AHFE Open AccessCognitive Computing and Internet of Things
Editors: Lucas Paletta
Topics: Cognitive Computing and Internet of Things
Publication Date: 2024
ISBN: 978-1-964867-00-7
DOI: 10.54941/ahfe1004697
Articles
Wearable Solutions for Smart Integrated Extreme Environments Health Monitor System.
This paper is an introduction to the design solutions implemented within the EU-funded SIXTHSENSE project; a multidisciplinary innovation and research project with the overall goal of significantly improving the effectiveness and safety of first responder deployment in hazardous environments by optimizing on-site team coordination and mission execution. The project proposes an innovative multimodal monitoring system based on biochemical and physiological sensors data that allows the detection in real time of the physical and mental status deterioration of the first responders deployed in the field. The core of the SIXTHSENSE platform is a sensing garment with a closed loop tactile biofeedback [1], that allows first responders in hazardous situations to receive recommendations related to their physical and mental status, as well as operational indications from the remote command center. The concept of the platform developed within the project focus on the applications related to the deployment of firefighters [2] and mountain rescue services in extreme conditions [3]. The system is equipped with integrated electrochemical and electrophysiological sensors embedded in a garment that ensures intimate contact with the skin to accurately detect parameters related to physiological and mental strain. System also comprises an array of electro-tactile pads providing intuitive tactile feedback in hazardous situations. The garment also combine electronics for acquisition and fusion of sensor data, a microstimulator and controller for generation of spatio-temporally distributed electrical pulses, and communication modules, all embedded in a wearable device.The development of the system is performed iteratively, advancing in parallel hardware (sensors, electronics, electrodes), software (sensor data processing, calibration algorithms, electrotactile feedback control, command center dashboard), and research (data analytics, feedback representation, new telecommunications paradigms, etc .).The overall approach of the project is envisioned through core activities that are embedded in both applications. These are complemented with specific actions, like integrating the system with a specific sensor configuration into appropriate garments and using telecommunication channels best suited for the intended environment. These activities have been performed in three consecutive iterations, following a progressive processing approach to result in two demonstrator systems related to the applications, based on three main development iterations, named Alfa, Beta and Gamma each producing a prototype platform.[1]M. Štrbac et al. , ‘Integrated and flexible multichannel interface for electrotactile stimulation’, J. Neural Eng., vol.13, no. 4, p. 046014, 2016..[2]B. Carballo-Leyenda, J. G. Villa, J. López-Satué, J. A. Rodríguez-Marroyo. “Characterizing wildland firefighters’ thermal environment during live-fire suppression.” Frontiers in Physiology, vol. 10, pp. 949. 2019.[3]D. Curone, E.L. Secco, L. Caldani, A. Lanatà, R. Paradiso, A. Tognetti, G. Magenes, “Assessment of Sensing Fire Fighters Uniforms for Physiological parameter Measurement in Harsh Environment,”IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 501-511, 2012
Rita Paradiso
Open Access
Article
Conference Proceedings
Innovative Biosensor based Aeromedical Monitoring Solution for Specific Military Medical Evacuation Scenarios
Natural disasters, industrial accidents, or outbreaks, as evidenced by the COVID-19 pandemic, underscores the importance of preparedness in managing immediate evacuation of affected individuals. Infectious diseases pose the risk of compromising the transport environment by potentially contaminating the interior of the transport vessel. Addressing this challenge, this paper presents the DEKO-AirTrans solution introducing the development and implementation of a mobile evacuation system, tailored for use aboard the C-130 Hercules aircraft. The system features a flexible cell construction anchored to a multifunctional HCU-6 pallet, enveloped in a specifically designed protective tent material with high filtration and air permeability qualities to avoid contamination of the C-130 interior. Integrated sensor systems and wireless biosensors allow for continuous health monitoring of transported individuals, ensuring medical oversight during transport. DEKO-AirTrans represents an advancement in the air evacuation domain, offering a cost-effective, rapidly deployable, and flexible solution for the safe transport of highly infectious or contaminated persons. The system's design ensures quick setup, ease of storage, transportability, and compatibility with existing standards, highlighting its potential as a solution for emergency medical evacuation scenarios.
