CPS for security and wellbeing of shop floor workers
The Application Experiment will collect data from shop floor workers and provide feedback at work or at home to promote worker’s safety and wellbeing
The present application establishes a platform that collects data from workers and provides advice to the worker and to the enterprise’s human resources about the best strategies to keep the worker safe and fit at work and at home.
With the objective of improving life and workplace balance, prevention Occupational Service Health schemes are permanently being developed in multi-businesses industry parks in Portugal covering the multiple aspects of the Citizen wellbeing @home and @workplace, physiotherapist, GP on demand including the adoption of Wearables, smart textiles, training and fitness data, home training.
The approach to be developed is based on agreements with the worker so that he can maintain the fitness and wellbeing with a monitoring strategy that can inform him about his health status while providing guidance for a better life and adjustment to his specific health condition. The objective in such interventions is to maintain supportive actions towards the workers, especially those with some age, that while being less young maintain, however a high value creation potential. The monitoring of bodily variables, especially in old workers, becomes of most value to provide the proper maintenance of health and lifestyle thus ensuring a cooperation that keeps them going for longer time as a value for themselves and for the society.
This way a platform is developed for the gathering of information and the evaluation of parameters, which will provide useful insights on the health condition and the best strategy to develop fitness-training programs and adjust the working schedule for the benefit of the worker and the ongoing work. These programs can include programs at work in properly design places or for exercising at home thus providing security and trust for those willing to train but afraid of injuries or abnormal health outcomes. This way DIH4CPS will aim at advising and ensuring supportive actions for a longer permanence in active work and to promote wellbeing at home.
|Uninova||Systems development for physiological monitoring and assessment||Running and validating the pilot|
|KnowledgeBiz||Implementing strategies for the uptake of the industry 4.0 and Developing technology solutions in eHealth.||Software Development
|Produtech||Industrial Association||Provide real case studies|
Problem/Business Case description
Workers suffer from different types of physiological and posture related problems due to work execution without advice and corrective measures. That is a problem that happens at work and even at home .The cooperation will be based on workers data collection where appropriate using smart sensors and devices (e.g. smart shoes, smart belts, smart t-shirts, smartphones, etc.) that will be used to monitor older workers physical activity through a set of parameters: e.g. standing hours, trunk flexion, load lifting/pushing pulling, body vibration, etc. The objective is to trigger real-time corrective notifications to older workers through the conversational interface, in order to reduce the risk of injury, but at the same time, to notify the worker and the human resources department, about causes affecting physical conditions of workers, so that preventive measures can be taken within the working environment. In order to materialize such objective, the platform considers non-obtrusive theft methods to alert the worker to improve its posture or relax its shoulders and back muscles. These alerts can come from the combination of data collection from sensors and actuators placed around the worker, on the desk furniture, or on an ergonomic chair, and will provide personalized feedback to improve postural behaviour. On the workers side, the usage of technological bracelets will provide readings of heart rate and will alert about high values often associated with stress situations. The system will also recommend pauses during the day and issue warnings about the maximum time exceeded, inviting the worker to leave the office.
After work, older workers usually arrive home tired and just want to relax by being seated watching tv or reading a book. This consecutive routine will slowly deteriorate the person’s body and mind, and is the situation is drastically increased by the lack of physical and mental exercises that can oppose the usual after work sedentary behaviour. Another problem is the place where the person usually sits or the mattress where it sleeps may not be the most appropriate and can continue to deteriorate their health. In order to address such identified problems, at DIH4CPS the older worker continues to be accompanied by the devices, that he is wearing or that exist in his living environment. These communicate with the Platform and provide valuable information that may go unnoticed by the person. By analysing those feeds of information, from the worker and the surrounding environment, the user can be notified on how to improve his living environment. Those recommendations may include the suggestion to turn on the lights when watching tv, change sleeping mattress, modify room temperature or even play relaxing music. In general, the ageing workers will be stimulated to exercise to eat better and to modify their most penalising living habits.
The experiment will consist in establishing different modalities for data collection that will enable the reasoning and advice to workers improvement of posture, wellbeing and lifestyle.
Information gathering platform – Platform to collect and analyse data
- User centric profile gathering architecture
- System with data collecting protocolls implemented
- Algorithms for processing and analyse gathered data from users
- Infrastructure developed and installed
User’s data collection systems
- User’s data collection devices established and configured
- Applications for the data collection purposes
- Hardware and software developed and implemented
Machine learning Analytics – Platform developed
- Collected data analysed with machine learning algorithms
- A.I. developed to improve the systems efficiency and prediction methods
System implementation, testing and training
- System developed and implemented in real environment
- Tests with users to evaluate and improve the system
- Training program developed on the established platform