How to Measure Stress in Smart and Intelligent Manufacturing Systems: A Systematic Review
Abstract
:1. Introduction
- What are the most appropriate stress measurements in smart and intelligent manufacturing systems?
- What are the other factors that may influence the stress evaluation?
2. Work-Related Stress
3. Smart and Intelligent Manufacturing Systems
4. Methodology
5. Abbreviation
6. Results of Stress-Evaluation Methods
6.1. Physical Measurements
6.1.1. Posture
- Peruzzini et al. [47] and Caterino et al. [49] discussed the complexity of the task and its impact on the worker’s posture through the Ovako working-posture analysis system (OWAS). This assessment is based on analysis of the position assumed by the main sections of the body (such as the back, legs, and arms) and also the weights the worker has to deal with during the task. The result of this assessment is a score associated with a specific colour depending on the risk level of the task analysed. An acceptable risk score is associated with the colour green; in this case, the task can be improved by reducing the postural load. A medium-risk level is associated with the colour orange, and some tasks modifications and improvements may be required. A high-risk level is associated with the colour red, and corrective actions on the tasks need to be taken urgently.
- The second indicator is called Rapid Entire Body Assessment (REBA) and was adopted by Peruzzini et al. [47] and Grandi et al. [48]. This indicator considers the position of the upper and lower limbs, trunk and wrists of the workers, the stability of their position, and the force required to perform the tasks. The result is a numerical score that can range from 1 to 15. Scores between 1 and 3 are associated with very low postural risk, scores between 4 and 7 scores indicate medium risk, for scores from 8 to 10 the risk is high, and scores over 11 are very high risk—in this case, urgent actions are required to improve and correct the tasks.
- The third indicator is the Rapid Upper Limb Assessment (RULA). It evaluates the postural risk and musculoskeletal problems in the upper body, taking into account the position of the worker’s legs, arms, trunk, wrists, and neck. The final score can vary from 1 to 7 and is directly proportional to the level of postural risk.
- The vector-magnitude units (VMU) indicator is calculated as the vectorial sum of the activity physically performed by the worker in the three orthogonal directions and is very useful in the analysis of the worker’s physical activity [46].
6.1.2. Behavioural Measurements
6.2. Physiological Measurements
6.2.1. Cardiac Activity
6.2.2. Electrodermal Activity (EDA)
6.2.3. Breathing Activity
6.2.4. Brain Activity
6.3. Psychological Measurements
6.3.1. Emotional State
6.3.2. Perceived Stress
7. Results of Other Factors
7.1. Experimental Protocols
7.2. Environmental Factors
7.3. Demographic Factors
8. Discussion
8.1. Objective Measurements
8.2. Subjective Measurement
8.3. Other Factors
9. Conclusions
10. Future Directions
Funding
Data Availability Statement
Conflicts of Interest
References
- Oztemel, E.; Gursev, S. Literature review of Industry 4.0 and related technologies. J. Intell. Manuf. 2020, 31, 127–182. [Google Scholar] [CrossRef]
- Wang, B.; Tao, F.; Fang, X.; Liu, C.; Liu, Y.; Freiheit, T. Smart Manufacturing and Intelligent Manufacturing: A Comparative Review. Engineering 2021, 7, 738–757. [Google Scholar] [CrossRef]
- Bongomin, O.; Yemane, A.; Kembabazi, B.; Malanda, C.; Mwape, M.C.; Mpofu, N.S.; Tigalana, D. Industry 4.0 Disruption and Its Neologisms in Major Industrial Sectors: A State of the Art. J. Eng. 2020, 2020, 8090521. [Google Scholar] [CrossRef]
