Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Vânia Figueira
 -- 2623 2024-02-28 21:38:06 |
2 Reference format revised.
Lindsay Dong
 Meta information modification 2623 2024-02-29 09:00:36 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Figueira, V.; Silva, S.; Costa, I.; Campos, B.; Salgado, J.; Pinho, L.; Freitas, M.; Carvalho, P.; Marques, J.; Pinho, F. Wearables for Monitoring and Postural Feedback. Encyclopedia. Available online: https://encyclopedia.pub/entry/55691 (accessed on 02 May 2024).
Figueira V, Silva S, Costa I, Campos B, Salgado J, Pinho L, et al. Wearables for Monitoring and Postural Feedback. Encyclopedia. Available at: https://encyclopedia.pub/entry/55691. Accessed May 02, 2024.
Figueira, Vânia, Sandra Silva, Inês Costa, Bruna Campos, João Salgado, Liliana Pinho, Marta Freitas, Paulo Carvalho, João Marques, Francisco Pinho. "Wearables for Monitoring and Postural Feedback" Encyclopedia, https://encyclopedia.pub/entry/55691 (accessed May 02, 2024).
Figueira, V., Silva, S., Costa, I., Campos, B., Salgado, J., Pinho, L., Freitas, M., Carvalho, P., Marques, J., & Pinho, F. (2024, February 28). Wearables for Monitoring and Postural Feedback. In Encyclopedia. https://encyclopedia.pub/entry/55691
Figueira, Vânia, et al. "Wearables for Monitoring and Postural Feedback." Encyclopedia. Web. 28 February, 2024.
Wearables for Monitoring and Postural Feedback
Edit
This entry is adapted from the peer-reviewed paper 10.3390/s24041341

Wearables offer a promising solution for simultaneous posture monitoring and/or corrective feedback. Some wearables have the feature to provide real-time corrective sensory feedback when adopting inadequate postures. This feedback can be auditory (typically conveyed through diverse auditory channels), visual (usually displayed by screens or projectors), haptic (application of vibratory stimulus), or a combination of these, providing information based on performance or outcome.