Florian Haid, Alexander Almer, Anna Weber, Markus Bergen, Gerald Bauer
Open Access
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Conference Proceedings
Semantic Decision Support for Action Forces with Risk Stratification from Estimated Physiological Strain, Cognitive-Emotional Stress and Situation Awareness
In the life-threatening work of action forces, a decision support system (DSS) must provide a software application that should improve a mission decision maker's capability to make decisions. This requires analysing large amounts of data and to present and visualize the best possible options available. In case of first responders, where errors in decision-making can have fatal consequences, timely identification of increased risk of physiological collapse, insufficient cognitive readiness and lack of situation awareness is mandatory. This paper therefore introduces our Semantic Decision Support System (SDSS) that can apply intelligent analytics on data from wearable biosignal sensors, to provide feedback in terms of risk stratification. It also includes a recommender engine that identifies the best next action at team management level. Its novelty lies specifically in the combination of various multimodal data streams each being equipped with assessment modules, risk stratification and recommender engines in order to finally combine various aspects of decision support that is based on psychophysiological measurement technologies. All relevant data is systematically merged into an advanced expert dashboard, providing a comprehensive platform for the continuous real-time monitoring and visualization of critical information. This capability enables the ongoing assessment of risk levels associated with a diverse group of action forces. The centralized dashboard serves as a powerful tool, enabling careful surveillance and prompt response to emerging risks across a broad spectrum of operational scenarios.
Florian Haid, Michael Schneeberger, Belén Carballo Leyenda, Jose A Rodríguez-marroyo, Stefan Ladstätter, Anna Weber, Alexander Almer, Jochen Mosbacher, Lucas Paletta
Open Access
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Conference Proceedings
Deep learning based Human Activity Recognition in first responders wearing a sensorized garment
Safety and well-being of first responders operating in hazardous environments are paramount considerations. These individuals routinely find themselves immersed in dangerous situations, leading to heightened levels of both physical and mental stress. In this context, a system for the automatic and real-time monitoring of first responders' (FRs) activities could play an important role in timely identifying potentially dangerous situations. The present paper addresses this issue and introduces a Deep Learning (DL) based Human Activity Recognition (HAR) approach for the automatic identification of tasks carried out by first responders. In our proposed framework, we leverage the use of a garment equipped with various integrated sensors to capture both physiological and inertial measurements during the course of first responders' duties. For this aim we harness the power of DL techniques, specifically recurrent neural networks (RNNs), aiming at achieving an accurate classification of a limited set of diverse tasks. To validate the efficacy of our proposed system, we conducted the evaluations on a comprehensive hold-out set compiled from real-world scenarios, involving FRs. The results of our evaluation showcase not only high accuracy (0.9813) but also robust reliability in classifying the activities undertaken by the operators. The implications of our deep learning-based activity recognition framework extend beyond mere classification, since gaining insights into the risk associated to a particular task performed could enable the development of more effective, timely and safer emergency response strategies.
Edoardo Spairani, Rita Paradiso, Giovanni Magenes
Open Access
Article
Conference Proceedings
Wearable System for the evaluation of Well-Being in the Workplace
Healthcare workers experience physically, emotionally stressful situations, are exposed to human suffering, experience pressure from interactions with patients and family members, and are under constant threat of infection, injury and stress. Healthcare workers are at greater risk of developing stress-related mental disorders, such as depression or post-traumatic stress disorder (Braquehais et al., 2023, doi:10.1016/j.mcna.2022.04.004). The COVID-19 pandemic has highlighted the need for healthcare organizations to ensure the well-being of healthcare workers. Indeed, more stressful working hours, the fear of being infected and the need to ensure immediate decision-making have significantly increased the risk of burnout, depression, anxiety and insomnia. In the USA and Europe, a series of regulations have been issued to preserve the health of workers, specific to the workload linked to the various tasks and in the literature work-related stress indices have been evaluated in the healthcare sector, linked to muscle disorders skeletal injuries due to patient handling, for nurses and personal care/assistance workers. However, biomechanical overload and the risk of damage to the musculoskeletal system are only one aspect linked to the health of the worker: the dimension of work stress has a significant role in the general well-being of the worker during his activities and a methodology for the objective assessment of mental and physical workload in work environments.In this study we propose the use of a wearable system (WWS by Smartex) compatible with work activity, to monitor and extract significant information on the workload, due to