Effective Human-Robot Collaboration Through Wearable Sensors

Al-Yacoub, Ali; Buerkle, Achim; Flanagan, Myles; Ferreira, Pedro; Hubbard, Ella‐Mae; Lohse, Niels

doi:10.1109/etfa46521.2020.9212100

Cited by 21 publications

(14 citation statements)

References 35 publications

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“…Muscular and Skeletal Positioning tracking can be used as a physiological measurement to link a person's posture to a mental state [28]. Yet, the linkage between speci c postures and mental workload is currently considered as challenging [37].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Buerkle

Matharu

Al-Yacoub

et al. 2021

Preprint

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Manufacturing challenges are increasing the demands for more agile and dexterous means of production. At the same time, these systems aim to maintain or even increase productivity. The challenges risen from these developments can be tackled through Human-Robot Collaboration (HRC). HRC requires effective task distribution according to each parties’ distinctive strengths, which is envisioned to generate synergetic effects. To enable a seamless collaboration, the human and robot require a mutual awareness, which is challenging, due to the human and robot “speaking” different languages as in analogue and digital. Thus, this challenge can be addressed by equipping the robot with a model of the human. Despite a range of models being available, data-driven models of the human are still at an early stage. This paper proposes an adaptive human sensor framework, which incorporates objective, subjective, and physiological metrics, as well as associated Machine Learning. Thus, it is envisioned to adapt to the uniqueness and dynamic nature of human behavior. To test the framework, a validation experiment was performed, including 18 participants, which aims to predict Perceived Workload during two scenarios, namely a manual and an HRC assembly task. Perceived Workloads are described to have a substantial impact on a human operator’s task performance. Throughout the experiment physiological data from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiration sensor was collected and interpreted. For subjective metrics, the standardized NASA Task Load Index was used. Objective metrics included task completion time and number of errors/assistance requests. Overall, the framework revealed a promising potential towards an adaptive behavior, which is ultimately envisioned to enable a more effective HRC.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Buerkle

Matharu

Al-Yacoub

et al. 2021

Preprint

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“…Loose connections were causing electrical noise and wires outside the box caused issues as they interfered with the user's movements. [10]. New design iterations were developed and assessed against the PDS until the most viable solution was created.…”

Section: Design Methodology and Approachmentioning

confidence: 99%

Wearable Sensor Tools for Efficient Human Robot Interaction

Workman¹,

Al-Yacoub²,

Hubbard³

2021

UK-RAS Conference for PhD and Early Career Researchers Proceedings

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Human-robot interaction (HRI) technology is growing rapidly, with many products being designed to operate outside the industry, such as in the home. However, controlling robots in a domestic setting is challenging due to high variations in such environments. Also, human behaviour varies, and the same individual might behave differently in similar situations. In this paper, the design of a wearable data acquisition system, comprised of wearable sensors and a control box that communicates wirelessly with sensors on the robot to improve safe human-robot collaboration on tasks, is revised. The literature review identifies factors that affect the robotic system's wearability and leads to the production of a Product Design Specification (PDS), against which the original and subsequent designs of the set-up are evaluated. Several iterations are assessed, and how limitations in the original design are overcome are explained with reference to the system's location, ergonomics, and wearability. The final prototype is then tested on human users using 'range of motion' and 'representative task' experiments to assess its improved wearability. The potential applications of the device in the domestic environment are explained and suggestions for the future scope of the research made.

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“…This prevents the participants from verbally sharing their intention. The follower was equipped with muscle activity sensors, which were integrated with the Robot Operating System (ROS) network [43].…”

Section: Weights Vicon Markersmentioning

confidence: 99%

Data-Driven Modelling of Human-Human Co-Manipulation Using Force and Muscle Surface Electromyogram Activities

et al. 2021

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With collaborative robots and the recent developments in manufacturing technologies, physical interactions between humans and robots represent a vital role in performing collaborative tasks. Most previous studies have focused on robot motion planning and control during the execution of the task. However, further research is required for direct physical contact for human-robot or robot-robot interactions, such as co-manipulation. In co-manipulation, a human operator manipulates a shared load with a robot through a semi-structured environment. In such scenarios, a multi-contact point with the environment during the task execution results in a convoluted force/toque signature that is difficult to interpret. Therefore, in this paper, a muscle activity sensor in the form of an electromyograph (EMG) is employed to improve the mapping between force/torque and displacements in co-manipulation tasks. A suitable mapping was identified by comparing the root mean square error amongst data-driven models, mathematical models, and hybrid models. Thus, a robot was shown to effectively and naturally perform the required co-manipulation with a human. This paper’s proposed hypotheses were validated using an unseen test dataset and a simulated co-manipulation experiment, which showed that the EMG and data-driven model improved the mapping of the force/torque features into displacements.

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Effective Human-Robot Collaboration Through Wearable Sensors

Cited by 21 publications

References 35 publications

An Adaptive Human Sensor Framework for Human-Robot Collaboration

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Wearable Sensor Tools for Efficient Human Robot Interaction

Data-Driven Modelling of Human-Human Co-Manipulation Using Force and Muscle Surface Electromyogram Activities

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