- Breque, M.; De Nul, L.; Petridis, A. Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry; Publications Office of European Union: Luxembourg, 2021. [Google Scholar]
- Badri, A.; Boudreau-Trudel, B.; Souissi, A.S. Occupational health and safety in the industry 4.0 era: A cause for major concern? Saf. Sci. 2018, 109, 403–411. [Google Scholar] [CrossRef]
- Leso, V.; Fontana, L.; Iavicoli, I. The occupational health and safety dimension of Industry 4.0. Med. Lav. 2018, 110, 327–338. [Google Scholar] [PubMed]
- Wang, B.; Xue, Y.; Yan, J.; Yang, X.; Zhou, Y. Human-Centered Intelligent Manufacturing: Overview and Perspectives. Chin. J. Eng. Sci. 2020, 22, 139. [Google Scholar] [CrossRef]
- Arai, T.; Kato, R.; Fujita, M. Assessment of operator stress induced by robot collaboration in assembly. CIRP Ann. Manuf. Technol. 2010, 59, 5–8. [Google Scholar] [CrossRef]
- Zorzenon, R.; Lizarelli, F.L.; Daniel, D.B.A. What is the potential impact of industry 4.0 on health and safety at work? Saf. Sci. 2022, 153, 105802. [Google Scholar] [CrossRef]
- Wegner, D.M. Stress and mental control. In Handbook of Life Stress, Cognition and Health; Fisher, S., Reason, J., Eds.; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 1988; pp. 683–697. [Google Scholar]
- Cox, T.; Griffiths, A. Work-related stress: Nature and assessment. In IEE Colloquium on Stress and Mistake-Making in the Operational Workplace; IET: London, UK, 1995. [Google Scholar]
- Brunzini, A.; Peruzzini, M.; Grandi, F.; Khamaisi, R.K.; Pellicciari, M. A preliminary experimental study on the workers’ workload assessment to design industrial products and processes. Appl. Sci. 2021, 11, 12066. [Google Scholar] [CrossRef]
- Yeow, J.A.; Ng, P.K.; Tan, K.S.; Chin, T.S.; Lim, W.Y. Effects of stress, repetition, fatigue and work environment on human error in manufacturing industries. J. Appl. Sci. 2014, 14, 3464–3471. [Google Scholar] [CrossRef] [Green Version]
- Zizic, M.C.; Mladineo, M.; Gjeldum, N.; Celent, L. From Industry 4.0 towards Industry 5.0: A Review and Analysis of Paradigm Shift for the People, Organization and Technology. Energies 2022, 15, 5221. [Google Scholar] [CrossRef]
- European Commission, Directorate-General for Research and Innovation. Factories of the Future: Multi-Annual Roadmap for the Contractual PPP under Horizon 2020. 2013. Available online: https://data.europa.eu/doi/10.2777/29815 (accessed on 3 February 2023).
- Nguyen Ngoc, H.; Lasa, G.; Iriarte, I. Human-centred design in industry 4.0: Case study review and opportunities for future research. J. Intell. Manuf. 2022, 33, 35–76. [Google Scholar] [CrossRef] [PubMed]
- Ho, P.T.; Albajez, J.A.; Santolaria, J.; Yagüe-Fabra, J.A. Study of Augmented Reality Based Manufacturing for Further Integration of Quality Control 4.0: A Systematic Literature Review. Appl. Sci. 2022, 12, 1961. [Google Scholar] [CrossRef]
- Cárdenas-Robledo, L.A.; Hernández-Uribe, Ó.; Reta, C.; Cantoral-Ceballos, J.A. Extended reality applications in industry 4.0.-A systematic literature review. Telemat. Inform. 2022, 73, 101863. [Google Scholar] [CrossRef]
- Villani, V.; Gabbi, M.; Sabattini, L. Promoting operator’s wellbeing in Industry 5.0: Detecting mental and physical fatigue. In Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic, 9–12 October 2022; pp. 2030–2036. [Google Scholar]
- Sgarbossa, F.; Grosse, E.H.; Neumann, W.P.; Battini, D.; Glock, C.H. Human factors in production and logistics systems of the future. Annu. Rev. Control. 2020, 49, 295–305. [Google Scholar] [CrossRef]
- Reiman, A.; Kaivo-oja, J.; Parviainen, E.; Takala, E.P.; Lauraeus, T. Human factors and ergonomics in manufacturing in the industry 4.0 context—A scoping review. Technol. Soc. 2021, 65, 101572. [Google Scholar] [CrossRef]
