Combining inertial-based ergonomic assessment with biofeedback for posture correction: a narrative review

Work-related musculoskeletal disorders (WRMSDs) are the most reported work-related health problem in the European Union, representing an economic burden equivalent to 2% of its gross domestic product. Awkward postures are one of the main risk factors. Several postural assessment tools try to identify ergonomic exposure factors for evaluating WRMSD risk, yet these are commonly based on observation. Replacing observations with objective measurements can bring more accuracy and reproducibility to this analysis; hence, a direct measurement approach for the assessment is desired. This review looks for two-fold solutions, able to not only monitor workers’ posture using inertial sensors but also to return that information to the user, in a biofeedback loop. It presents systems for posture risk assessment, regarding ergonomic methods, sensors’ and actuators’ characteristics, and validation protocols. In particular, this review advances previous manuscripts by exploring the literature regarding different biofeedback strategies and ways to encode meaningful information in the cues, i.e., able to deliver intuitive ergonomic guidance so that the user becomes aware and changes into a more neutral posture. The combination of inertial sensors and vibrotactile motors stood out, due to its effectiveness in reducing postural risk. Directional feedback to guide users’ segments individually was found to be a promising strategy, although its validation is still limited. The results of the reviewed manuscripts pointed out the relevant practices, potentialities, and limitations of the existing solutions, allowing the identification of future challenges.This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, and the INTEGRATOR project under Grant 2022.15668.MIT. Sara Cerqueira was supported by the doctoral Grant FRH/BD/151382/2021, financed by FCT, under MIT Portugal Program

Smart vest for postural monitoring and ergonomic risk assessment

Dissertação de Mestrado Integrado em Engenharia Biomédica (Ramo Eletrónica Médica)The costs associated with Work-Related Musculoskeletal Disorders (WRMSDs) are estimated at approximately 0.5% to 2% of the Gross Domestic Product. Additionally, severe WRMSDs can result in permanent disability and, consequently, preclude the workers’ return to their work and/or limit their daily lives. The body parts with higher reported injuries are the back, shoulders, and neck. In fact, about 30.7% of the Portuguese working population manifest backache. To reduce the exposure of workers to hazardous environments and tasks, ergonomists and engineers have been putting their efforts into developing Risk Assessment methods for quantification of the risk of WRMSDs. However, at a time where industry 4.0. is emerging along with the Human-Cyber-Physical Systems (H-CPPS), ergonomists still commonly evaluate this exposure based on observational methods, which are highly dependent on the analyst expertise, diminishing repeatability, precision, and objectiveness. Within this framework, this dissertation presents the development of a solution that aims at empowering operators with posture awareness and ergonomists with objective data from the workers’ tasks. To accomplish such solution, a product design inspired methodology was followed, which comprised steps such as literature review, requirements gathering, system idealization, and prototyping. The developed system consists of a smart vest with 6 IMUs located along the spine (vertebrae S1, T12, and T4), both upper arms and the back of the head for acquisition of upper body postural information. Along with the sensory system, an actuation system, composed by 4 vibrotactile motors placed at the lumbar, cervical and both arms, was added aiming for a localized and effortless biofeedback strategy. All acquired data is saved on a USB Pen driver and can be accessed and analyzed using a graphical interface developed in MATLAB®. System testing revealed a maximum autonomy of 12h20 hours, and a RSME of the angle measurement of 3.1%, in relation to the full movement range. Validation experiments with the smart garment were conducted with 5 participants. The results showed a reduction in the ergonomic risk level between trials without biofeedback and with biofeedback. Additionally, it was observed that the developed graphical interface allows a fast overview of the risk level of data and the time analysis of each upper body part of the user in both sagittal and coronal planes. The usability of this interface was tested with 6 engineering students. As future work, validation in an industrial context, hardware, and software improvements, and a rehabilitation approach are pointed out.Os custos associados a Lesões Musculoesqueléticas Relacionadas com o Trabalho (LMERT) são estimadas em cerca de 0.5% a 2% do Produto Nacional Bruto (PIB). LMERTs graves podem resultar em incapacidade permanente e, consequente, impedir que o trabalhador regresse ao seu trabalho e/ou limitar o seu dia-a-dia. As partes do corpo com maior relato de lesões são as costas, ombros e pescoço. Para reduzir a exposição dos trabalhadores a ambientes e tarefas perigosas, métodos para a avaliação do nível de risco ergonómico tem vindo a ser desenvolvidos. No entanto, numa altura em que o emerge o conceito de indústria 4.0 e de Human-Cyber-Physical Systems (H-CPPS), a avaliação do risco ergonómico continua a ser realizada através de métodos observacionais, altamente dependentes da experiência do ergonomista e, consequentemente, possuem baixa repetibilidade, precisão e objetividade. Neste âmbito, a presente dissertação apresenta o desenvolvimento de uma solução que objetiva providenciar consciência da sua postura aos operadores e dados objetivos aos ergonomistas, relativos às tarefas diárias dos trabalhadores. Para atingir este fim, foi seguida uma metodologia inspirada em design de produto que compreendeu etapas tais como revisão de literatura, levantamento de requerimentos, idealização do sistema e prototipagem do mesmo. O sistema desenvolvido consiste num colete instrumentado com 6 unidades de medida inerciais localizadas ao longo da coluna (nas vertebras S1, T12 e T4), ambos os braços superiores e na parte traseira da cabeça, necessários para adquirir a informação postural da parte superior do corpo do utilizador. Para além da tecnologia sensorial, foi adicionado um sistema de atuação que compreende 4 motores vibráteis, colocados na zona da cervical, lombar e braços, objetivando providenciar ao utilizador uma experiência de biofeedback localizado e com o menor esforço cognitivo possível. A informação é guardada numa Pendriver USB, cujos dados podem ser acedidos e analisados através de uma interface desenvolvida em MATLAB® Os testes ao sistema revelaram uma autonomia máxima de 12 horas e 20 minutos e um RSME na medida dos ângulos de 3.1%, em relação à amplitude do movimento medida. Testes de validação foram realizados com 5 sujeitos, onde foram observadas reduções no nível de risco ergonómico entre trials sem e com biofeedback. Ademais, a interface gráfica desenvolvida permite uma análise global e rápida do nível de risco dos dados e uma análise temporal da postura do utilizador em dois planos de movimento. A usabilidade da aplicação foi testada com 6 estudantes de engenharia. Como trabalho futuro aponta-se a validação em contexto industrial, melhorias de hardware e software e endereçar uma abordagem de reabilitação

