Decision Support System for Health Continuous Vigilance in Industrial Environments

María Martínez-Piqueras#1, Carlos Fernández-Llatas#2, Carlos Cebrián*3, Teresa Meneu#4 #ITACA - Health and Wellbeing Technologies Universidad Politécnica de Valencia, Spain

1mamarpi2@itaca.upv.es 2carfell@itaca.upv.es 4tmeneu@itaca.upv.es

*TISSAT, S.A. Parque Tecnológico de Valencia, Spain

3ccebrian@tissat.es

Abstract— Several European statistics confirm that a large number of people have fatal accidents every year in the workplace. For this reason, one of the most important European objectives is to reduce the number of industrial accidents significantly. Fasys Project, focused on factories of machining and assembly operations, aims to achieve this improvement promoting the use of technologies and giving, at the same time, a principal role to the worker. From now on, the worker, who has represented a neglected element in the factories, will be the center of attention. The increase of his security, and the enhancement of his working conditions and health, will be key elements for the Factory of the Future performance. In this paper, a health continuous vigilance system is proposed. The system includes both the monitoring to characterize workers activity and environment, and aspects related to prevention protocols. To manage it, several systems of collecting data are needed. They can be distributed around the factory and monitor, for example, personal and environment data, or get information, for example, from medical knowledge or previous medical information of the worker. Besides, due to the big amount of generated information, intelligent systems for massive data processing are needed. In this way, the information could be easily managed and classified, in order to obtain data from a specific situation that could be required.

I. INTRODUCTION

The International Labour Organization (ILO) estimates that 160 million workers are victims of occupational accidents and diseases every year [1]. The base of several associations is that workers should be protected from sickness, disease and injury arising from their employment. But currently two million of people lose their lives every year from work-related accidents and diseases. The suffering caused by such accidents and illnesses to workers and their families is innumerable. The standards on occupational safety and health provide necessary tools for governments, employers, and workers to establish such practices and to provide for full safety at work. In 2003, ILO assumed a global strategy to improve occupational safety and health, which included the introduction of a preventive safety and health culture, the

promotion and development of relevant instruments, and technical assistance [1]. One of the European objectives set for 2020 is the 25%reduction in the number of industrial accidents [2-3]. In order to reduce accidents it is essential to pay attention to the workers, their single workplaces and to their working conditions. In this way, if workers had a safer environment, the number of accidents could be significantly reduced, implying therefore a reduction in costs. This economic saving is very important to the general economy of the company. In addition, these favourable environments make workers feel more comfortable while they are in the factories, and thus the efficiency is increased. As a consequence, it is possible to obtain the maximum efficiency in the factory as a whole, which also produces economic benefit for the company. From a healthcare point of view, factories lack normally in an amount of enough information to allow a holistic care of the worker. Health data stored by companies are only a small amount of data, usually stored once a year, and referred to the physical condition of a person just in a particular moment [4]. For this reason, the future work has to be oriented on new technological applications to get a factory safer and to reduce significantly the number of accidents. To control the accidents it is necessary to anticipate and estimate what can happen. So, prevention will be a key point. To manage it, it is important to collect and measure data during a period of time, in order to evaluate their progress. Consequently, the perfect model would be a factory in which the risks and health were controlled at any time. So, this paper describes the objective to turn the punctual monitoring into a more frequent and personalized vigilance. To achieve this goal, it is necessary a continuous and individual monitoring, respectively. Collecting data of many people during a long period of time requires collecting a big amount of information. People are not able to process so much information, so intelligent systems for massive data processing are needed. Examples of this kind of systems are: CEP [5], Process Mining [6], ECA [7]. These intelligent

systems classify data and generate alarms associated to the worker. Thanks to these alerts and all the other environmental and personal data stored, it is possible to predict health threats. Thus, it is possible to act in the most appropriate way for each worker in particular. These preventive actions, adapted for each worker, are represented by workflows [8]. In order to configure templates of these prevention actions, it is required to develop a visual and intuitive interface that allows experts to directly do it. In addition, the created protocol must be automatically executable in computer systems. To manage it, a specific system has to turn the design of the plan into an executable format. Nowadays, the concept of absolutely safe and healthy environments [9] is increasingly used. In order to get the main objective, that is, the reduction of industrial accidents, Fasys Project [10] aims to an absolutely safe and healthy factory, developing knowledge and technology to guarantee both the safety and permanent wellbeing of the worker in the factories of machining, handling and assembly operations of the future. Through this, the workers will become the key factors of competitiveness and differentiation of the new productive model. To solve the lack of the continuous and personal vigilance and the personalization of the preventive actions, Fasys proposes a general scheme of a decision support system for health continuous vigilance in industrial environments. This scheme includes blocks focused on monitoring, collecting and managing data, creating diagnosis and establishing prevention plans. With the purpose of interrelating this modules, in the project emerges the need to develop an architecture which connects and relates all of them.