wearables feedback posture work-related musculoskeletal disorders workstation

1. Introduction

Currently, work-related musculoskeletal disorders (WRMSDs) are the most prevalent occupational health problem worldwide [1], and they are also the most common in the European Union (EU) [2][3], affecting three out of five workers [4]. Indeed, it is considered a public health problem [5] with multifactorial causes [6][7], resulting from the complex interaction between individual, biomechanical, organisational and psychosocial risk factors [6][8].
WRMSDs affect individuals in all aspects of their lives—as well as companies, society and the economy [2][9][10]—as they result in higher healthcare costs, reduced productivity, increased absenteeism, lower job satisfaction, and reduced physical, psychological, and social well-being of workers [7][11]. In fact, some disorders have a lasting impact on the worker’s life, limiting them in their daily activities and preventing their return to work due to permanent disability caused by pain and decreased functionality [5].
Recently, an increase in WRMSDs has been observed due mainly to mechanical overload [1]. Prolonged inappropriate working postures, tasks with high physical demands, repetitive and meticulous work gestures, and an intense work pace with few breaks have been reported as the main causes of WRMSDs [3][8][9]. Additionally, the demographic change in the workforce [12] has led to an increase in the number of older workers, which may have also contributed to the increase in WRMSDs [1][2].
Given the musculoskeletal system overload [1], real-time monitoring and postural correction in the workplace [13] are urgent. This will minimise the harmful effects of some postures, resulting from the misalignment of body segments with the line of gravity [14]. Posture is a highly complex, variable, and dynamic system that can respond to minimal psychophysical and socioenvironmental perturbations [15]. Due to these features, wearable technology has emerged as a viable alternative with high potential for real-world context implementation [16][17][18]. The projection of wearables, also referred to in the literature as wearable sensors and defined as electronic devices integrated into clothing and/or other accessories that comfortably adapt to the human body [19] would seem to be a valid proposal for improving working conditions, early identification of WRMSDs risks, increased work efficiency, and promoting well-being [20].
Inertial measurement unit (IMU) is the common underlying technology for most wearables [7][21][22]. It is possible to identify static and dynamic improper postures that are maintained for long periods [23][24] through discrete and continuous monitoring of body posture in real-world settings [21][25] in a shorter timeframe/in a shorter time [5]. Other advantages of wearables that make them robust for integration in monitoring work activity include objective, reliable, and accurate results [26] which provide a trustworthy and realistic assessment of work-related conditions [27]; low cost; lightweight design; small size; portability; and energy efficiency [28].
Furthermore, some wearables have the feature to provide real-time corrective sensory feedback when adopting inadequate postures [3]. This feedback can be auditory (typically conveyed through diverse auditory channels), visual (usually displayed by screens or projectors), haptic (application of vibratory stimulus), or a combination of these, providing information based on performance or outcome [29][30]. Haptic feedback stands out as the most common choice [9][31][32] and also as the most advantageous option given its discreet nature [29]. It contrasts with auditory and visual feedback, which can be perceived by other workers, affecting their concentration [9]. Regarding the feedback signal, it can be provided at the end of the task, referred to as terminal feedback, or in real-time, known as concurrent feedback [29]. The latter has the advantage of promoting immediate changes in work postures [33]. However, regardless of the type of corrective feedback, it promotes greater postural self-awareness, allowing for the minimisation of inadequate postures and, consequently, reducing the musculoskeletal overload [13]. This factor emphasises the potential for the urgent implementation of wearables in different work contexts to reduce the incidence of WRMSDs and the associated healthcare costs [9][32]. Nevertheless, still in their early stages, wearables present disadvantages and/or challenges such as accuracy, technical functionality, and usability [30]. Factors such as battery life, long-term comfort, preparation time (donning and doffing; changing or recharging batteries) and the stability of wireless communication contribute to workers’ reluctance to use such devices [2][30][34][35]. Recent studies have tried to address the challenges of wearables by improving the usability and effective monitorization of human movement, as well as the autonomy of wearables, which are crucial for practicality and commercial viability, with the aim of establishing wearables as common tools in workplace settings [36]. The potential of wearables in preventing and minimising WRMSDs is supported by growing scientific evidence on postural monitoring wearables in various contexts [20][36][37][38][39]. Despite advances, challenges remain in wearables for simultaneous postural monitoring and feedback in real-world scenarios, emphasising the need for continued research. Furthermore, the importance of wearables in minimising WRMSDs positions them as an emerging field of research. It is important to understand and summarise all the current evidence to contribute to the development of new methods to promote the worker’s health and quality of life.

2. Wearables for Monitoring and Postural Feedback

2.1. Wearables and Variables of Human Movement in the Workplace Settings

Certainly, it is important to highlight that the monitorisation and correction of posture in real environments, which is currently feasible, is only possible due to the exponential development of technology that allows the use of minimalist portable sensors (in particular inertial sensors) which are commonly used in biomechanical studies and integrated into wearables devices [28]. The gold standard for kinematic assessment, particularly joint range assessment, which includes digital goniometers that are easy to access and use, as well as optical motion capture systems, have some disadvantages when it comes to being integrated into a workplace context. As far as goniometers are concerned, they are usually used more for kinematic measurements of the wrist, especially twin-axis electrical goniometers placed on the targeted body segment [40]. Their accuracy depends on the expertise of the user, while optical capture systems are more limited to laboratory settings [41]. Therefore, the use of IMUs seems to be more consistent with greater potential for application in a workplace context, providing more reliable and trustworthy results [2]. It is widely recognised that real tasks, as opposed to simulated tasks, appear to involve a greater variability and complexity of movement that is difficult to replicate in controlled environments [42]. In this sense, wearables have emerged as a viable solution to address these gaps due to their ability to provide objective measurements of human movement, particularly posture [22].

