Frontiers in occupational health and safety management

This Special Issue of the International Journal of Environmental Research and Public Health is devoted to the “Frontiers in Occupational Health and Safety Management” [...

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

Safety requirements for the design of collaborative robotic workstations in europe – a review

Industrial manufacturing is moving towards flexible and intelligent processes. Human-Robot Collaboration (HRC) has a pivotal role in smart factories due to a more versatile resource allocation that ultimately drives higher productivity and efficiency. The physical barriers that separate robots’ and humans’ workspaces are removed to facilitate HRC, which raises new safety concerns. To cope with this new robotics paradigm, regulatory legislation and international safety standards have been issued and are enforced for any machinery placed in factories. In this paper, we aim to shorten the gap between research projects and industry-ready robotic systems, by providing the guidelines and general requirements for collaborative robotic applications. We review the current international safety standards, certification procedures under the scope of European jurisdiction, and elaborate a literature review of papers related to safety for collaborative workstations.This work was supported by NORTE-06-3559-FSE-000018, integrated into the invitation NORTE-59-2018-41, aiming to hire 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 (ESF)

iBoccia: a framework to monitor the Boccia gameplay in elderly

The increase of the elderly population has an enormous effect on the health care system of a country, as the rise of this population sets the mood to an exponential growth in assistance and care. Indeed, the inherent costs of this populational class are higher when comparing to the younger classes. Today paradigm focuses on the reduction of these costs by promoting a healthier lifestyle on all classes of the populations. Thus, the concern of a more active lifestyle is present in the elderly population, which has proven to reduce, for example, the risk of coronary problems. The stimulus on physical activity is now higher and it is possible to get several monitoring devices to keep track on the activity that was performed. Following this trend, the present paper presents a hybrid approach that employs the use of wearable devices, the Mio Fuse band and the pandlet, and a non-wearable device, the Kinect camera, to monitor elderly people during a Boccia game scenario. Preliminary tests were performed in laboratory. The results include data collected concerning a main movement that is used during a Boccia gameplay.This article is a result of the project Deus ex machina: NORTE-01-0145- FEDER-000026, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

A wearable and non-wearable approach for gesture recognition: initial results

A natural way of communication between humans are gestures. Through this type of non-verbal communication, the human interaction may change since it is possible to send a particular message or capture the attention of the other peer. In the human-computer interaction the capture of such gestures has been a topic of interest where the goal is to classify human gestures in different scenarios. Applying machine learning techniques, one may be able to track and recognize human gestures and use the gathered information to assess the medical condition of a person regarding, for example, motor impairments. According to the type of movement and to the target population one may use different wearable or non-wearable sensors. In this work, we are using a hybrid approach for automatically detecting the ball throwing movement by applying a Microsoft Kinect (non-wearable) and the Pandlet (set of wearable sensors such as accelerometer, gyroscope, among others). After creating a dataset of 10 participants, a SVM model with a DTW kernel is trained and used as a classification tool. The system performance was quantified in terms of confusion matrix, accuracy, sensitivity and specificity, Area Under the Curve, and Mathews Correlation Coefficient metrics. The obtained results point out that the present system is able to recognize the selected throwing gestures and that the overall performance of the Kinect is better compared to the Pandlet.This article is a result of the project Deus Ex Machina: NORTE-01-0145-FEDER-000026, supported by Norte Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Fuzzy Nonbalanced Hedonic Scale (F-NBHS): a new method for treatments of food preference data collected with hedonic scales of points

Hedonic point scales are widely used in food preference studies. However, in this type of scale, the symmetrical distribution of categories and inaccuracy of the responses may interfere with the results of the research. This paper proposes the fuzzy nonbalanced hedonic scale (F-NBHS) as a new method for treatments of food preference data collected with hedonic scales of 9 points and can be generalized to scales with a different number of points. Data analysis from F-NBHS aims to improve the limitations presented by a traditional treatment, especially regarding the distribution of numerical values between the categories and the inaccuracy of the responses. The validation of the proposed scale was carried out through a food preference research done within a Portuguese university. A set of 64 foods, divided into 8 food groups, was evaluated by 119 students in two experiments. The frequency and variability of the data were studied according to the categories in different areas of the scale. Findings showed that the structure of the proposed scale is observed in the behavior of experimental data and intermediate areas, which indicated the intensity of perception and variability of different responses from other areas of the scale. The data used with F-NBHS were more satisfactory in relation to standard deviations and consensus index measurements compared with a traditional treatment. Thus, it is concluded that the F-NBHS scale is a more efficient and robust method for the treatment of dietary preference information compared to a traditional treatment.(undefined