the physical demand and the physiological response to stress, on a sample of physiotherapists. The system consists of a t-shirt for the continuous detection of a cluster of physiological parameters, that can be stored and processed during work. The system detects an ECG lead via integrated textile electrodes, the respiratory signal through the measurement of thoracic movement, posture, and physical activity recognition, via an Inertial and Magnetic Measurement Unit (IMMU), integrated into the RUSA device, a portable data logger dedicated to the acquisition, processing, storage and/or transmission of data.The RUSA is connected to the garment through a simple plug and can be easily unplugged when necessary. The T-shirt is absolutely similar to a common underwear. The base yarn is composed by antibacterial materials to guarantee a safe and prolonged use. The sensors are made of fibers that are directly woven during the production process to be fully integrated in the garment without discontinuity. The shirts come in male and female version and in several sizes to fit the largest number of users. The data acquired by the RUSA are processed on board to extract the following parameters: Hearth Rate (HR), HR Variability, RR interval, signal quality, Breathing rate, activity classification, activity intensity. The RUSA can save data on a Flash Memory (microSD), transmit data via Bluetooth® 2.1), save and transmit them simultaneously, without losing information in case of interruption of wireless transmission.A pilot study has been performed on 11 physiotherapists, engaged in XX sessions. During the study, the cluster of physiological data have been combined with a set of meta-data related to the work session such as the type of intervention (i.e. neurological rehabilitation, orthopaedic rehabilitation, etc), the level of physical impairment of the patient (according to modified Rankin score and Communicative disability scale), the working place etc.). As well as to the results of NASA questionnaire that has been administrated after each acquisition section to the physiotherapists. Preliminary results on the stress level will be presented, in parallel to evaluate the use of the IMMU platform for the overload of the musculoskeletal system, research on the posture evaluation in the rehabilitation workplace has been performed. The accuracy of a single IMMU to retrieve trunk angles was assessed by comparison with stereophotogrammetry. The results revealed that the IMMU is adequately effective in determining sagittal angles but has limitations in assessing lateral and transverse angles in a natural and uncontrolled environment.
Rita Paradiso, Eefke Krijnen, FEderica Vannetti
Open Access
Article
Conference Proceedings
Validation of Wearable Biosignal Sensor-based Estimation of the Physiological Strain Index Using Gaussian Process Regression
At physiologically intensive work or during acute exercises, early alert functions are highly required to prevent physiological damage to human health. Wearable sensor-based monitoring of vital parameters can provide real-time measures for the quantification of a worker’s individual psychophysiological and thermal strain to define risk levels for appropriate decision support. One of the most well-recognized indices suitable for use in the workplace so far is the Physiological Strain Index (PSI; Moran et al., 1998) based on sensor data about (i) the core body temperature (CBT) as well as (ii) the heart rate (HR). Until recently, the ground truth information about CBT was particularly measured by cumbersome swallowing expensive gastrointestinal temperature pills. A more comfortable strategy is to attach bioelectrical temperature sensors to the human skin and from these data provide an estimate about the CBT. Dolson et al. (2022) provided a systematic review on distinct algorithms to predict the core body temperature using wearable technology. Most of these algorithms deployed Kalman filters for the prediction. Only a few algorithms incorporated individual and environmental data into their core body temperature prediction, despite the known impact of individual health and situational and environmental factors on the CBT. The presented Machine Learning (ML) framework provides a comparison between a large set of Artificial Intelligence (AI) methods. The Gaussian Process Regression method (GPR; Rasmussen and Williams, 2006) has determined the minimum root mean square error (RMSE) on data from a highly challenging exercise profile applied by a wildland firefighter group. The results are highly competitive with the methods reported in Dolson et al. (2022).
Michael Schneeberger, Belén Carballo Leyenda, Jose A Rodríguez-marroyo, Lucas Paletta
Open Access
Article
Conference Proceedings
Requirements for Virtual Reality-based Trainings of Human-Robot Interaction
Nowadays, the use of robots has grown into a standard in industry. In this context, the interaction of humans and robots is intended to combine the relevant capabilities in order to achieve the highest possible efficiencies. This points to the need for an appropriate training to reduce fears and increase trust and acceptance. Virtual realities (VR) can be a helpful platform for such trainings. The aim of the study was to examine subjective impressions and suggestions for implementing a VR training in the industrial context. A simple interaction with an industrial robot, conducted in the three scenarios "reality", "virtual reality" and "hybrid reality", was used. The interviews revealed a large pool of information and concrete suggestions for the implementation of VR training. The information obtained provides a useful basis for designing different training scenarios.