An admittance-based control strategy for human-robot collaboration task quality management

Today’s dynamic industrial environments are characterized by continuous process changes, which demand diverse expertise among workers. However, this diversity can inadvertently impact production quality and productivity, since inexperienced workers require significant time to learn and adapt to complex processes. Consequently, the need for innovative approaches such as Human-Robot Collaboration (HRC) to manage task quality effectively arises. This paper explores an Admittancebased control strategy approach for HRC task quality management, where two modes were implemented: Free-drive mode and Guided-drive mode. Both modes implement an admittance control with adaptable admittance parameters. Free-drive mode allows easy and effortless hand-drive of a tool attached to a UR10e end-effector. Guided-drive mode is the main mode of this study, which allow a corrective and intuitive assistance aiming to minimize errors occurred in a hand-drive task towards a task quality management. The performance of both controllers is evaluated regarding metrics such as trajectory error, exerted force, time execution, and muscle activation percentage

A DMPs-based approach for human-robot collaboration task quality management

Industry 5.0 places humans alongside robots on a symbiotic collaboration to improve the efficiency and productivity of industrial processes. The current manufacturing industry targets personalized products, that demand flexible, agile, and quickly changeable workstations that require less skilled workers capable to handle the different production needs. Human-robot collaboration and Learning from demonstration are emerging fields in robotics that can be exploited for this end. This paper explores a learning from demonstration (LfD) approach to learn how to perform a collaborative task with an experienced collaborator and actively teach and/or assist a novice worker. A reference trajetory was recorded using the UR10e robot and modelled by non-linear dynamical system, specifically, dynamic movement primitives (DMPs), whose weights are learned using Covariance matrix adaptation evolution strategy (CMA-ES). This paper also explores DMP effectiveness to generate the learned trajectory, with the ultimate goal of managing the quality of a collaborative task. The obtained results explore DMPs robustness against sudden perturbations and deviations from the encoded trajectory, both in simulation and in real context. Furthermore, the flexibility and stability of DMPs in learning the references’ trajectories, as well as their temporal and scale invariance, were verified.This work was supported in part by the Fundac¸ao para a Ciência e Tecnologia (FCT) under the national support to RD units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868. Sara Cerqueira was supported by the doctoral Grant SFRH/BD/151382/2021, financed by the Portuguese Foundation for Science and Technology (FCT), under MIT Portugal Progra