iBoccia: monitoring elderly while playing Boccia gameplay

The size of the aging population has been increasing over the last years, leading to a search for solutions that can increase the quality of life of the elderlies. One of the main means of action is focused on their physical activity. A non-sedentary life can help in disease prevention and disability reduction, leading to an independent living with quality. Moreover, the practice of physical exercise can decrease fall risks and its consequences. Furthermore, it is desirable that the solutions can be accessed by anyone, with a low inherent cost. The Boccia game is a good way to promote physical activity to the elderly, due to its simplicity and easy adaptability to the physical limitations of the elderly. Following this trend, this paper presents iBoccia, a novel framework to monitor elderly while playing Boccia game, through wearable sensors, Mio Fuse band and pandlet (inertial sensor), and a non-wearable device, Kinect camera. Several performance metrics are expected to be measured during the gameplay. Using the pandlet we calculate wrist rotation angles and force applied during ball throw, using the Kinect we recognize facial expressions and from the Mio Fuse band we retrieve heart rate.We would like to acknowledge the financial support obtained from North Portugal Regional Operational Programme (NORTE 2020), Portugal 2020 and the European Regional Development Fund (ERDF) from European Union through the project Symbiotic technology for societal efficiency gains: Deus ex Machina (DEM), NORTE-01-0145-FEDER-000026.info:eu-repo/semantics/publishedVersio

Decision-making framework for implementing safer human-robot collaboration workstations: system dynamics modeling

Human-Robot Collaboration (HRC) systems are often implemented seeking for reducing risk of Work-related Musculoskeletal Disorders (WMSD) development and increasing productivity. The challenge is to successfully implement an industrial HRC to manage those factors, considering that non-linear behaviors of complex systems can produce counterintuitive effects. Therefore, the aim of this study was to design a decision-making framework considering the key ergonomic methods and using a computational model for simulations. It considered the main systemic influences when implementing a collaborative robot (cobot) into a production system and simulated scenarios of productivity and WMSD risk. In order to verify whether the computational model for simulating scenarios would be useful in the framework, a case study in a manual assembly workstation was conducted. The results show that both cycle time and WMSD risk depend on the Level of Collaboration (LoC). The proposed framework helps deciding which cobot to implement in a context of industrial assembly process. System dynamics were used to understand the actual behavior of all factors and to predict scenarios. Finally, the framework presented a clear roadmap for the future development of an industrial HRC system, drastically reducing risk management in decision-making.This work was supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n◦ 39479; Funding Reference: POCI-01-0247-FEDER-39479] and by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

Digitalization of musculoskeletal risk assessment in a robotic-assisted assembly workstation

The ergonomic assessment of adopted working postures is essential for avoiding musculoskeletal risk factors in manufacturing contexts. Several observational methods based on external analyst observations are available; however, they are relatively subjective and suffer low repeatability. Over the past decade, the digitalization of this assessment has received high research interest. Robotic applications have the potential to lighten workers’ workload and improve working conditions. Therefore, this work presents a musculoskeletal risk assessment before and after robotic implementation in an assembly workstation. We also emphasize the importance of using novel and non-intrusive technologies for musculoskeletal risk assessment. A kinematic study was conducted using inertial motion units (IMU) in a convenience sample of two workers during their normal performance of assembly work cycles. The musculoskeletal risk was estimated according to a semi-automated solution, called the Rapid Upper Limb Assessment (RULA) report. Based on previous musculoskeletal problems reported by the company, the assessment centered on the kinematic analysis of functional wrist movements (flexion/extension, ulnar/radial deviation, and pronation/supination). The results of the RULA report showed a reduction in musculoskeletal risk using robotic-assisted assembly. Regarding the kinematic analysis of the wrist during robotic-assisted tasks, a significant posture improvement of 20–45% was registered (considering the angular deviations relative to the neutral wrist position). The results obtained by direct measurements simultaneously reflect the workload and individual characteristics. The current study highlights the importance of an in-field instrumented assessment of musculoskeletal risk and the limitations of the system applied (e.g., unsuitable for tracking the motion of small joints, such as the fingers).This work was 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 (ESF). This work was also supported by FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020