A multi-parametric wearable system to monitor neck movements and respiratory frequency of computer workers

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively

The New Normal in the Post-pandemic Workplace? A Meta-Analysis on the Use Cases and Implementation Challenges of Internet-of-Things Technology in Office Settings

While governmental tracing apps received special attention by research and the media during the Covid-19 pandemic, the surge in new work surveillance technologies went almost unnoticed. New organizational infrastructures based on Internet-of-things (IoT) technology have emerged at both, public and private sector organizations, promising a safe return to the workplace but equally threatening the privacy of employees. The goal of this paper is to take a closer look at a technology with ambivalent use by conducting a meta-synthesis of extant IoT studies. We classify the literature into four use cases with their implementation options: physical health monitoring, mental health monitoring, environmental health monitoring, and connected workplace. We also discuss main challenges emerging from privacy concerns along the IoT data lifecycle for occupational health initiatives in the office context. Based on that, we propose normative guidelines to assist employers interested in implementing privacy preserving IoT solu-tions for health and safety at work

Functional mimicry of Ruffini receptors with fibre Bragg gratings and deep neural networks enables a bio-inspired large-area tactile-sensitive skin

Collaborative robots are expected to physically interact with humans in daily living and the workplace, including industrial and healthcare settings. A key related enabling technology is tactile sensing, which currently requires addressing the outstanding scientific challenge to simultaneously detect contact location and intensity by means of soft conformable artificial skins adapting over large areas to the complex curved geometries of robot embodiments. In this work, the development of a large-area sensitive soft skin with a curved geometry is presented, allowing for robot total-body coverage through modular patches. The biomimetic skin consists of a soft polymeric matrix, resembling a human forearm, embedded with photonic fibre Bragg grating transducers, which partially mimics Ruffini mechanoreceptor functionality with diffuse, overlapping receptive fields. A convolutional neural network deep learning algorithm and a multigrid neuron integration process were implemented to decode the fibre Bragg grating sensor outputs for inference of contact force magnitude and localization through the skin surface. Results of 35 mN (interquartile range 56 mN) and 3.2 mm (interquartile range 2.3 mm) median errors were achieved for force and localization predictions, respectively. Demonstrations with an anthropomorphic arm pave the way towards artificial intelligence based integrated skins enabling safe human–robot cooperation via machine intelligence

A Multi-Parametric Wearable System to Monitor Neck Movements and Respiratory Frequency of Computer Workers

Musculoskeletal disorders are the most common form of occupational ill-health. Neck pain is one of the most prevalent musculoskeletal disorders experienced by computer workers. Wrong postural habits and non-compliance of the workstation to ergonomics guidelines are the leading causes of neck pain. These factors may also alter respiratory functions. Health and safety interventions can reduce neck pain and, more generally, the symptoms of musculoskeletal disorders and reduce the consequent economic burden. In this work, a multi-parametric wearable system based on two fiber Bragg grating sensors is proposed for monitoring neck movements and breathing activity of computer workers. The sensing elements were positioned on the neck, in the frontal and sagittal planes, to monitor: (i) flexion-extension and axial rotation repetitions, and (ii) respiratory frequency. In this pilot study, five volunteers were enrolled and performed five repetitions of both flexion-extension and axial rotation, and ten breaths of both quite breathing and tachypnea. Results showed the good performances of the proposed system in monitoring the aforementioned parameters when compared to optical reference systems. The wearable system is able to well-match the trend in time of the neck movements (both flexion-extension and axial rotation) and to estimate mean and breath-by-breath respiratory frequency values with percentage errors ≤6.09% and ≤1.90%, during quiet breathing and tachypnea, respectively

Monitorizar e Analisar o Stress em Organizações Públicas no Setor da Saúde

Enquadramento: O aumento do volume de trabalho em condições adversas e com problemas de saúde associados, tem levado a um incremento do distress nas organizações, contribuindo para o presenteísmo e absentismo. Estas condições afetam negativamente a produtividade da organização. Considerando esta problemática colocou-se a questão sobre, de que forma a monitorização do stress, associada à promoção do awareness, contribui para o aumento da eficiência da organização. Metodologia: Estudo aplicado, descritivo e de abordagem metodológica mista, com um estudo de síntese de evidência através de revisão sistemática de literatura, abordagem qualitativa com recurso ao estudo de caso numa organização pública do setor da saúde e entrevistas semiestruturadas, e abordagem quantitativa através aplicação de questionários. Dados qualitativos analisados com recurso a análise de conteúdo e realização de análise descritiva dos dados quantitativos recolhidos. Resultados: Desenvolveu-se o Modelo de Monitorização e Diagnóstico Preventivo, assente na Teoria Geral dos Sistemas, Teoria Gestalt e Teoria Cibernética, complementado com uma abordagem BizDevOps e tendo por base o paradigma da Saúde 4.0. Foram identificados um conjunto de indicadores para uma monitorização multidimensional do contexto laboral, conteúdo laboral e características de saúde do colaborador. Foram obtidas 353 respostas a questionários e realizadas 4 entrevistas semiestruturadas, com validação da pertinência da monitorização do stress e das dimensões do modelo. Conclusões: O modelo permitiu aferir que a monitorização na organização, a promoção do bem-estar e o aumento do awareness são promotores de uma maior produtividade e satisfação dos colaboradores. Sugere-se a operacionalização do modelo para validar a replicabilidade do mesmo.Background: The increased workload under adverse conditions and associated health problems has increased organisational distress, contributing to presenteeism and absenteeism. These conditions negatively affect the organization's productivity. Considering this issue, the question arose about how stress monitoring, associated with promoting awareness, increases the organization's efficiency. Methodology: An applied and descriptive study, with a mixed methodological approach, with a study of synthesis of evidence through a systematic literature review, a qualitative approach using a case study in a public organization in the health sector and semi-structured interviews, and a quantitative approach through the application of questionnaires. Qualitative data was analysed using content and descriptive analysis of the collected quantitative data. Results: The Monitoring and Preventive Diagnosis Model was developed based on the General Systems Theory, Gestalt Theory and Cybernetic Theory, complemented with a BizDevOps approach and based on the Health 4.0 paradigm. Indicators were identified for multidimensional monitoring of the work context, work content and employee health characteristics. Three hundred fifty-three answers to questionnaires were obtained, and four semi-structured interviews were carried out, with validation of the pertinence of monitoring stress and the dimensions of the MPDM. Conclusions: The model made it possible to verify that monitoring the organization, promoting well-being and increasing awareness are promoters of greater productivity and employee satisfaction. It is suggested to operationalize the model to validate its replicability