Monitoring muscle fatigue following continuous load changes

Department of Human Factors EngineeringPrevious studies related to monitoring muscle fatigue during dynamic motion have focused on detecting the accumulation of muscle fatigue. However, it is necessary to detect both accumulation and recovery of muscle fatigue in dynamic muscle contraction while muscle load changes continuously. This study aims to investigate the development and recovery of muscle fatigue in dynamic muscle contraction conditions following continuous load changes. Twenty healthy males conducted repetitive elbow flexion and extension using 2kg and 1kg dumbbell, by turns. They performed the two tasks of different intensity (2kg intensity task, 1kg intensity task) alternately until they felt they could no longer achieve the required movement range or until they experienced unacceptable biceps muscle discomfort. Meanwhile, using EMG signal of biceps brachii muscle, fatigue detections were performed from both dynamic measurements during each dynamic muscle contraction task and isometric measurements during isometric muscle contraction right before and after each task. In each of 2kg and 1kg intensity tasks, pre, post and change value of EMG amplitude (AEMG) and center frequency were computed respectively. They were compared to check the validity of the muscle fatigue monitoring method using Wavelet transform with EMG signal from dynamic measurements. As a result, a decrease of center frequency in 2kg intensity tasks and an increase of center frequency in 1kg intensity tasks were detected. It shows that development and recovery of muscle fatigue were detected in 2kg and 1kg intensity tasks, respectively. Also, the tendency of change value of center frequency from dynamic measurements were corresponded with that from isometric measurements. It suggests that monitoring muscle fatigue in dynamic muscle contraction conditions using wavelet transform was valid to detect the development and recovery of muscle fatigue continuously. The result also shows the possibility of monitoring muscle fatigue in real-time in industry and it could propose a guideline in designing a human-robot interaction system based on monitoring user's muscle fatigue.clos

Human centric collaborative workplace: the human robot interaction system perspective

The implementation of smart technologies and physical collaboration with robots in manufacturing can provide competitive advantages in production, performance and quality, as well as improve working conditions for operators. Due to the rapid advancement of smart technologies and robot capabilities, operators face complex task processes, decline in competences due to robots overtaking tasks, and reduced learning opportunities, as the range of tasks that they are asked to perform is narrower. The Industry 5.0 framework introduced, among others, the human-centric workplace, promoting operators wellbeing and use of smart technologies and robots to support them. This new human centric framework enables operators to learn new skills and improve their competencies. However, the need to understand the effects of the workplace changes remain, especially in the case of human robot collaboration, due to the dynamic nature of human robot interaction. A literature review was performed, initially, to map the effects of workplace changes on operators and their capabilities. Operators need to perform tasks in a complex environment in collaboration with robots, receive information from sensors or other means (e.g. through augmented reality glasses) and decide whether to act upon them. Meanwhile, operators need to maintain their productivity and performance. This affects cognitive load and fatigue, which increases safety risks and probability of human-system error. A model for error probability was formulated and tested in collaborative scenarios, which regards the operators as natural systems in the workplace environment, taking into account their condition based on four macro states; behavioural, mental, physical and psychosocial. A scoping review was then performed to investigate the robot design features effects on operators in the human robot interaction system. Here, the outcomes of robot design features effects on operators were mapped and potential guidelines for design purposes were identified. The results of the scoping review showed that, apart from cognitive load, operators perception on robots reliability and their safety, along with comfort can influence team cohesion and quality in the human robot interaction system. From the findings of the reviews, an experimental study was designed with the support of the industrial partner. The main hypothesis was that cognitive load, due to collaboration, is correlated with quality of product, process and human work. In this experimental study, participants had to perform two tasks; a collaborative assembly and a secondary manual assembly. Perceived task complexity and cognitive load were measured through questionnaires, and quality was measured through errors participants made during the experiment. Evaluation results showed that while collaboration had positive influence in performing the tasks, cognitive load increased and the temporal factor was the main reason behind the issues participants faced, as it slowed task management and decision making of participants. Potential solutions were identified that can be applied to industrial settings, such as involving participants/operators in the task and workplace design phase, sufficient training with their robot co-worker to learn the task procedures and implement direct communication methods between operator and robot for efficient collaboration

Ergonomics and human factors as a requirement to implement safer collaborative robotic workstations: a literature review

There is a worldwide interest in implementing collaborative robots (Cobots) to reduce work-related Musculoskeletal Disorders (WMSD) risk. While prior work in this field has recognized the importance of considering Ergonomics & Human Factors (E&HF) in the design phase, most works tend to highlight workstations’ improvements due to Human-Robot Collaboration (HRC). Based on a literature review, the current study summarises studies where E&HF was considered a requirement rather than an output. In this article, the authors are interested in understanding the existing studies focused on Cobots’ implementation with ergonomic requirements, and the methods applied to design safer collaborative workstations. This review was performed in four prominent publications databases: Scopus, Web of Science, Pubmed, and Google Scholar, searching for the keywords ‘Collaborative robots’ or ‘Cobots’ or ‘HRC’ and ‘Ergonomics’ or ‘Human factors’. Based on the inclusion criterion, 20 articles were reviewed, and the main conclusions of each are provided. Additionally, the focus was given to the segmentation between studies considering E&HF during the design phase of HRC systems and studies applying E&HF in real-time on HRC systems. The results demonstrate the novelty of this topic, especially of the real-time applications of ergonomics as a requirement. Globally, the results of the reviewed studies showed the potential of E&HF requirements integrated into HRC systems as a relevant input for reducing WMSD risk.This work has been supported by FCT–Fundação para a Ciência e Tecnologia and MIT Portugal Program under the doctoral Grant SFRH/BD/151365/2021. This work has been also supported by NORTE-06-3559-FSE-000018, integrated in the invitation NORTE-59-2018-41, aiming the Hiring of Highly Qualified Human Resources, co-financed by the Regional Operational Programme of the North 2020, thematic area of Competitiveness and Employment, through the European Social Fund. Additionally, has been also supported by FCT within the Project “I-CATER–Intelligent robotic Coworker Assistant for industrial Tasks with an Ergonomics Rationale”, Ref. PTDC/EEIROB/3488/2021, and within R&D Units Project Scope: UIDB/00319/2020