II. MATERIALS AND METHODS Nowadays, the number of sensors for monitoring personal health data is increasing. In addition, sensors that collect environmental parameters in industrial factories are being introduced more and more. The problem encountered so far, and intended to be solved in this project, is that these data are not connected. The information is only collected in order to produce isolated diagnosis, but not common results, and the collected data become less relevant if they are not treated together. The final decision, in a dynamic environment like a factory, could be more precise if results came from a study of a diverse set of parameters. According to Fasys project, the first step to improve the health and safety in factories is to increase the personal and environmental monitored data. Consequently, there is a need to develop a system able to store all this information. Nowadays, NOMHAD system is an application able to stored part of this required information: workers' personal parameters such as blood pressure, pulse and oxygen saturation. The system performs a prioritization and an intelligent management of alarms. These alarms are based on the rules and protocols accepted by health professionals and by the health system. This service will combine the information, through the prioritized alarm list, with the generation of

specific summaries about the state and evolution of the person. This enables a more efficient management of events. With the purpose of completing the personal data stored in NOMHAD, Fasys project proposes a connection to the current health system. This connection provides a register about the health state of the person during his lifetime, which collects data such as his diseases, surgically interventions or pains. This health system is commonly known as EHR (Electronic Health Record) [11]. Finally, once the information has been monitored and classified, the next point is focused on the intervention. With the aim of representing prevention protocols for this intervention, workflows are developed. Given the workers singularity, the adaptation of the prevention protocols is needed for each one of them. In this way, the elimination of the occupational hazard is much more effective.

III. RESULTS Several studies confirm that, in European Union, approximately 5,500 people per year have fatal accidents in the workplace [1]. These accidents cost a high price for the EU and affect all sectors of the economy, mainly enterprises with less than 50 workers. It has been checked that prevent work accidents has more benefits than just reducing damages [1]. In addition, from the European point of view, the Factories of the Future (FoF) will have fabrication environments highly dynamics, what entails that workers will be involved progressively in more diverse situations [10]. The increase in the number of accidents that currently occurs in factories, added to the European ideas in the factories of the future, makes workers the key roles in the industrial environments. The worker health and safety become a central element in the production process, relating it to the performance, productivity and efficiency. Consequently, there is a need to generate systems for health continuous vigilance of workers. The Figure1 presents the scheme, developed in Fasys, for a health continuous vigilance system. This system is based on five main parts: Monitoring Module; Response Medical Center; Differential Diagnosis Module; Prevention Plans Module; and Intervention Module. Each of these parts can be influenced by a number of external variables and parameters such as the Electronic Healthcare Record Given the big amount of generated information in this model, it is necessary to process all the collected data, since such amount of information would not be easily understandable by health professionals. Services and intelligent devices that have been generated will provide a classification of the monitored data. Some data will be set inside a normal range, and others will be out of the settled limits, generating alarms due to this. Furthermore, this classification will help the doctor to organize and evaluate all workers’ data and at the same time it will be able to act more precisely against a particular diagnostic.

Fig. 1 General scheme for a health continuous vigilance

The content of the blocks shown in Figure1 is:

- Where a group of personal data is collected is the Monitoring Module. - All these personal data, obtained from the monitoring and a group of environment variables from several sensors in the factory, are joined together in the Response Medical Center. As environment variables, one can understand parameters such as, environment temperature or humidity, that is, particular characteristics from the workplaces at which the worker can stay during a work journey. The Response Medical Center allows to filter and organize the population depending on the changeable rules and on the user role. So, it is in this module where the first amount of data is collected, creating, as a result, personalized records of the workers and establishing alerts which make easier the task of health professionals. From this point on, next steps are already focused on getting an action line according to the problem detected. - The information stored in the previous module is not enough to make a complete diagnosis. So, data from other sources are needed such as: • Data from a medical base of knowledge (it contains

relations among diseases, risks, medical tests, medical recommendations, etc).

• Personal data from the health system, which include the previous medical history and it is known as Electronic Healthcare Record (EHR).

• Trend analyzer data. This system is in charge of detecting how some parameters of a person are changing during the pass of time. These parameters can be added to the absolute values in order to get a more complete evaluation of the person.

