Ebook: Transforming Ergonomics with Personalized Health and Intelligent Workplaces

Life expectancy is increasing, and as a result society includes an ever larger proportion of older people. Among other things, this is necessitating an increase in the retirement age in many countries. The fact that more than 95% of the world's population suffers from one or more health conditions or disorders [1] makes keeping people healthy and able to work for longer a difficult challenge. This is a challenge that has existed for more than two decades, and its consequences, such as increasing costs, a shortage of healthcare personnel, and more complex combinations of chronic diseases, have become particularly apparent in recent years. In addition, hazards at work and unhealthy work practices are often the underlying cause of musculoskeletal disorders (MSD) and depression due to burnout. All of these factors make it difficult to sustain the current social system.

To facilitate a longer working life for the general population, a balance must be found between the demands of work and human capabilities. This is in line with suggested approaches for chronic disease management to reduce the healthcare burden. The most common approach to minimising risks, reducing exposure and avoiding a harmful working lifestyle is prevention by design, i.e. designing the work environment for the healthy and safe execution of the tasks to be performed. Ergonomists already assess MSD risk factors and suggest changes to workplaces, however, existing methods are mainly based on visual observation, which is relatively unreliable and can only cover part of the working day. Furthermore, suggestions generally concern the workplace and the organization of work overall, but rarely include the working techniques of individuals. In this context, the use of pervasive technology, ubiquitous computing and p-health monitoring provide a key toolset to transform many common working scenarios into healthy, intelligent workplaces.

Emerging technologies, such as smart textiles and micro-electronics integrated into wearable devices, have enabled the development of intelligent biomedical clothing, and the recent proliferation of Internet of Things (IoT) systems have facilitated the integration of pervasive sensitive services into the environment. These, together with ambient intelligence (AmI) technology techniques and big data analytics, have fostered a proliferation of p-Health monitoring solutions. This, together with advances in the development of inertial measurement units, activity and heart-rate-sensing watches and garments and their wide presence in the consumer electronics market, have opened a new arena for monitoring the physical workload and posture of different limbs. These wearable and IoT technologies, combined with ergonomic assessments, facilitate the gathering of epidemiological data for further big data analysis, and even provide the opportunity for prompt feedback and for coaching through deployment of the appropriate personalized m-healthcare tools.

Transformation of a work environment into a careful – even healthy – intelligent workplace as a deployment platform for p-Health services may support not just ergonomists, employees and employers, but also society in general; enabling the workplace as an intelligent environment might be the solution to ensuring the sustainability of current social welfare systems.

However, the inclusion of these emerging technologies and analysis techniques create other challenges that up to now have not been part of the field or context of ergonomics and the design of workspaces. As regards pervasive sensitive services, the interoperability of data and its security become essential to guarantee adoption and final acceptance, and it is therefore necessary to ensure that the systems developed conform to existing data protection laws and standards, i.e. General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) or Personal Information Protection and Electronic Documents Act (PIPEDA).

This book presents a collection of the most significant challenges and advances in the field of intelligent workspaces and personalized ergonomics, bringing together the most relevant results obtained after the completion of various international research projects. The book is organized into three main sections, each corresponding to a point of view covered by the projects carried out. The first section, Personalized Ergonomics, offers a vision about the need for practical and reliable risk assessment methods for the prevention of MSD and the enhancement of the workplace through the use of comprehensive stepped-care models for mental health. The section Pervasive Technology for Intelligent Workplaces identifies the opportunities and challenges of technology-based interventions to increase health-awareness and the security and privacy issues which must be covered in the smart workplace. The third section presents Data Warehouse Governance and Analytics related works. The book concludes with a chapter on lessons learnt.

Reference

[1] Vos, T., Barber, R. M., Bell, B., Bertozzi-Villa, A., Biryukov, S., Bolliger, I., … & Duan, L. (2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 386(9995), 743–800.

