Multijoint passive elastic spine exoskeleton for stoop lifting assistance

In the task of carrying heavy objects, it is easy to cause back injuries and other musculoskeletal diseases. Although wearable robots are designed to reduce this danger, most existing exoskeletons use high-stiffness mechanisms, which are beneficial to load-bearing conduction, but this restricts the natural movement of the human body, thereby causing ergonomic risks. This article proposes a back exoskeleton composed of multiple elastic spherical hinges inspired by the biological spine. This spine exoskeleton can assist in the process of bending the body and ensure flexibility. We deduced the kinematics model of this mechanism and established an analytical biomechanical model of human–robot interaction. The mechanism of joint assistance of the spine exoskeleton was discussed, and experiments were conducted to verify the flexibility of the spine exoskeleton and the effectiveness of the assistance during bending.

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CAD Design of Human Male Body for Mass–Inertial Characteristics Studies

MATEC Web of Conferences ◽

10.1051/matecconf/201814504006 ◽

2018 ◽

Vol 145 ◽

pp. 04006

Author(s):

Gergana Nikolova ◽

Vladimir Kotev ◽

Daniel Dantchev

Keyword(s):

Human Body ◽

Space Exploration ◽

Biomechanical Model ◽

Standing Position ◽

The Body ◽

Male Body ◽

Furniture Design ◽

Wearable Robots ◽

Human Male ◽

Car Crash

The aim of the present research is to present a 16-segmental biomechanical model of the Bulgarian male to determine the mass-inertial characteristics of the body of the Bulgarian male based on parameters available in the literature and its 3D generation within SolidWorks software. The motivation of the research is to support mainly sport, rehabilitation, wearable robots and furniture design users. The proposed CAD model of the human body of men is verified against the analytical results from our previous investigation, as well as through comparison with data available in the provided references. In this paper we model two basic human body positions: standing position and sitting with thighs elevated. The comparison performed between our model results and data reported in literature gives us confidence that this model can be reliably used to calculate the mass-inertial characteristics of male body at any postures of the body that is of interest. Therefore, our model can be used to obtain data for positions which the human body has to take in everyday live, in sport, leisure, including space exploration, for investigating criminology cases – body fall, car crash, etc. The model is suitable for performing computer simulation in robotics, medicine, sport and other areas.

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Human Body Pose Interpretation and Classification for Social Human-Robot Interaction

International Journal of Social Robotics ◽

10.1007/s12369-011-0099-6 ◽

2011 ◽

Vol 3 (3) ◽

pp. 313-332 ◽

Cited By ~ 19

Author(s):

Derek McColl ◽

Zhe Zhang ◽

Goldie Nejat

Keyword(s):

Human Body ◽

Human Robot Interaction ◽

Robot Interaction

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Socially Embodied Human-Robot Interaction

Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-7278-9.ch007 ◽

2015 ◽

pp. 169-190 ◽

Cited By ~ 3

Author(s):

J. Lindblom ◽

B. Alenljung

Keyword(s):

User Experience ◽

Human Robot Interaction ◽

The Body ◽

Human Interaction ◽

Future Research ◽

Crucial Importance ◽

Robot Interaction ◽

Use Of Technology ◽

Fundamental Challenge ◽

Socially Interactive Robots

A fundamental challenge of human interaction with socially interactive robots, compared to other interactive products, comes from them being embodied. The embodied nature of social robots questions to what degree humans can interact ‘naturally' with robots, and what impact the interaction quality has on the user experience (UX). UX is fundamentally about emotions that arise and form in humans through the use of technology in a particular situation. This chapter aims to contribute to the field of human-robot interaction (HRI) by addressing, in further detail, the role and relevance of embodied cognition for human social interaction, and consequently what role embodiment can play in HRI, especially for socially interactive robots. Furthermore, some challenges for socially embodied interaction between humans and socially interactive robots are outlined and possible directions for future research are presented. It is concluded that the body is of crucial importance in understanding emotion and cognition in general, and, in particular, for a positive user experience to emerge when interacting with socially interactive robots.

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Research on Discriminative Skeleton-Based Action Recognition in Spatiotemporal Fusion and Human-Robot Interaction

Complexity ◽

10.1155/2020/8717942 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Qiubo Zhong ◽

Caiming Zheng ◽

Haoxiang Zhang

Keyword(s):

Action Recognition ◽

Human Body ◽

Sequence Data ◽

Spatial Relationship ◽

Human Robot Interaction ◽

Robot Interaction ◽

Convolutional Network ◽

Action Sequence ◽

Interaction System ◽

High Discrimination

A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system.

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Gestures Identification for Myoelectric Based Control of the Robotic Arm

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst12514 ◽

2020 ◽

Vol 22 (12) ◽

Author(s):

Adhau P ◽

◽

Kadwane S. G ◽

Shital Telrandhe ◽

Rajguru V. S ◽

...

Keyword(s):

Neural Network ◽

Human Body ◽

Human Robot Interaction ◽

Robotic Arm ◽

Human Hand ◽

Robot Interaction ◽

The Neural Network

Human robot interaction have been ever the topic of research to research scholars owing to its importance to help humanity. Robust human interacting robot where commands from Electromyogram (EMG) signals is recently being investigated. This article involves study of motions a system that allows signals recorded directly from a human body and thereafter can be used for control of a small robotic arm. The various gestures are recognized by placing the electrodes or sensors on the human hand. These gestures are then identified by using neural network. The neural network will thus train the signals. The offline control of the arm is done by controlling the motors of the robotic arm.

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Erratum to: Physical human-robot interaction of an active pelvis orthosis: toward ergonomic assessment of wearable robots

Journal of NeuroEngineering and Rehabilitation ◽

10.1186/s12984-017-0262-x ◽

2017 ◽

Vol 14 (1) ◽

Author(s):

Nicolò d’Elia ◽

Federica Vanetti ◽

Marco Cempini ◽

Guido Pasquini ◽

Andrea Parri ◽

...

Keyword(s):

Human Robot Interaction ◽

Robot Interaction ◽

Wearable Robots

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Ergodicity reveals assistance and learning from physical human-robot interaction

Science Robotics ◽

10.1126/scirobotics.aav6079 ◽

2019 ◽

Vol 4 (29) ◽

pp. eaav6079

Author(s):

Kathleen Fitzsimons ◽

Ana Maria Acosta ◽

Julius P. A. Dewald ◽

Todd D. Murphey

Keyword(s):

Human Perception ◽

Human Motion ◽

Human Robot Interaction ◽

The Body ◽

Human Motion Analysis ◽

Robotic Assistance ◽

Information Channel ◽

Neural Encoding ◽

Robot Interaction ◽

Information Theoretic

This paper applies information theoretic principles to the investigation of physical human-robot interaction. Drawing from the study of human perception and neural encoding, information theoretic approaches offer a perspective that enables quantitatively interpreting the body as an information channel and bodily motion as an information-carrying signal. We show that ergodicity, which can be interpreted as the degree to which a trajectory encodes information about a task, correctly predicts changes due to reduction of a person’s existing deficit or the addition of algorithmic assistance. The measure also captures changes from training with robotic assistance. Other common measures for assessment failed to capture at least one of these effects. This information-based interpretation of motion can be applied broadly, in the evaluation and design of human-machine interactions, in learning by demonstration paradigms, or in human motion analysis.

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