- Stefana, E.; Marciano, F.; Rossi, D.; Cocca, P.; Tomasoni, G. Wearable Devices for Ergonomics: A Systematic Literature Review. Sensors 2021, 21, 777. [Google Scholar] [CrossRef]
- Argyle, E.M.; Marinescu, A.; Wilson, M.L.; Lawson, G.; Sharples, S. Physiological indicators of task demand, fatigue, and cognition in future digital manufacturing environments. Int. J. Hum. Comput. Stud. 2021, 145, 102522. [Google Scholar] [CrossRef]
- Digiesi, S.; Manghisi, V.M.; Facchini, F.; Klose, E.M.; Foglia, M.M.; Mummolo, C. Heart rate variability based assessment of cognitive workload in smart operators. Manag. Prod. Eng. Rev. 2020, 11, 56–64. [Google Scholar]
- Lesage, F.X.; Berjot, S.; Deschamps, F. Psychometric properties of the French versions of the perceived stress scale. Int. J. Occup. Med. Environ. Health 2012, 25, 178–184. [Google Scholar] [CrossRef]
- Widyanti, A.; Johnson, A.; de Waard, D. Adaptation of the rating scale mental effort (RSME) for use in Indonesia. Int. J. Ind. Ergon. 2013, 43, 70–76. [Google Scholar] [CrossRef]
- Leone, A.; Rescio, G.; Siciliano, P.; Papetti, A.; Brunzini, A.; Germani, M. Multi sensors platform for stress monitoring of workers in smart manufacturing context. In Proceedings of the 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia, 25–28 May 2020; pp. 1–5. [Google Scholar]
- Han, L.; Zhang, Q.; Chen, X.; Zhan, Q.; Yang, T.; Zhao, Z. Detecting work-related stress with a wearable device. Comput. Ind. 2017, 90, 42–49. [Google Scholar] [CrossRef]
- Setz, C.; Arnrich, B.; Schumm, J.; La Marca, R.; Tröster, G.; Ehlert, U. Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans. Inf. Technol. Biomed. 2009, 14, 410–417. [Google Scholar] [CrossRef] [PubMed]
- Khamaisi, R.K.; Brunzini, A.; Grandi, F.; Peruzzini, M.; Pellicciari, M. UX assessment strategy to identify potential stressful conditions for workers. Robot. Comput. -Integr. Manuf. 2022, 78, 102403. [Google Scholar] [CrossRef]
- Stephen, P.; Cary, C.; Kate, T. A Model of Work Stress to underpin the Health and Safety Executive advice for tackling work-related stress and stress risk assessments. In Counseling at Work, Winter; Center for Stress Management: London, UK, 2004. [Google Scholar]
- Yahaya, A.; Yahaya, N.; Bon, A.T.; Ismail, S.; Ing, T.C. Stress level and its influencing factors among employees in a plastic manufacturing and the implication towards work performance. Elixir Psychol. 2011, 41, 5932–5941. [Google Scholar]
- Lin, D.Y.; Hwang, S.L. The development of mental workload measurement in flexible manufacturing systems. Hum. Factors Ergon. Manuf. Serv. Ind. 1998, 8, 41–62. [Google Scholar] [CrossRef]
- Soltanpour Gharibdousti, M.; Azadeh, A. Performance Evaluation of Organizations Based on Human Factor Engineering Using Fuzzy Data Envelopment Analysis (FDEA). J. Soft Comput. Civ. Eng. 2019, 3, 63–90. [Google Scholar]
- Hassard, J.; Teoh, K.R.; Visockaite, G.; Dewe, P.; Cox, T. The cost of work-related stress to society: A systematic review. J. Occup. Health Psychol. 2018, 23, 1–17. [Google Scholar] [CrossRef] [Green Version]
- Zhou, L.; Jiang, Z.; Geng, N.; Niu, Y.; Cui, F.; Liu, K.; Qi, N. Production and operations management for intelligent manufacturing: A systematic literature review. Int. J. Prod. Res. 2022, 60, 808–846. [Google Scholar] [CrossRef]
- Shojaeinasab, A.; Charter, T.; Jalayer, M.; Khadivi, M.; Ogunfowora, O.; Raiyani, N.; Najjaran, H. Intelligent manufacturing execution systems: A systematic review. J. Manuf. Syst. 2022, 62, 503–522. [Google Scholar] [CrossRef]
- Wang, B.; Zheng, P.; Yin, Y.; Shih, A.; Wang, L. Toward human-centric smart manufacturing: A human-cyber-physical systems (HCPS) perspective. J. Manuf. Syst. 2022, 63, 471–490. [Google Scholar] [CrossRef]