Towards a user-specific ergonomics-based approach for an activity assessment tool

Work-related musculoskeletal disorders (WRMSDs) are the most reported work-related health problem in European Union. These are multifactorial disorders, influenced not only by sustained or repeated awkward postures but also by each worker’s individual and psychosocial context. Thus, it becomes crucial to quantify and automatize risk assessment, in an attempt to prevent and reduce WRMSD. This work presents the design of a solution for a user-specific assessment based on ergonomics for posture correction through an intuitive haptic feedback strategy to increase posture self-awareness and guide the user into a more neutral posture. The user’s angular configurations are continuously evaluated with a risk score, based on an ergonomic method, and then associated with the postures where those risks occurred. Posture is intended to be predicted by a deep learning model. Moreover, a joint kinematic wear index is used to carry out a cumulative assessment, taking into account the past postures’ scores. Inertial data from three individuals was collected and analyzed to perform movement analysis and define the ground truth of the recognition model. The resulting kinematic parameters’ ranges are presented. An offline risk assessment was also conducted, showing the potential of the cumulative approach for a more complete and meaningful evaluation.This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868. Sara Cerqueira was supported by the doctoral Grant SFRH/BD/151382/2021, financed by the Portuguese Foundation for Science and Technology (FCT), under MIT Portugal Program

Ergowear: an ambulatory, non-intrusive, and interoperable system towards a human-aware human-robot collaborative framework

Motivated by industry 5.0 paradigm and Human-robot collaboration (HRC) technology, this paper presents the first steps of the design and development of Ergowear, a wearable, non-intrusive, interoperable upper body inertial Motion Capture system. This system was developed aiming to be used as a sensing technology to make the robot aware of the human's intentions and states and, therefore, achieving human-Awareness. Consequently, it was developed to run on ROS2, to ease its integration in a HRC framework. This work depicts the design process and development of the prototype. Firstly, the system requirements are presented along with the system components and architecture. To verify interoperability, a protocol was designed to test the Ergowear's hardware, namely its autonomy, storing capacity, wireless communication's performance, and overall mechanical robustness. Overall, the achieved results are within the specified technical requirements, presenting the Ergowear as a promising sensing technology to be integrated within a Human-Aware HRC framework.This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Fundação para a Ciência e Tecnologia with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941

A DMPs-based approach for human-like robotic movements

Industry 5.0 requires flexible and agile robots, capable to be adapted to different tasks. Tasks that demand from human workers complex movements, with large amplitudes and considerable loads, and whose layout alteration to allow good ergonomics would imply a very significant economic expenditure. In these cases, where the ergonomic safety of the workers is not guaranteed, the introduction of a robot in a production line is preferable. Human-robot collaboration pose as a solution for this problematic. However, human-likeness motion reproduction is still missing from robots. This paper explores a Learning from Demonstration strategy, a subfield of Human-Robot Collaboration (HRC) focused on teaching robots how to master a skill based on human demonstrations. Specifically, 12 human movements were recorded using MTw Awinda Motion Capture system to be further modelled by non-linear dynamical system, specifically, Dynamic Movement primitives (DMP), whose weights are learned using Covariance matrix adaptation evolution strategy (CMAES). This was used to learn how to perform human movements and transfer these skills to a collaborative Robot UR10e.This work was supported in part by the Fundação para a Ciência e Tecnologia (FCT) under the national support to RD units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020 and by Project FAIR under grant 2022.05844.PTDC. Sara Cerqueira was supported by the doctoral Grant SFRH/BD/151382/2021, financed by FCT, under MIT Portugal Progra

Towards a smart-vest for forward posture monitoring: improving usability with e-textiles

The emergence of new communication technologies, such as computers and smartphones, has decisively contributed to the adoption of ergonomically incorrect postural behaviors. Forward Head Posture (FHP) stands out, which is sometimes associated with the onset of more serious problems, such as neck pain. This paper presents a brief overview of the most recurrent monitoring methods for FHP assessment, as well as the issues associated with the current electronics used in wearable devices and the development of e-textiles. Additionally, techniques for integrating electronics into textiles are addressed, focusing on the use of conductive yarns and screen printing. A wearable device prototype for FHP monitoring is proposed.This work was supported in part by the Fundação para a Ciência e a Tecnologia (FCT) under the national support to RD units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020, and by the FEDER Funds through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868. Sara Cerqueira was supported by the doctoral Grant SFRH/BD/151382/2021, financed by the FCT, under MIT Portugal Progra

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data