticle

Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology

by Chih-Feng Cheng and Chiuhsiang Joe Lin

Sensors 2023, 23(6), 2920; https://doi.org/10.3390/s23062920 - 8 Mar 2023

Cited by 1 | Viewed by 4100

Abstract

In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants’ self-evaluation. Affective computing uses biometrics to recognize people’s emotional states as they interact with a product. However, the cost of medical-grade [...] Read more.

In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants’ self-evaluation. Affective computing uses biometrics to recognize people’s emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform’s effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human–robot interaction. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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13 pages, 1678 KiB

Open AccessArticle

Quantification of Comfort for the Development of Binding Parts in a Standing Rehabilitation Robot

by Yejin Nam, Sumin Yang, Jongman Kim, Bummo Koo, Sunghyuk Song and Youngho Kim

Sensors 2023, 23(4), 2206; https://doi.org/10.3390/s23042206 - 16 Feb 2023

Cited by 1 | Viewed by 2978

Abstract

Human-machine interfaces (HMI) refer to the physical interaction between a user and rehabilitation robots. A persisting excessive load leads to soft tissue damage, such as pressure ulcers. Therefore, it is necessary to define a comfortable binding part for a rehabilitation robot with the [...] Read more.

Human-machine interfaces (HMI) refer to the physical interaction between a user and rehabilitation robots. A persisting excessive load leads to soft tissue damage, such as pressure ulcers. Therefore, it is necessary to define a comfortable binding part for a rehabilitation robot with the subject in a standing posture. The purpose of this study was to quantify the comfort at the binding parts of the standing rehabilitation robot. In Experiment 1, cuff pressures of 10–40 kPa were applied to the thigh, shank, and knee of standing subjects, and the interface pressure and pain scale were obtained. In Experiment 2, cuff pressures of 10–20 kPa were applied to the thigh, and the tissue oxygen saturation and the skin temperature were measured. Questionnaire responses regarding comfort during compression were obtained from the subjects using the visual analog scale and the Likert scale. The greatest pain was perceived in the thigh. The musculoskeletal configuration affected the pressure distribution. The interface pressure distribution by the binding part showed higher pressure at the intermuscular septum. Tissue oxygen saturation (StO₂) increased to 111.9 ± 6.7% when a cuff pressure of 10 kPa was applied and decreased to 92.2 ± 16.9% for a cuff pressure of 20 kPa. A skin temperature variation greater than 0.2 °C occurred in the compressed leg. These findings would help evaluate and improve the comfort of rehabilitation robots. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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13 pages, 1589 KiB

Open AccessArticle

Dataset with Tactile and Kinesthetic Information from a Human Forearm and Its Application to Deep Learning

by Francisco Pastor, Da-hui Lin-Yang, Jesús M. Gómez-de-Gabriel and Alfonso J. García-Cerezo

Sensors 2022, 22(22), 8752; https://doi.org/10.3390/s22228752 - 12 Nov 2022

Cited by 2 | Viewed by 2211

Abstract

There are physical Human–Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a safe manipulation of [...] Read more.