- Sarkar, B.; Dey, B.K.; Sarkar, M.; Kim, S.J. A smart production system with an autonomation technology and dual channel retailing. Comput. Ind. Eng. 2022, 173, 108607. [Google Scholar] [CrossRef]
- Dey, B.K.; Seok, H. Intelligent inventory management with autonomation and service strategy. J. Intell. Manuf. 2022, 1–24. [Google Scholar] [CrossRef] [PubMed]
- Mayrhofer, W.; Rupprecht, P.; Schlund, S. One-fits-all vs. tailor-made: User-centered workstations for field assembly with an application in aircraft parts manufacturing. Procedia Manuf. 2019, 39, 149–157. [Google Scholar] [CrossRef]
- Sibona, F.; Cheng, P.D.C.; Indri, M.; Di Prima, D. PoinTap system: A human-robot interface to enable remotely controlled tasks. In Proceedings of the 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vasteras, Sweden, 7–10 September 2021; pp. 1–8. [Google Scholar]
- Yen, G.G.; Acay, D. Adaptive user interfaces in complex supervisory tasks. ISA Trans. 2009, 48, 196–205. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Torres-Carrion, P.V.; Gonzalez-Gonzalez, C.S.; Aciar, S.; Rodriguez-Morales, G. Methodology for systematic literature review applied to engineering and education. In Proceedings of the 2018 IEEE Global Engineering Education Conference, EDUCON, Santa Cruz de Tenerife, Spain, 17–20 April 2018. [Google Scholar]
- Peruzzini, M.; Grandi, F.; Pellicciari, M. How to analyse the workers’ experience in integrated product-process design. J. Ind. Inf. Integr. 2018, 12, 31–46. [Google Scholar] [CrossRef]
- Peruzzini, M.; Grandi, F.; Pellicciari, M. Exploring the potential of Operator 4.0 interface and monitoring. Comput. Ind. Eng. 2020, 139, 105600. [Google Scholar] [CrossRef]
- Grandi, F.; Peruzzini, M.; Cavallaro, S.; Prati, E.; Pellicciari, M. Creation of a UX index to design human tasks and workstations. Int. J. Comput. Integr. Manuf. 2022, 35, 4–20. [Google Scholar] [CrossRef]
- Caterino, M.; Rinaldi, M.; Fera, M. Digital ergonomics: An evaluation framework for the ergonomic risk assessment of heterogeneous workers. Int. J. Comput. Integr. Manuf. 2023, 36, 239–259. [Google Scholar] [CrossRef]
- Lagomarsino, M.; Lorenzini, M.; De Momi, E.; Ajoudani, A. An Online Framework for Cognitive Load Assessment in Industrial Tasks. Robot. Comput. Integr. Manuf. 2022, 78, 102380. [Google Scholar] [CrossRef]
- Rao Pabolu, V.K.; Shrivastava, D.; Kulkarni, M.S. A Dynamic System to Predict an Assembly Line Worker’s Comfortable Work-Duration Time by Using the Machine Learning Technique. Procedia CIRP 2022, 106, 270–275. [Google Scholar] [CrossRef]
- Cavallo, D.; Facchini, F.; Mossa, G. Information-based processing time affected by human age: An objective parameters-based model. IFAC-PapersOnLine 2021, 54, 7–12. [Google Scholar] [CrossRef]
- Papetti, A.; Rossi, M.; Menghi, R.; Germani, M. Human-centered design for improving the workplace in the footwear sector. Procedia CIRP 2020, 91, 295–300. [Google Scholar] [CrossRef]
- Bettoni, A.; Montini, E.; Righi, M.; Villani, V.; Tsvetanov, R.; Borgia, S.; Secchi, C.; Carpanzano, E. Mutualistic and adaptive human-machine collaboration based on machine learning in an injection moulding manufacturing line. Procedia CIRP 2020, 93, 395–400. [Google Scholar] [CrossRef]
- Ciccarelli, M.; Papetti, A.; Germani, M.; Leone, A.; Rescio, G. Human work sustainability tool. J. Manuf. Syst. 2022, 62, 76–86. [Google Scholar] [CrossRef]
- Gervasi, R.; Aliev, K.; Mastrogiacomo, L.; Franceschini, F. User Experience and Physiological Response in Human-Robot Collaboration: A Preliminary Investigation. J. Intell. Robot. Syst. Theory Appl. 2022, 106, 36. [Google Scholar] [CrossRef]