• Evaluation module results. It can be defined as a “photograph of the person” in a particular moment, with no need to detect a problem.

These four mentioned sources are the subsystems shown in the general scheme, Figure 1, which provide important information to the main blocks. To manage all this information, Fasys has developed the Differential Diagnosis Module, shown in Figure1. This module, through intelligent systems, helps in decision making by health personal. - The next step is to reach the Prevention Plans Module, where it is defined how to act. The measures to be taken can be of two types: on one hand a medical diagnosis and on the other hand a technical diagnosis, for instance a redesign of the workplace. It is important to remark that these measures are not exclusive. According to this, different levels of action can be established. That is, from very complex levels to more simple levels such as, for example, reminder panels. In addition, the prevention actions carried out in this module can be conducted at three levels. At the first level, the system reacts automatically. When one of the collected data reaches a condition that the professional wants to be controlled, there is an automatic reaction. This associated reaction can be the activation of an alarm, a protocol in a situation of risk, etc. These automatic reactions are achieved using ECA rules- Event, Condition, Action. At the second level, health professionals receive the alerts and react to individual workers. The reaction of professionals can be the assignation of a prevention plan developed before, or the assignation of a prevention plan modified for the worker situation in particular. These ways that define the processes are called workflows. The third level is in charge of providing knowledge for the other two levels, improving the protocols, adapting them to new situations and personalizing the recommendations. Innovative intelligent tools are used to manage it. - Finally, the Intervention Module is responsible for performing the particular actuation selected for the problem in question. Fasys system is considered cyclic and of a continuous learning, in a way that, after the Intervention Module, it starts again from the Monitoring Module. Another important aspect to take into account is the personal privacy. As a consequence of this, only a few people will have access to the EHR (Electronic Healthcare Record), to the personal variables, and to the personal diagnosis. From all five main parts, it is going to be emphasized the Response Medical Center. All collected data in this module has to be processed by an application called NOMHAD. In the immediate future, ICT (Information Communication

Technologies) will have an outstanding role in the health sector. This will allow the improvement of the current processes, making them more accessible and efficient. Important efforts have been performed to extend its use in the health sector. This module receives automatically monitored data from all workers. This information is treated to prioritize and manage more efficiently the attention and the available resources for the factory population. So far, the options managed by NOMHAD are specifically the following: • To create a patient. The professional will fill the

administrative worker data and the medical relevant data for a future evaluation. Patients will also be assigned to health professionals.

• Reception and Display of Monitoring. The system stores the monitoring data of the person. It stores them into a database related to the personal health record in order to be used by health professionals in the future. The monitoring data are displayed in the right way, either in graphical form, numerical, image, etc The data stored in the system are processed on arrival. A set of several rules, defined by the doctor and adapted to the personal profile, are applied. These rules allow the system to detect potential anomalies found in the data, in order to take decisions. For the definition of the rules, health professionals will have a tool to help themselves with this task.

• To configure alerts: The system will have an alert module that, based on the monitoring data and the limits defined as optimal by a professional, will be able to detect whether the data stored are acceptable or not.

• The possibility of assigning questionnaires: The patient mood could be extracted from these questionnaires. Health professional will choose the questionnaire and will have the possibility to personalize it depending on the needs of each worker in particular. These questionnaires will be available in the future in case the professionals want a subsequent consulting or validations.

• Monitoring: The measurements can be obtained with usual external devices, whose information is introduced afterwards in the system or it can be used integrated elements into the system (controlled, for example, by Bluetooth and transmitting the captured data directly to the system). The design of the system can be extended to introduce new devices.

The following shows the main screen of the application.

Fig. 2 NOMHAD system

When the general modules and the relation among them are defined, an architecture must be created, that is to say, a way to guarantee the connection and interoperability among them. To get information from the worker environment and his personal parameters, it is necessary to interconnect sensors and services in a fault tolerance and decentralized way. This process, complex and highly interconnected, can be solved using Choreography of Services [12-8]. This means that the choreographied processes are independent and can communicate each other to define execution flows. This model makes easier the connection and disconnection of services dynamically, and at the same time it is capable of using different kind of sensors and configurations. This approach is shown in Figure 3. The use of choreography to interconnect services requires also the use of a common exchange language to allow the services to understand each other. This can be performed by an architecture which includes a Semantic layer in the Choreographer. The reason to do that is to improve the intercommunication among sensors, actuators and services of the system. The ontologies [12] are a solution to describe concepts formally. Concretely, an ontology is a formal and explicit specification of a shared conceptualization. It provides a common vocabulary that can be used to model the kind of objects and/or concepts and its properties and relations. The reasoners [12] are software applications allowing the semantic seek in the ontologic description. Using this technology, it is possible to describe semantically the sensors and data services, giving them the ability of having a more complete understanding of the collected data and the services actions. The use of services of Ontologies and Reasoning Systems to describe the data coming from the sensors, makes possible to get a more precise interpretation and to detect automatically the sensors and services available at any time.