Jonas Birkle, Verena Wagner-hartl
Open Access
Article
Conference Proceedings
SmartAktiv: A tablet- and virtual reality-based training for individuals with cognitive decline
Against the background of population ageing and the increasing global prevalence of chronic diseases, such as dementia (Nichols et al., 2022), and the associated increase in healthcare services, the development of new technologies to counteract an impending care gap is of great importance. Virtual Reality (VR) has been identified as a new technology in addressing the needs of older people (Abdi et al., 2020) and shown to improve quality of life (QoL; Afifi et al., 2022) and performance of activities of daily living (ADL; Ge et al., 2018). To the authors’ knowledge there is no combination of immersive VR and tablet training that (1) has been developed with and for people with cognitive decline, and (2) targets intelligent multimodal activation and ADL training within meaningful/leisure activities. The aims of the SmartAktiv project concern the development of a tablet- and VR-based training environment and its impact on QoL and ADL performance as well as the correlation of digital biomarkers for dementia and different screening tools and questionnaires. Methods: In a first phase, data from previous VR-studies were examined and discussed in an expert workshop (n = 6) and a focus group (n = 7 people with cognitive decline) to develop the VR scenarios. The tablet-training is based on an earlier study, its content was adapted to the VR scenarios within an expert workshop (n = 10). In a second phase, a usability study with n = max. 9 participants with cognitive decline and n = max. 3 health professionals will be conducted to adjust the tablet-VR-system. In the third and final phase, the system will be tested in a one-month pilot study (n = max. 30) including participants with subjective cognitive decline, mild cognitive impairment, and mild dementia. Collected data will comprise quantitative and qualitative data from questionnaires/scales, sensor data from the VR-glasses, tablet-PC, biosensors and interviews to assess QoL, usability, cognition, ADL-performance, and experience. First Results: Four scenarios (beach, winter, hiking, and urban experience) were selected for further development. The focus group provided valuable insights into the preferred means of transport and the planned activities (e.g., gathering mushrooms in a forest, eating ice cream in a beach bar) within each VR scenario. The planned training (tablet and VR-scenarios) will cover the entire journey, from planning (e.g., packing a suitcase) to embarking on the chosen trip (e.g., boarding the right means of transport), and will integrate interactive cognitive (I)ADL tasks, such as managing finances and medication. Results of the usability and pilot study will be presented at the conference. Conclusion: SmartAktiv aims to develop an innovative learning environment which enables multimodal activation through playful interactive cognitive training. Additionally, SmartAktiv strives to advance the use of biomarkers in the early diagnosis of cognitive impairments.
Anna Schultz, Lucas Paletta, Amadeus Linzer, Judith Goldgruber, Ursula Berger, Wolfgang Kratky, Silvia Russegger, Sandra Draxler, Thomas Orgel, Amir Dini, Michael Schneeberger, Martin Pszeida, Wolfgang Weiss, Thomas Pfitzer, Marisa Koini, Sandra Schuessler, Julia Zuschnegg
Open Access
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Conference Proceedings
How to manage the safety of service robots operating in coexistence with demented patients
The developing elderly wave is expected to give a considerable demand for robotic solutions in the elderly care, especially in the nursing homes, to compensate for the lack of human resources required to maintain the quality of the elderly care services. Thus, social service robots are expected to fill an important role in the future, by providing services like logistics, remote medical consultations, entertainment, physical training etc. However, new challenges appear when introducing service robots in an environment where residents with impaired cognitive skills, coexist with service robots. This, especially, since the patients cannot take care of themselves, and are formally NOT responsible for any unfortunate safety conflicts. Thus, compared to operating in e.g. a normal restaurant, a service robot in a in a nursing home requires additional attention to the risks mitigation to ensure a safe operation. This includes analysis of both additional physical risks in addition to new, interperceptual risks. This paper addresses the complexity of fulfilling the required safety of service robots operating in nursing homes, and an extended risk mitigation methods is suggested in order to minimize the unavoidable, residual risk.
Trygve Thomessen
Open Access
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Conference Proceedings
PREPARIO - Service Design for a Connected and Automated Food Preparation Platform
Existing solutions for meal preparation are insufficient for many aged people who struggle to re-heat their delivered meals. It often happens that the meals get over- or under heated which can ruin the meal itself as well as destroy the food experience. In some cases, this leads to malnutrition and severe illness and in other cases caregivers are needed to assist with food preparation on an everyday basis, which is time that could be spent better with the client and poses a great financial burden on society.We developed a holistic solution for the preparation of delivered meals. The goal is to enable aged people to prepare their meals safely and independently at home for longer. The solution consists of a novel microwave oven with wireless temperature control that enables fully automated heating of delivered meals to optimal serving temperature. The solution is supported by a digital voice assistance platform and a data-driven support system for external monitoring to allow for efficient care provision. This paper illustrates the applied design process and its accompanying activities and results. The focus is on developing and evaluating a whole service for a diverse user group with an end-user- and customer-centred design process. Co-creation workshops and field trials have been carried out. This lead to detailed insights in the needs and wants of the targeted groups. These activities lead to guide the further technical development of the components as well as to establish a new B2B2C business model with the aim to reduce the burden on the primary end-users.