There are physical Human–Robot Interaction (pHRI) applications where the robot has to grab the human body, such as rescue or assistive robotics. Being able to precisely estimate the grasping location when grabbing a human limb is crucial to perform a safe manipulation of the human. Computer vision methods provide pre-grasp information with strong constraints imposed by the field environments. Force-based compliant control, after grasping, limits the amount of applied strength. On the other hand, valuable tactile and proprioceptive information can be obtained from the pHRI gripper, which can be used to better know the features of the human and the contact state between the human and the robot. This paper presents a novel dataset of tactile and kinesthetic data obtained from a robot gripper that grabs a human forearm. The dataset is collected with a three-fingered gripper with two underactuated fingers and a fixed finger with a high-resolution tactile sensor. A palpation procedure is performed to record the shape of the forearm and to recognize the bones and muscles in different sections. Moreover, an application for the use of the database is included. In particular, a fusion approach is used to estimate the actual grasped forearm section using both kinesthetic and tactile information on a regression deep-learning neural network. First, tactile and kinesthetic data are trained separately with Long Short-Term Memory (LSTM) neural networks, considering the data are sequential. Then, the outputs are fed to a Fusion neural network to enhance the estimation. The experiments conducted show good results in training both sources separately, with superior performance when the fusion approach is considered. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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Figure 1
The robotic manipulator with the three-fingered gripper performs a grasp to recognize the location of the grasped forearm. Bones and muscles present different shapes and sizes along the forearm. The 3D images of the musculoskeletal model at the bottom are from BioDigital [<a href="#B32-sensors-22-08752" class="html-bibr">32</a>]. Full article ">Figure 2
Experimental setup used to obtain a forearm dataset. A description of the elements of the underactuated gripper attached to the forearm measurement device. Full article ">Figure 3
Kinematic design of the gripper, defining the two underactuated fingers parameters and the fixed finger. Full article ">Figure 4
Flowchart of the steps needed to record a full haptic dataset using the forearm measurement device. Full article ">Figure 5
Time sequences of tactile and kinesthetic information are excerpted during the grasping process. A collection of tactile images composing tactile information (a). Five tactile images are presented in this figure to demonstrate how the pressure distribution evolves along the sequence, distinguishing the bones and muscles of the forearm. The variation in the joint position of the underactuated fingers’ joints produces kinesthetic information (b). Here, <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>l</mi> <mi>a</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>r</mi> <mi>a</mi> </mrow> </msub> </semantics></math> denote the left and right fingers’ actuated joint angles, respectively, while <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>l</mi> <mi>p</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>θ</mi> <mrow> <mi>r</mi> <mi>p</mi> </mrow> </msub> </semantics></math> denote the left and right fingers’ underactuated joint angles. Full article ">Figure 6
Diagram of the regression neural network structure. Kinesthetic and tactile data are trained separately. The kinesthetic network is defined by three LSTM layers and a fully connected layer. The tactile network is formed by a convolutional LSTM layer with a convolutional layer and two fully connected layers. Both outputs are concatenated and fed to a fully connected layer fusion network that uses both sources of information to improve the output. Full article ">Figure 7
The median, 25/75 percentiles, and the maximum and the minimum estimation output of each <math display="inline"><semantics> <msub> <mi mathvariant="script">X</mi> <mo>%</mo> </msub> </semantics></math>. The test is presented for the tactile, kinesthetic, and fusion neural networks. Full article ">Figure 8
The root mean squared error (RMSE) and the medium absolute error (MAE) achieved for each <math display="inline"><semantics> <msub> <mi mathvariant="script">X</mi> <mo>%</mo> </msub> </semantics></math> tested. Both errors are presented for the kinesthetic, the tactile, and the fusion neural networks. Full article ">

Review

Jump to: Research, Other

33 pages, 2003 KiB

Open AccessReview

Human Factors Considerations for Quantifiable Human States in Physical Human–Robot Interaction: A Literature Review

by Nourhan Abdulazeem and Yue Hu

Sensors 2023, 23(17), 7381; https://doi.org/10.3390/s23177381 - 24 Aug 2023

Cited by 4 | Viewed by 2894

Abstract

As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The [...] Read more.

As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The successful integration of robots as domestic service providers in our lives requires them to possess efficient manipulation capabilities, provide effective physical assistance, and have adaptive control frameworks that enable them to develop social understanding during human–robot interaction. In this context, human factors, especially quantifiable ones, represent a necessary component. The objective of this paper is to conduct an unbiased review encompassing the studies on human factors studied in research involving physical interactions and strong manipulation capabilities. We identified the prevalent human factors in physical human–robot interaction (pHRI), noted the factors typically addressed together, and determined the frequently utilized assessment approaches. Additionally, we gathered and categorized proposed quantification approaches based on the measurable data for each human factor. We also formed a map of the common contexts and applications addressed in pHRI for a comprehensive understanding and easier navigation of the field. We found out that most of the studies in direct pHRI (when there is direct physical contact) focus on social behaviors with belief being the most commonly addressed human factor type. Task collaboration is moderately investigated, while physical assistance is rarely studied. In contrast, indirect pHRI studies (when the physical contact is mediated via a third item) often involve industrial settings, with physical ergonomics being the most frequently investigated human factor. More research is needed on the human factors in direct and indirect physical assistance applications, including studies that combine physical social behaviors with physical assistance tasks. We also found that while the predominant approach in most studies involves the use of questionnaires as the main method of quantification, there is a recent trend that seeks to address the quantification approaches based on measurable data. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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25 pages, 377 KiB

Open AccessReview

A Review on Human Comfort Factors, Measurements, and Improvements in Human–Robot Collaboration

by Yuchen Yan and Yunyi Jia

Sensors 2022, 22(19), 7431; https://doi.org/10.3390/s22197431 - 30 Sep 2022

Cited by 19 | Viewed by 4794

Abstract

As the development of robotics technologies for collaborative robots (COBOTs), the applications of human–robot collaboration (HRC) have been growing in the past decade. Despite the tremendous efforts from both academia and industry, the overall usage and acceptance of COBOTs are still not so [...] Read more.