- Eyam, A.T.; Mohammed, W.M.; Martinez Lastra, J.L. Emotion-Driven Analysis and Control of Human-Robot Interactions in Collaborative Applications. Sensors 2021, 21, 4626. [Google Scholar] [CrossRef]
- Petrovic, M.; Vukicevic, A.M.; Djapan, M.; Peulic, A.; Jovicic, M.; Mijailovic, N.; Milovanovic, P.; Grajic, M.; Savkovic, M.; Caiazzo, C.; et al. Experimental Analysis of Handcart Pushing and Pulling Safety in an Industrial Environment by Using IoT Force and EMG Sensors: Relationship with Operators’ Psychological Status and Pain Syndromes. Sensors 2022, 22, 7467. [Google Scholar] [CrossRef]
- Arkouli, Z.; Michalos, G.; Makris, S. On the Selection of Ergonomics Evaluation Methods for Human Centric Manufacturing Tasks. Procedia CIRP 2022, 107, 89–94. [Google Scholar] [CrossRef]
- Morton, J.; Zheleva, A.; Van Acker, B.B.; Durnez, W.; Vanneste, P.; Larmuseau, C.; De Bruyne, J.; Raes, A.; Cornillie, F.; Saldien, J.; et al. Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context. Appl. Ergon. 2022, 102, 103763. [Google Scholar] [CrossRef] [PubMed]
- Gualtieri, L.; Fraboni, F.; De Marchi, M.; Rauch, E. Development and evaluation of design guidelines for cognitive ergonomics in human-robot collaborative assembly systems. Appl. Ergon. 2022, 104, 103807. [Google Scholar] [CrossRef] [PubMed]
- Vijayakumar, V.; Sgarbossa, F.; Neumann, W.P.; Sobhani, A. Framework for incorporating human factors into production and logistics systems. Int. J. Prod. Res. 2022, 60, 402–419. [Google Scholar] [CrossRef]
- Kopp, V.; Holl, M.; Schalk, M.; Daub, U.; Bances, E.; García, B.; Schneider, U. Exoworkathlon: A prospective study approach for the evaluation of industrial exoskeletons. Wearable Technol. 2022, 3, e22. [Google Scholar] [CrossRef]
- Mailliez, M.; Hosseini, S.; Battaiä, O.; Roy, R.N. Decision Support System-like Task to Investigate Operators’ Performance in Manufacturing Environments. IFAC-PapersOnLine 2020, 53, 324–329. [Google Scholar] [CrossRef]
- Panchetti, T.; Pietrantoni, L.; Puzzo, G.; Gualtieri, L.; Fraboni, F. Assessing the Relationship between Cognitive Workload, Workstation Design, User Acceptance and Trust in Collaborative Robots. Appl. Sci. 2023, 13, 1720. [Google Scholar] [CrossRef]
- Vithanawasam, T.M.W.; Madhusanka, B.G.D.A. Face and upper-body emotion recognition using service robot’s eyes in a domestic environment. In Proceedings of the 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka, 28 March 2019. [Google Scholar]
- Castaldo, R.; Melillo, P.; Bracale, U.; Caserta, M.; Triassi, M.; Pecchia, L. Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis. Biomed. Signal Process. Control. 2015, 18, 370–377. [Google Scholar] [CrossRef] [Green Version]
- Nicolò, A.; Massaroni, C.; Schena, E.; Sacchetti, M. The importance of respiratory rate monitoring: From healthcare to sport and exercise. Sensors 2020, 20, 6396. [Google Scholar] [CrossRef]
- Corlett, E.N.; Bishop, R.P.A. A technique for measuring postural discomfort. Ergonomics 1976, 9, 175–182. [Google Scholar] [CrossRef]
- Destouet, C.; Tlahig, H.; Bettayeb, B.; Mazari, B. Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement. J. Manuf. Syst. 2023, 67, 155–173. [Google Scholar] [CrossRef]
- Balasubramanian, V.; Narendran, T.T.; Sai Praveen, V. RBG risk scale: An integrated tool for ergonomic risk assessments. Int. J. Ind. Syst. Eng. 2011, 8, 104–116. [Google Scholar] [CrossRef]
- Di Pasquale, V.; Miranda, S.; Neumann, W.P. Ageing and human-system errors in manufacturing: A scoping review. Int. J. Prod. Res. 2020, 58, 4716–4740. [Google Scholar] [CrossRef]