Although work-life is changing, and production is modernized, work-related musculoskeletal disorders (WMSDs) are still frequent, inducing very large costs for companies and societies all over the world. Ergonomists and other work health consultants work to make organizations sustainable. In their work to prevent WMSDs it is important to identify risks in a reliable way, to prioritize risks, and then to perform interventions (participatory interventions have shown to more often be successful), so that the risks and the disorders may be reduced. Most interventions concerns the environment and work methods, but also individual work technique, e.g. lifting technique and habitual work postures may be in focus. Today, risks are most often assessed by observation. However, observational methods generally have low reliability, i.e. low agreement between different observers for the same job task. There is also a low inter-method reliability, i.e. when the same work is assessed with different methods different risk levels are often obtained. There are now validated technical methods that may be used by practitioners. But, user-interfaces needs to be improved, and today's inexpensive electronic devices should be utilized to a higher degree, in the development of tools, in collaboration with practitioners. New methods should be attractive, easy and time efficient to use. The results of these methods will be objective and should increase the reliability in risk assessments of work tasks and jobs.

There is an urgent need for mental healthcare in the workplace though little has been done so far to address that. Many tragic incidents such as workplace suicide and death due to ‘workplace bullying’, ‘violence’ and ‘overwork’ have happened in South Korea and Japan. The unemployment rate of workers suffering from mental health problems is also increasing, which aggravates the risk of serious economic impacts for an aging population. Therefore, it is crucial to establish a mental health management system at the workplace level. This chapter presents a model based on the use of smart technologies for mental healthcare services to enhance the ergonomics and quality of working conditions at the workplace, and to help create a mentally healthy workplace. With the rapid development of SmartMentalTech, an innovative shift with new ways of delivering mental healthcare services is taking place changing the way service users, employers, and system designers use technology to deliver mental healthcare at the workplace. The objective of this chapter is to suggest an ergonomic platform where viable applications related to psychological challenges at the workplace are provided by service users, developers, practitioners, decision makers, and policymakers. The approach of Occupational Health Psychology (OHP) emphasizes on the significance of workplace design, management and psychosocial risk mitigation. There are differences across service users; therefore, the challenges facing the managers of mental healthcare services at the workplace are daunting. Currently 12 types of developed SmartMentalTech can be used for personalized care. This chapter provides a comprehensive account on how to set up and to manage mental healthcare delivery in order to minimize workplace mental health problems and the social stigma of mental health issues by establishing a new comprehensive mental healthcare stepped-care model (CMHSCM) system for service users' satisfaction and healthy intelligent workplace in a timely and economically responsive manner.

Well-being at work is gaining an increasing importance on the overall health promotion as the workplace is considered an adequate setting to support health-related interventions reaching large audiences. In fact, an increasing number of initiatives are being carried out to influence employees towards healthier lifestyles in later years. However, despite demonstrating moderate efficacy, the body of literature shows that the lack of adherence of the target audience to the interventions is an important factor to overcome in order to attain higher success. To increase employees' motivation and prevent early drop-out, disengagement or high attrition rates, this work presents an intervention methodology based on the Internet of Things (IoT) paradigm. Specifically, it presents a novel concept of a participatory worker-centric IoT solution for enhancing individuals' well-being in office environments. This approach seeks to stress the significance of empowering workers providing to them fine-grained control of their own well-being and self-care which correlates to higher rates of participation in health promotion initiatives. Along this chapter the main challenges associated with the design and development of technology-based interventions are reviewed. Moreover, the value of increasing the acceptance and adoption of the presented IoT approach from the employee's perspective is analyzed in a comprehensive manner.

The workplaces organization has evolved during the last decades from individual private offices to open spaces, which offer a higher flexibility degree when there are important changes in firms' necessities. In these open spaces, the optimization of environmental conditions are an essential factor to achieve employees' adequate comfort levels to develop their tasks. The rise of Internet of Things (IoT) technologies has cheapened and extended the processes automation in tasks such as air conditioning, lighting control, etc. New challenges have arisen from this new scenario, where factors such as privacy and access control for personal information are crucial. In this work, we show the application of an access control system which is able to operate with different communications protocols to a use case based on a smart office. We have developed a prototype of the devices which would control the sensors and actuators in the system, and we have carried out a series of experiments to measure the delay added to communication when using the access control techniques. The results demonstrate the validity and feasibility of the system.