Fig. 3 Fasys Architecture

In order to illustrate graphically the action to perform and the standards describing the flow followed by actions, it is possible to use workflows. They are a formalization of the process to be automated. Some workflow languages can be executed automatically. This is known as a workflow interpretation. The automatic interpretation of a workflow is done by a workflow engine, which can complete the actions explained in a workflow, in the order and with the derivation rules specified in it. Workflows can be employed by people who are not experts in programming for the health area. For that reason and thanks to these modules, health professionals are able to design and modify the protocols to be executed automatically. A Services Orchestrator [8] is included in the architecture and moreover, it is connected to the Choreographer which accepts the use of Workflows. Finally, the objective of this paper is to show the current situation of the health data warehouses related to Fasys project. Nowadays, there is a health system ready to be used by health professionals. It stores the medical data of the patient from the point of view of the assistance process, and it is owned by the current healthcare system. This repository is called EHR (Electronic Healthcare Record). With the purpose of improving the characterization of the person and his environment, EHR data have been increased. This new amount of information is stored, together with the EHR information, in other repositories. These repositories are known as PHR (Personal Health Record) [13] and can collect data such as habits, preferences, information about the family, moods, customs or nutritional profile. A PHR adapted to the

needs of Fasys requirements is being developed. This kind of repositories is owned by the person, who has the option to share it with people he chooses. When an employee goes to work in a factory for the first time, health professionals ask him to download his previous EHR, in order to have the personal file (PHR) more complete for the final diagnosis. In general, current PHR contains a summarized version of EHR ready for patients and, in some cases, home monitoring data. PHR developed by Fasys is based on the following aspects: • It is focused on workplace health. • It allows patient to introduce data (automatically or

manually) • It allows an exchange of information with the healthcare

system (EHR) • It includes an option to generate summaries to share

information with others PHR. One of the advantages, for example, is when a worker goes to work in other factory. If the new factory has the Fasys system, his PHR could be downloaded in the system of the new factory in order to have a more complete file.

• Stored Data can also be extracted for consultations in case health professionals need to do. Consequently, there must be an Access Control. With this control, it is ensured that these personal data can only be seen by authorized people. If the data have to be used for statistical studies, it must be made anonymous. So, the results of the studies will not be related to people in particular.

In addition, and with the objective of validate the developed work in the different stages of the project, several meetings with experts have been done. On the one hand, the first meetings had the objective to clarify the main points to be considered for a health vigilance scheme. And on the other hand, the last meetings had the mission to validate the developed scheme of health vigilance. Their point of view is vital to perform a good scheme of a support decision and a health continuous vigilance, directing it to solve the real needs that contain each covered area.

IV. CONCLUSIONS According to the project objectives, a scheme of health continuous vigilance has been designed. It provides a workable solution in order to improve the current healthcare system in the factories of machining, handling and assembly operations. It is possible to obtain a more continuous monitoring of the worker, improving his own health and making the factory safer and healthier. To achieve this, it is necessary to increase the number of variables obtained from the environment of the worker, from personal parameters, and to combine them with the medical knowledge and the actuation protocols.

Future steps will be focused on the detailed definition of some modules of the scheme of health vigilance that have to be completed. The optimal way to fit all input and output data should be studied in depth. It is important to remark the proper connection with other modules and smaller subsystems which they are related with.

Up to now, the application included into the Response Medical Center collects data from personal monitoring. In the future, this application will be improved, introducing relevant data for the project, such as: environment variables (room temperature, humidity…) or type of machine used (which is related to one kind of strain or another). Besides the introduction of new parameters, other two innovations for this application are being studied: • Possibility to carry out videoconferences between the

doctor and the worker. This action will improve the continuous monitoring. On the one hand, it will be useful for external consultations, in case the doctor is not in the factory. And on the other hand, it will be useful to raise remote queries to specialists.

• Mobility. The goal is to build a little version of the application. It can be used on small devices like a tablet. Thus, the information will be available anywhere, carrying out a supervision of the processes and a management of alerts in real time.

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