Wolfgang Weiss, Henrik Schneider, Sandra Draxler, Manuela Ferreira, Adriana Antunes, Celine Sommer
Open Access
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Conference Proceedings
IoT-based Vertical Farming Systems
Vertical farming, a revolutionary approach to agricultural production, has gained significant attention in recent years due to its potential to address various challenges facing traditional farming practices. This paper provides a comprehensive overview of IoT-based Vertical Farming systems, exploring their hardware design, implementation strategies, testing methodologies, and prospects.The hardware design of IoT-based Vertical Farming systems encompasses a range of components essential for creating optimal growing environments. Soil moisture sensors, temperature and humidity sensors, light-dependent resistors (LDRs), and ESP32 Wi-Fi modules are among the key elements utilized in these systems. Soil moisture sensors enable precise irrigation management by measuring water content in the soil, while temperature and humidity sensors provide insights into environmental conditions. LDRs detect light levels, facilitating optimal lighting control, and ESP32 Wi-Fi modules enable wireless communication for remote monitoring and control.Implementation strategies for IoT-based Vertical Farming systems involve hardware setup, software development, and integration with existing infrastructure. Sensor nodes distributed throughout the farming environment are connected to a central control unit via Wi-Fi or other communication protocols. Software interfaces and applications are developed to provide users with real-time monitoring and control capabilities, allowing them to adjust environmental parameters as needed.Effective testing methodologies are crucial for ensuring the reliability, functionality, and security of IoT-based Vertical Farming systems. Black box testing focuses on external functionality, such as user interface interactions and sensor responses, while white box testing examines internal system components and code logic. Grey box testing combines elements of both black and white box testing, with a focus on limited knowledge and system behavior.The future prospects of IoT-based Vertical Farming are promising, with opportunities for innovation and advancement. Research and development efforts are needed to enhance system scalability, energy efficiency, and data analytics capabilities. Integration with artificial intelligence (AI) and machine learning (ML) algorithms can enable predictive analytics and autonomous decision-making, optimizing crop production and resource utilization. Expanding the application of vertical farming to diverse environments, including urban areas and arid regions, can address global food security challenges and promote sustainable agriculture practices.In conclusion, IoT-based Vertical Farming represents a transformative approach to agriculture, offering scalable and sustainable solutions to meet the growing demand for food production. Continued research, development, and adoption of these systems have the potential to revolutionize the agricultural industry and contribute to a more food-secure and environmentally sustainable future.
Javed Anjum Sheikh, Asia Mumtaz, Saba Farzeen
Open Access
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Conference Proceedings
Democratization in Industry via Multi-Agent Systems, The case of a production company
Democracy is typically a question of political government. Nevertheless, in recent years, the forms of democratic development have changed in the course of the governance debate. According to Council of Europe, E-democracy tools use technology to boost key democratic values like participation, inclusivity, efficiency, effectiveness, transparency, openness and accountability within the democratic system. Alongside civil society, companies are playing an increasingly important role in the establishment of collective order. The difficult aspects of this development can be seen in the concentration of market power and the circumvention of employee co-determination. At the same time, however, Small and Medium Enterprises (SME) sometimes take on the role of pioneers. One key example is about AI-based decision support systems in order to realize new decision-making and co-determination opportunities. This raises the question of what potential for democratization and, if so, in what form, is actually emerging here. On the one hand, this article raises the question of how aspects of a “democratization” in companies can be realized and presents a conceptual approach for analysing such ambitions. On the other hand, specific challenges of a “democratization” via digital tools will be worked out by analysing the case study of a SME. An important result is that forms of democratization via multi-agent systems are only perceived as democratic if they are introduced and procedurally anchored within the company through social processes that are perceived as legitimate.