Sensor technologies and their integration with advanced software applications are fostering a new era of safety and healthiness. Factories of the future can benefit from the integration of ambient and wearable sensors in the workplace, as the worker him/herself can provide a reliable real-time profile of the conditions and adaptation to the work environment and demands. The information collected by the network of sensors can be aggregated and displayed in a dashboard in a simple but effective way, enabling health professionals to revise and follow-up massive streams of data. These huge amounts of data can be turned into meaningful information with the support of intelligent algorithms capable of processing data and detecting abnormalities. Our vision of the absolute healthy and safe factory of the future introduces the Medical Response Center (MRC), a unit capable of monitoring the factory from a computer. One of the main weaknesses of current factories is the lack of completeness in the information related to worker health, which subsequently is one of the major challenges industrial environments. Isolated health data are not enough to obtain an accident-free and safety factory. Time to time monitoring should be turned into a continuous health vigilance. Our view of the factory is as source of large amounts of data (for example, employee monitoring; environmental monitoring; medical decision support system; management protocols systems; treatment and adherence to therapy systems). This chapter aims at proposing an architecture which allows the exchange of information and connection between several intelligent services and devices, where all generated data are stored, selected, treated, etc. The first part of the chapter is devoted to explain in general terms the course followed by the information in order to achieve continuous worker vigilance. Secondly, and as a main block, the decision support system architecture is detailed. This architecture consists of a choreographer core that receives information of several services and devices. This proposal is thereafter linked to an ergonomics case, that is, with ergonomics from the point of view of a physical response to a specific need of the worker. Our case elaborates on detecting that a worker has visited the medical office for a defined number of times in the last months as an indicative of continuous discomfort. The medical study in detail can lead to a postural change or a reorganization of your workplace (for example due to shoulder problems); which is an improvement in the work place and ergonomics. This case is illustrative as it is not a direct consequence of a value collected through health wearable sensors, but data collected in the MRC, in which the review of health data is essential to rule out other diseases and generate an adequate diagnosis.

Work-related disorders account for a significant part of total healthcare expenditure. Traditionally muscle-skeletal disorders were predominant as source of work absenteeism but in the last years work activity-related disorders have increased remarkably. Too little activity at work, sedentarism, or too much work activity leads to stress. The individualized behavioural analysis of employees can support ergonomy experts in optimizing work environments. However, in order to understand which aspects of the working environment need to be improved a clear understanding on the behaviours and working conditions of employees is needed. This requires analyzing and summarizing the multi-dimensional set of variables that describes a work environment. Process Mining Technologies can offer a human understandable view of what is actually occurring in workplaces in an individualized way. In this paper, we present a proof of concept of how Process Mining technologies can be used for discovering employees work flows in order to support the ergonomy experts in the selection of more accurate interventions for improving occupational health.

Nowadays, the normalization culture is the usual strategy in enterprises. The formalization has demonstrated its utility for creating efficient, traceable and optimized processes in all the stages of manufacturing. This in being applied to medicine protocols in order to ensure a better quality of service to patients. This protocols can be used to deploy prevention plans on factories improving the current policies by implementing a individualized and holistic approach. Nevertheless, the deployment of these protocols in factory conditions is very complex due to the lack of easy to configure, simple, understandable and efficient systems that could be integrated on the process factory.

In this paper, a workflow based solution that enable occupational health professionals is presented. This system enables occupational health professionals to create individualized prevention protocols that allows an easy control of specific workers integrated on the available infrastructure in the factory.

This section contains lessons learnt mainly from several different projects funded by different private and public financiers. The projects were funded from Swedish national and European financiers. The project consortia had significantly different composition with both public and private partners being national, international or cross-national. All these projects have in common that aimed at integrating Wearable Sensing technologies with Information and Communication Technologies to improve the working environment conditions to avoid the exposure to high risk posture and movements leading to musculoskeletal disorders and increasing the risk of injuries. The origin of these lessons learnt is very broad lessons and have been grouped in the following categories: Information Technology Infrastructure, Data Security and Policy issues; Regulatory and Ethics; Employees concerns, human interactions and dual role users and targets; Body Sensing Networks; and Team Management & Communication.