As the development of robotics technologies for collaborative robots (COBOTs), the applications of human–robot collaboration (HRC) have been growing in the past decade. Despite the tremendous efforts from both academia and industry, the overall usage and acceptance of COBOTs are still not so high as expected. One of the major affecting factors is the comfort of humans in HRC, which is usually less emphasized in COBOT development; however, it is critical to the user acceptance during HRC. Therefore, this paper gives a review of human comfort in HRC including the influential factors of human comfort, measurement of human comfort in terms of subjective and objective manners, and human comfort improvement approaches in the context of HRC. Discussions on each topic are also conducted based on the review and analysis. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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Other

Jump to: Research, Review

26 pages, 1454 KiB

Open AccessPerspective

SoftSAR: The New Softer Side of Socially Assistive Robots—Soft Robotics with Social Human–Robot Interaction Skills

by Yu-Chen Sun, Meysam Effati, Hani E. Naguib and Goldie Nejat

Sensors 2023, 23(1), 432; https://doi.org/10.3390/s23010432 - 30 Dec 2022

Cited by 2 | Viewed by 5015

Abstract

When we think of “soft” in terms of socially assistive robots (SARs), it is mainly in reference to the soft outer shells of these robots, ranging from robotic teddy bears to furry robot pets. However, soft robotics is a promising field that has [...] Read more.

When we think of “soft” in terms of socially assistive robots (SARs), it is mainly in reference to the soft outer shells of these robots, ranging from robotic teddy bears to furry robot pets. However, soft robotics is a promising field that has not yet been leveraged by SAR design. Soft robotics is the incorporation of smart materials to achieve biomimetic motions, active deformations, and responsive sensing. By utilizing these distinctive characteristics, a new type of SAR can be developed that has the potential to be safer to interact with, more flexible, and uniquely uses novel interaction modes (colors/shapes) to engage in a heighted human–robot interaction. In this perspective article, we coin this new collaborative research area as SoftSAR. We provide extensive discussions on just how soft robotics can be utilized to positively impact SARs, from their actuation mechanisms to the sensory designs, and how valuable they will be in informing future SAR design and applications. With extensive discussions on the fundamental mechanisms of soft robotic technologies, we outline a number of key SAR research areas that can benefit from using unique soft robotic mechanisms, which will result in the creation of the new field of SoftSAR. Full article

(This article belongs to the Special Issue Robotic Contact with the Human Body in Physical Human–Robot Interaction)

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Figure 1
(a) The CASTOR robot’s soft outer shell has the ability to absorb impact, courtesy of Casas-Bocanegra et al. [<a href="#B21-sensors-23-00432" class="html-bibr">21</a>], (b) the SEA neck joint and the integrated element connecting the head and the body in the CASTOR robot, courtesy of Casas-Bocanegra et al. [<a href="#B21-sensors-23-00432" class="html-bibr">21</a>], (c) the Probo robot with a removable soft jacket and expressive trunk, courtesy of Saldien et al [<a href="#B38-sensors-23-00432" class="html-bibr">38</a>], and (d) the schematic of the NBDS actuation system for Probo showing the worm gear design, courtesy of Saldien et al [<a href="#B32-sensors-23-00432" class="html-bibr">32</a>]. All images are courtesy of open access articles. Full article ">Figure 2
(a) An example of a PN soft-robotic gripper manipulating an object. The gripper also has color-changing ability due to covalent bond activation, courtesy of Gossweiler et al. [<a href="#B56-sensors-23-00432" class="html-bibr">56</a>] with permission obtained from ACS Publications; (b) an example of variable-length tendon-driven octopus-inspired robotic arm, which utilizes SMA springs in both the transverse and longitudinal direction; courtesy of Cianchetti et al. [<a href="#B61-sensors-23-00432" class="html-bibr">61</a>], open access article; and (c) examples of ionic EAP actuator motion under different DC voltages, courtesy of Park et al. [<a href="#B66-sensors-23-00432" class="html-bibr">66</a>], open access article. Full article ">Figure 3
The Pepper robot displaying different emotions using nonverbal communication via its head, torso, and arm movements and changing eye colors: (a) positive valence and high arousal; and (b) negative valence and low arousal; courtesy of Shao et al. [<a href="#B116-sensors-23-00432" class="html-bibr">116</a>], open access article. Full article ">

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