EEG Spectral Feature Modulations Associated with Fatigue in Robot-Mediated Upper Limb Gross and Fine Motor Interactions

© 2022 Dissanayake, Steuber and Amirabdollahian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/This paper investigates the EEG spectral feature modulations associated with fatigue induced by robot-mediated upper limb gross and fine motor interactions. Twenty healthy participants were randomly assigned to perform a gross motor interaction with Haptic MASTER or a fine motor interaction with SCRIPT passive orthosis for 20 minutes or until volitional fatigue. Relative and ratio band power measures were estimated from the EEG data recorded before and after the robot-mediated interactions. Paired samples t-tests found a significant increase in the relative alpha band power and a significant decrease in the relative delta band power due to the fatigue induced by the robot-mediated gross and fine motor interactions. The gross motor task also significantly increased the (θ + α)/β and α/β ratio band power measures, whereas the fine motor task increased the relative theta band power. Furthermore, the robot-mediated gross movements mostly changed the EEG activity around the central and parietal brain regions, whereas the fine movements mostly changed the EEG activity around the frontopolar and central brain regions. The subjective ratings suggest that the gross motor task may have induced physical fatigue, whereas the fine motor task may have induced mental fatigue. Therefore, findings affirm that changes to localised brain activity patterns indicate fatigue developed from the robot-mediated interactions. It can also be concluded that the regional differences in the prominent EEG spectral features are most likely due to the differences in the nature of the task (fine/gross motor and distal/proximal upper limb) that may have differently altered an individual’s physical and mental fatigue level. The findings could potentially be used in future to detect fatigue during robot-mediated post-stroke therapies.Peer reviewedFinal Published versio

A framework of integrating knowledge of human factors to facilitate HMI and collaboration in intelligent manufacturing

Recent developments in the field of intelligent manufacturing have led to increased levels of automation and robotic operators becoming commonplace within manufacturing processes. However, the human component of such systems remains prevalent, resulting in significant disturbance and uncertainty. Consequently, semi-automated processes are difficult to optimise. This paper studies the relationships between robotic and human operators to develop the understanding of how the human influence affects these production processes, and proposes a framework to integrate and implement knowledge of such factors, with the aim of improving Human-Machine-Interaction, facilitating bi-directional collaboration, and increasing productivity and quality, supported by an example case-study

Human-Robot Collaboration in Automotive Industry

Human–Robot Collaboration is a new trend in the field of industrial and service. Application of human-robot-collaboration techniques in automotive industries has many advantages on productivity, production quality and workers’ ergonomic; however, workers’ safety aspects play the vital role during this collaboration. Previously, the machine is allowed to be at automatic work only if operators are out of its workspace but today collaborative robots provide the opportunity to establish the human robot cooperation. In this thesis, efforts have been made to present innovative solutions for using human-robot collaboration to develop a manufacturing cell. These solutions are not only used to facilitate the operator working with collaborative robots but also consider the worker safety and ergonomic. After proposing different solutions for improving the safety of operations during the collaboration with industrial robots, the efficiency of the solutions is tested in both laboratory and virtual environments. In this research, firstly, Analytic Hierarchy Process (AHP) has been used as a potential decision maker to prove the efficiency of human-robot collaboration system over the manual one. In the second step, detailed task decomposition has been done using Hierarchical Task Analysis (HTA) to allocate operational tasks to human and robot reducing the chance of duty interference. In the International Organization of Standardization's technical specification 15066 on collaborative robot safety four methodologies have been proposed to reduce the risk of injury in the work area. The four methods implied in ISO/TS 15066 are safety-rated monitored stop (SMS), hand-guided (HG), speed and separation monitoring (SSM) and power force limiting (PFL). SMS method reduces the risk of operator’s injury by stopping the robot motion whenever the operator is in the collaborative workspace. HG method reduces the chance of operator’s injury by providing the possibility of having control over the robot motion at all times in the workstation using emergency system or enabling device. The SSM method determines the minimum protective distance between a robot and an operator in the collaborative workspace, below which the robot will stop any kind of motion and PFL method reduces the momentum of a robot in a way that contact between an operator and the robot will not cause any injury. After determining the requirements and specifications of hybrid assembly cell, few of the above-mentioned methods for evaluating the safety of human-robot-collaboration procedure have been tasted in the laboratory environment. Due to the lack of safety camera (sensors) in the laboratory workstation, the ISO methods such as SSM, that needs sensors in the workstation, have been modeled in virtual environment to evaluate different scenario of human-robot-interaction and feasibility of the assembly process. Implementing different scenarios of ISO methods in hybrid assembly workstation not only improves the operator safety who is in interaction with the collaborative robot but also improves the worker ergonomic during the performing of repetitive heavy tasks