- Kaklauskas, A.; Abraham, A.; Ubarte, I.; Kliukas, R.; Luksaite, V.; Binkyte-Veliene, A.; Vetloviene, I.; Kaklauskiene, L. A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States. Sensors 2022, 22, 7824. [Google Scholar] [CrossRef] [PubMed]
- Abd Elgawad, Y.Z.; Youssef, M.I.; Nasser, T.M. New methodology to detect the effects of emotions on different biometrics in real time. Int. J. Electr. Comput. Eng. 2023, 13, 1358. [Google Scholar] [CrossRef]
- Mansi, S.A.; Cosoli, G.; Pisello, A.L.; Pigliautile, I.; Revel, G.M.; Arnesano, M. Thermal discomfort in the workplace: Measurement through the combined use of wearable sensors and machine learning algorithms. In Proceedings of the IEEE International Workshop on Metrology for Industry 4.0 & IoT, Trento, Italy, 7–9 June 2022. [Google Scholar]
- Abbasi, A.M.; Motamedzade, M.; Aliabadi, M.; Golmohammadi, R.; Tapak, L. Combined effects of noise and air temperature on human neurophysiological responses in a simulated indoor environment. Appl. Ergon. 2020, 88, 103189. [Google Scholar] [CrossRef]
- Martins Gnecco, V.; Pigliautile, I.; Pisello, A.L. Long-Term Thermal Comfort Monitoring via Wearable Sensing Techniques: Correlation between Environmental Metrics and Subjective Perception. Sensors 2023, 23, 576. [Google Scholar] [CrossRef]
- Ahmad, A.; Darmoul, S.; Dabwan, A.; Alkahtani, M.; Samman, S. Human error in multitasking environments. In Proceedings of the 6th International Conference on Industrial Engineering and Operations Management (IEOM 2016), Kuala Lumpur, Malaysia, 8–10 March 2016. [Google Scholar]
- Mura, M.D.; Dini, G. Improving ergonomics in mixed-model assembly lines balancing noise exposure and energy expenditure. CIRP J. Manuf. Sci. Technol. 2023, 40, 44–52. [Google Scholar] [CrossRef]
Identification-Phase Criteria | Value |
---|---|
Subject area | Engineering |
Document type | Article, conference paper |
Source type | Journal, conference proceeding |
Publication stage | Final |
Language | English |
Abbreviation | Description | Abbreviation | Description |
---|---|---|---|
BDP | Body Part Discomfort Scale | OCRA | Occupational repetitive action |
CLAM | Cognitive Load Assessment for Manufacturing | OWAS | Ovako working-posture analysis system |
DASS | Depression Anxiety Stress Scales | PSS | Perceived Stress Scale |
ECG | Electrocardiography | REBA | Rapid Entire Body Assessment |
EDA | Electrodermal activity | RULA | Rapid Upper Limb Assessment |
HR | Heart rate | RSME | Rating Scale Mental Effort |
HRV | Heart-rate variability | SSSQ | Short Stress State Questionnaire |
ISA | Instantaneous Self-Assessment | SC | Skin conductance |
MCH | Modified Cooper–Harper Scale | STAI | State–Trait Anxiety Inventory |
NASA-TLX | National Aeronautics Space Administration–Task Load Index | SWAT | Subjective workload-assessment technique |
NAS | Numeric Analog Scale | VMU | Vector-magnitude units |
WP | Workload profile |
Country | First Author’s Affiliation | Number of Studies |
---|---|---|
Belgium | imec-mict-UGent, Gent | 1 |
Finland | Tampere University, Tampere | 1 |
France | Université de Toulouse, Toulouse | 1 |
Germany | Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart | 1 |
Greece | University of Patras, Rion Patras | 1 |
Italy | Università Politecnica delle Marche, Ancona | 3 |
Polytechnic University of Bari, Bari | 1 | |
Politecnico di Torino, Turin | 1 | |
University of Modena and Reggio Emilia, Modena | 4 | |
University of Bozen-Bolzano, Bolzano | 1 | |
University of Campania | 1 | |
University of Bologna | 1 | |
Istituto Italiano di Tecnologia, Genoa | 1 | |
India | Shiv Nadar University, Uttar Pradesh | 1 |
Norway | Norwegian University of Science and Technology, Trondheim | 1 |
Serbia | University of Belgrade, Belgrade | 1 |
Switzerland | University of Applied Sciences and Arts of Southern Switzerland, Manno | 1 |
