SmartWork

Kocsis, Otilia; Μουστάκας, Κωνσταντίνος; Fakotakis, Nikos; Vassiliou, Charalampos; Toska, Androniki; Vanderheiden, Gregg C.; Stergiou, Achilleas; Amaxilatis, Dimitrios; Pardal, André; Quintas, João; Hermens, Hermie; Cabrita, Miriam; Dantas, Carina; Hansen, Sonja; Ziemke, Tom; Tageo, Valentina; Dougan, Paula

doi:10.1145/3316782.3322766

Cited by 11 publications

(5 citation statements)

References 18 publications

(16 reference statements)

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“…Mukhtar et al [54] proposed a framework that uses sensors to check the health of a person carrying any disease, or not. Kocsis et al [55] developed a worker-centric AI system in the SmartWork project that provides a good work environment for office workers. It also helps them to increase work efficiency and productivity of the employees through better management of the available resources.…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

Uppal

Gupta

Juneja³

et al. 2022

Sustainability

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The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring automation systems efficiently. In this paper, a prediction model that monitors real-time data of sensor nodes in a clinical environment using a machine learning algorithm is proposed. An IoT-based smart hospital environment has been developed that controls and monitors appliances over the Internet using different sensors such as current sensors, a temperature and humidity sensor, air quality sensor, ultrasonic sensor and flame sensor. The IoT-generated sensor data have three important characteristics, namely, real-time, structured and enormous amount. The main purpose of this research is to predict early faults in an IoT environment in order to ensure the integrity, accuracy, reliability and fidelity of IoT-enabled devices. The proposed fault prediction model was evaluated via decision tree, K-nearest neighbor, Gaussian naive Bayes and random forest techniques, but random forest showed the best accuracy over others on the provided dataset. The results proved that the ML techniques applied over IoT-based sensors are well efficient to monitor this hospital automation process, and random forest was considered the best with the highest accuracy of 94.25%. The proposed model could be helpful for the user to make a decision regarding the recommended solution and control unanticipated losses generated due to faults during the automation process.

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Section: Related Workmentioning

confidence: 99%

Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

Uppal

Gupta

Juneja³

et al. 2022

Sustainability

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“…Kocsis et al [100] introduced the SmartWork model to assess aged workers' health, emotion/stress, cognitive, task models, and workability. Workers' health, emotion/stress, cognitive, task models, and workability were modelled from the remote monitoring of workers' activities to evaluate their cognitive and functional decline by their employers for decision-making.…”

Section: The Smartwork Modelmentioning

confidence: 99%

“…The SmartWork model is the only extensive model in SEs that caters for aged workers' workability, and it is similar to the proposed behavioural model in this research. However, these aspects were well differentiated and structured into appropriate models in the following ways: (i) Workers' health modelling was modelled within the workercentric AI block of the SmartWork model [100]. This study modelled health as an internal context within user modelling based on a previous study [31].…”

Section: The Positive Behaviour Change Modelmentioning

confidence: 99%

The PBC Model: Supporting Positive Behaviours in Smart Environments

Adewoyin

Wesson

Vogts

2022

Sensors

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Several behavioural problems exist in office environments, including resource use, sedentary behaviour, cognitive/multitasking, and social media. These behavioural problems have been solved through subjective or objective techniques. Within objective techniques, behavioural modelling in smart environments (SEs) can allow the adequate provision of services to users of SEs with inputs from user modelling. The effectiveness of current behavioural models relative to user-specific preferences is unclear. This study introduces a new approach to behavioural modelling in smart environments by illustrating how human behaviours can be effectively modelled from user models in SEs. To achieve this aim, a new behavioural model, the Positive Behaviour Change (PBC) Model, was developed and evaluated based on the guidelines from the Design Science Research Methodology. The PBC Model emphasises the importance of using user-specific information within the user model for behavioural modelling. The PBC model comprised the SE, the user model, the behaviour model, classification, and intervention components. The model was evaluated using a naturalistic-summative evaluation through experimentation using office workers. The study contributed to the knowledge base of behavioural modelling by providing a new dimension to behavioural modelling by incorporating the user model. The results from the experiment revealed that behavioural patterns could be extracted from user models, behaviours can be classified and quantified, and changes can be detected in behaviours, which will aid the proper identification of the intervention to provide for users with or without behavioural problems in smart environments.

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“…Accounting for both, societal needs and technological advances, SmartWork project aims to build a Worker-Centric AI System for work ability sustainability, by integrating unobtrusive sensing and modelling of the worker state with a suite of innovative services for context and worker-aware adaptive work support [2]. Work ability is directly linked to the functional abilities and cognitive capacity of the worker, which are continuously assessed by unobtrusively and pervasively monitoring the health, behavioral, cognitive and emotional status of the office worker.…”

Section: Introductionmentioning

confidence: 99%

“…This work is an exploratory study on the potential of a Laplacian matrix completion variant for data imputation 978-1-7281-4754-3/19/$31.00 ©2019 IEEE / BIA-2019, ID 48344X on various time-series data collected with wearable or work devices in the SmartWork unobtrusive monitoring system [2]. The usage of the proposed algorithm for big data is investigated by considering variable matrix size and by comparing the results to other imputation approaches.…”

Section: Introductionmentioning

confidence: 99%

Coping with missing data in an unobtrusive monitoring system for office workers

Nousias

Papoulias

Kocsis

et al. 2019

2019 International Conference on Biomedical Innovations and Applications (BIA)

Self Cite

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Current trend of population ageing at global level is accompanied by increased prevalence of chronic diseases and higher rates of early retirement and labor market exit. In particular, the lifestyle of office workers is characterized by prolonged sitting and overall sedentary life, which alone is a high risk factor for cardiometabolic diseases, obesity and other related chronic diseases. The SmartWork unobtrusive monitoring system allows for continuous monitoring of various lifestyle, health, behavioural and work related parameters of office workers targeting to empower work ability sustainability. The large amounts of collected data in such systems are often characterized by the presence of missing entries. This work is an exploratory study on the potential of a Laplacian matrix completion variant for data imputation on the multi-channel time-series data collected with wearable or work devices in the SmartWork system.

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SmartWork

Cited by 11 publications

References 18 publications

Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

The PBC Model: Supporting Positive Behaviours in Smart Environments

Coping with missing data in an unobtrusive monitoring system for office workers

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