Noushin Gheibi, Stefan Boeschen
Open Access
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Conference Proceedings
Explainability of Industrial Decision Support System using Digital Design Thinking with Scene2Model
To ensure the acceptance of decisions made in complex cyber-physical environments, orchestrated between human and machine actors, not only the developers need to understand how a decision is reached, but also the decision-makers and stakeholders affected by the decisions. To this end this contribution discusses how high-level visualisations can be derived to support the explanation of decisions using OMiLAB’s digital design thinking approach in an inverse manner.These visualisations will not be mere pictures, but diagrammatic models, containing additional information, which is understandable to machines, allowing to process them during an enrichment phase and interactively explain their involvement and impact to the users. The representation as conceptual models enables a) the cognitive perception by human actors, b) the machine interpretation for semantic lifting (focusing on elevating understandability) and c) further design iterations to adapt the system to become adequate and effective from a design but also operational perspective.
Christian Muck, Julia Tschuden, Herwig Zeiner, Wilfrid Utz
Open Access
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Conference Proceedings
From Simple to Sophisticated: Investigating the Spectrum of Decision Support Complexity with AI Integration in Manufacturing
In the evolving manufacturing landscape, the integration of Artificial Intelligence (AI) into Decision Support Systems (DSSs) has become crucial for enhancing decision-making. However, a visible challenge arises from the wide range of methodologies available, requiring a thoughtful choice of a suitable method for a given problem description. The absence of adequate resources for guiding developers in selecting an appropriate method is evident. In response to this gap, the presented work aims to improve the clarity and understanding of integrating existing methods, including AI, into DSSs. The clarity is achieved by introducing a structured grouping of DSSs based on the implemented methodology into four categories: rule-based, optimisation-based, simulation-based, and learning-based. Furthermore, this research illustrates decision-making with real-world examples by drawing insights from the literature. It underlines the user-centric importance in decision-making, emphasising that the effectiveness of the chosen DSS category depends on user interaction and comprehension. Looking ahead with the continuous evolution of AI, the ongoing incorporation of methodological advancements into DSSs is crucial for the continuous improvement of decision-making processes and alignment with the dynamic needs of users and the challenges present in modern manufacturing.
Sylwia Olbrych, Alexander Nasuta, Marco Kemmerling, Anas Abdelrazeq, Robert H Schmitt
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Conference Proceedings
Explainability as a means for transparency? Lay users' requirements towards transparent AI
With the rise of increasingly complex artificial intelligent systems (AI), their inner processes have become black boxes. The failure of some systems and the largely unregulated market of digital services have prompted governments and organs such as the EU to work on legislation for regulation. Their main requirement is that AI must be transparent for all stakeholders. While AI developers and experts have worked on interpretability and Explainability, social scientists emphasize that explainable AI is hardly understandable for lay users. The question arises as to whether the concept of Explainability can be used to create transparency for laypersons and what (additional) requirements these users might have towards transparent AI.To answer the questions, three fictitious AI apps were discussed in focus groups with n=26 participants. The apps differed in their domain and error significance to be able to identify system dependent requirements.The results indicate that lay users have different expectations and requirements for transparency in AI than technical experts: (a) previous experience with domain and system(s) strongly shape transparency demands, (b) background information beyond Explainability concepts is highly relevant for building trust, and (c) the system factor error-significance acts as a burning glass for transparency requirements.As a summary, the qualitative study shows that Explainability cannot serve as the only means of making systems transparent for lay users. Possible implications for system development are discussed. These implications apply in particular to AI that addresses lay users, i.e. non-computer experts.
Johanna M Werz, Esther Borowski, Ingrid Isenhardt
Open Access
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Conference Proceedings
Resilience Scores from Wearable Biosignal Sensors for Decision Support of Worker Allocation in Production
Mental health and well-being have to be considered on an equal footing when designing digitalized workplaces in production. We present the configuration of selected wearable sensor technologies together with the architecture of the Intelligent Sensor Box to enable monitoring resilience scores at the production site. The wearables include a Garmin vivosmart 5 fitness tracker to provide cardiovascular data, the greenTEG CORE body temperature sensor, Pupil Labs Neon eye tracking glasses and an optional sanSirro QUS smart shirt with textile biosignal measurements of vital parameters. We provide a framework to integrate a sequence of daily strain scores within a pre-determined time window of a preceding working period, and finally integrate this into a current resilience score. We present the estimation of the daily strain score based on the wearable sensing data that were captured in the Human Factors Lab in Austria during activities that are characteristic for the car production workplace. Furthermore, we demonstrate how resilience scores would impact the decision-making in the use case of daily dynamic worker allocation.
Lucas Paletta, Michael Schneeberger, Martin Pszeida, Jochen Mosbacher, Florian Haid, Julia Tschuden, Herwig Zeiner
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