Measurement | References | ||
---|---|---|---|
Type | Category | Object | |
Objective | Physical | Posture | [46,47,48,49] |
Behaviour | [50,51] | ||
Physiological | Cardiac activity | [12,30,46,47,48,52,53,54,55] | |
Electrodermal activity | [30,53,55,56] | ||
Breathing activity | [46,47] | ||
Brain activity | [57] | ||
Subjective | Psychological | Emotional state | [12,32,34,35,58] |
Perceived stress | [47,59,60,61,62,63,64,65] |
Postural Measurements | Indicators | References |
---|---|---|
Entire Body Assessment | OWAS | [47,49] |
Entire Body Assessment | REBA | [47,48] |
Upper Limb Assessment | RULA | [47] |
Entire Body Assessment | VMU |
Behavioural Measurement | Indicators | References |
---|---|---|
Body language | S is equal to the number of occurrences recorded in a 1 min interval from time t-60 to time t; t indicates the time in seconds | [50] |
Body motion | Assembly-line speed | [51] |
Hyperactivity | [50] |
Physiological Measurement | Indicators | References |
---|---|---|
Cardiac activity | HR, HRV | [46,47,53,54,55] |
HRuser is the mean value of the specific user’s HR as recorded during the task simulation; HRbaseline is the mean HR value as recorded during the user’s baseline phase; HRmax is the maximum HR value as recorded for each user during the entire test | [48] | |
RR baseline refers to the RR value during a resting phase of the activity participant; RR mean and RR max refer to values calculated under stress | [12] | |
[30] | ||
RMSSD | [56] | |
VO2 corresponds to individual oxygen consumption; VO2max corresponds to the maximum individual oxygen consumption; HR is the heart rate; RHR is the resting heart rate | [52] |
Neurophysiological Measurement | Indicators | References |
---|---|---|
Brain activity | High-beta frequency band (23 to 38 Hz) | [57] |
Psychological Measurements | Indicators | References |
---|---|---|
Emotional state | Trait and state anxiety | [55] |
Depression, anxiety, and tension/stress | [58] | |
Interest, excitement, focus, and relaxation alterations | [57] | |
Pleasure–arousal–dominance of the affective state of the participants | [56] | |
Perceived cognitive and emotional conditions | [12] | |
Perceived stress | Mental stress | [64] |
Time load, mental load, and psychological-stress load | [59,62] | |
Stress level | [60,61,65] | |
Body-area stress | [63] | |
Perceived comfort | [47] |
Reference | Indicators | Experimental Task | Experimental Environment | ||
---|---|---|---|---|---|
Physiological | Physical | Psychological | |||
[54] | HRV | Plastic-injection assembly line | Injection-moulding manufacturing line | ||
[12] | RR | Perceived cognitive and emotional conditions | Engine oil-filter replacement sequence | Laboratory | |
[52] | Cognitive task Motor task | Laboratory | |||
[57] | EEG | Interest, excitement, focus, and relaxation alterations | Assembling a wooden box alone/with the cobot | Laboratory | |
[56] | SCR, RMSSD | Pleasure–arousal–dominance of the affective state of the participants | Human–robot collaboration for an assembly task | Laboratory | |
[48] | HA | REBA | Tractor assembly | Virtual simulation | |
[61] | Stress level | Pneumatic cylinder assembly with collaborative robot. | Laboratory | ||
[30] | ΔEDA, ΔRR | Oil- and gas-pipes manufacturing sequence | Virtual-reality simulation | ||
[63] | Body-area stress | Assembly and disassembly | Automotive plant | ||
[50] | Self-touching, Hyperactivity | Assembly | Laboratory | ||
[60] | Stress level | Assembly and cognitive | Laboratory | ||
[53] | HRV, EDA | Assembly | Real manufacturing environment | ||
[46] | HR, HRV, BR Skin temperature | Body activity | Motor task | Mixed reality: virtual model and real items in a laboratory context | |
[47] | BR | OWAS, REBA, RULA, VMU | Perceived comfort | Air-cabin filter assembly | Virtual-reality simulation |
[58] | Depression, Anxiety, and tension/stress | Pushing and pulling | Industrial environment | ||
[65] | SSSQ | Perceived stress | Assembly task with collaborative robots | Laboratory | |
[49] | OWAS | Stress | Manual assembly | Automotive Italian company |
Reference | Environmental Temperature | Environmental Noise | Environmental Light | Environmental Air Quality |
---|---|---|---|---|
[12] | Constant temperature at 24 °C | Silence (no sources of noise) | Totally artificial light sources (neon lamps positioned on the ceiling) | |
[30] | Temperature is variable with the external one. | Around 85–135 dB | Artificial light | High presence of dust |
[50] | Constant | Constant lighting conditions | ||
[60] | Ambient factory-floor sounds were played | |||
[53] | Temperature, based on operators’ clothing and activity | Noise exposure evaluated by a sound-level meter and according to the Daily Personal Noise Exposure Level (LEP, d) established by the Directive 2003/10/EC | A colorimeter is used to measure the colour temperature and lux. | Global pollution index (GPI), a weighted compound of the different pollutants measured |
Reference | Gender | Age | Expertise/Experience/Background |
---|---|---|---|
[54] | 1 female, 3 males | ||
[12] | 1 female, 7 males | Mean age: 25.6 years | |
[52] | Males and females | Different ages for each task | |
[56] | 42 participants (71.4% males, 28.6% females) | Mean age: 28.24 ± 8.1 years | ● 28.6% of participants had never interacted with a cobot; ● 45.2% of participants had never interacted with a cobot but knew of them; ● 16.7% of participants had already interacted with a cobot; ● 9.5% of participants had already programmed and interacted with a cobot. |
[48] | Five users with different levels of expertise | ||
[61] | 12 males, 2 females | The age of participants ranged from 23 to 57 years old | -No previous experience with collaborative robots and minimal experience in manufacturing operations. Different backgrounds: researchers, master student, technicians/administrative |
[63] | Young experts | ||
[50] | Model calibration experiment: 5 males; Multi-subject cognitive-load-assessment experiments: 5 males, 5 females | Model-calibration experiment: (Mean age: 27.6 ± 2.0 years) Multi-subject cognitive-load-assessment experiments: (Mean age: 26.6 ± 3.7 years) | No previous expertise or experience |
[60] | 25 females, 21 males | 46 participants between 19 and 40 years old Mean age: 25.8 ± 4.19 years | Variability in participants’ educational background (from secondary education to PhD) |
[53] | 2 males | 31 and 28 years old | |
[58] | 20 males | Mean age: 35.0 ± 5.8 years | Experienced and non-experienced in industrial physical tasks |
[65] | 11 males, 3 females | Mean age: 31.6 ± 4 years |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Blandino, G. How to Measure Stress in Smart and Intelligent Manufacturing Systems: A Systematic Review. Systems 2023, 11, 167. https://doi.org/10.3390/systems11040167
Blandino G. How to Measure Stress in Smart and Intelligent Manufacturing Systems: A Systematic Review. Systems. 2023; 11(4):167. https://doi.org/10.3390/systems11040167
Chicago/Turabian StyleBlandino, Graziana. 2023. "How to Measure Stress in Smart and Intelligent Manufacturing Systems: A Systematic Review" Systems 11, no. 4: 167. https://doi.org/10.3390/systems11040167
APA StyleBlandino, G. (2023). How to Measure Stress in Smart and Intelligent Manufacturing Systems: A Systematic Review. Systems, 11(4), 167. https://doi.org/10.3390/systems11040167