Furthermore, Buisseret et al. [25] stated that wearables add significant value to the analysis of human movement in real and dynamic situations, which justifies their application in posture monitoring and correction. Paloschi et al. [21] argued that wearables are valid for quantitative measurement of daily occupational posture, with inertial measurement units (9 axis IMUs) being a robust instrument commonly used for this purpose. This statement is supported by Cerqueira et al. [5], who noted that IMUs are an increasingly valid and recognised option for wearable integration due to their many advantages, including three-dimensional motion capture, size and weight, and portability. Choi et al. [43] also argue that IMUs are important tools for managing the health and safety of workers, allowing continuous monitorisation and identification of incorrect postures, with feedback provided when a risky posture is detected. Based on this evidence, it seems reasonable to integrate IMUs into posture monitoring and feedback wearables in the workplace context. It is worth noting that IMUs, which allow for a more comprehensive assessment [7], are the most used type of sensor according to Ciccarelli et al. [2]; Conforti et al. [14]; Donisi et al. [44], and Patel et al. [18].

2.2. Analysis and Feedback in the Workplace: Type, Location, Attachment, and Quantity of Sensors

Photogrammetric methods, which are the gold standard for posture analysis, consist of approaches that are more geared towards laboratory use and are not suitable for monitoring and correcting workers’ postures in their daily lives [22]. On the other hand, indirect methods such as questionnaires or observational methods like REBA or RULA are, according to the available literature, the most used in the workplace context to assess postures and related factors [45][46]. Consequently, the need to develop alternative methods for real-time objective assessment of work tasks and the subsequent adoption of appropriate postures was created [46]. However, based on existing evidence, it is known that workers are often unaware of their posture and frequently adopt incorrect behaviours due to time constraints, task demands, and the need to meet productivity goals [22]. Truly, there is a lack of consensus on correct posture [13], which is generally described as considering the alignment of different body segments about the line of gravity with minimal energy expenditure [13][47].
Most sensors used in the included studies were at the prototype stage despite the growing trend towards commercialisation of wearable devices [22]. In fact, although there is already a wide range of commercially available wearable devices today, most are aimed at physical activity monitoring, posture and physiological parameters, in contrast to the apparent lack of commercial wearable systems that combine postural monitoring and feedback [22]. This seems to be a gap for their integration in the workplace context [2]. This may be due to difficulties not only in accessing such devices but also to the fact that, in most cases, they do not meet usability parameters, which can consequently hinder the acceptance of these instruments by workers and companies, and actually provide data incongruent with reality [2][34]. 
Despite the device placement, also the usability, the comfort and accuracy are key factors in worker acceptance, and current evidence reinforces its importance [5][7][48][49]. Indeed, the sensors attachment is a fundamental role in its usability. Therefore, to meet this requirement, the sensor attachment must fulfil three essential conditions, namely being imperceptible to the worker; having intuitive use; and providing quick, reliable, and easily interpretable information [21]. These criteria are crucial to ensure the effectiveness of wearables, alongside worker acceptance, and can be integrated into accessories or clothing [25]. 
Other sensor attachment methods can include direct placement on the skin using electrodes that incorporate EMG [50], adhesive tape or elastic straps [33][46], integration into smartwatches, or even integration into personal protective equipment (PPE) such as helmets and visors [51][52][53]. The latter form of attachment is consistent with Yang et al.’s [54] study on construction workers, who used sensors attached to PPE, specifically helmets and safety harnesses or vests. In fact, Choi et al. [43] argue that this is the most suitable location for sensor placement in this occupational activity, as it does not appear to interfere with the work, which can increase usability.

2.3. Occupation and Work Tasks

Healthcare professionals such as nurses, dentists, healthcare assistants, and unspecified healthcare professionals were the most studied populations on posture monitorisation and feedback using wearables. Indeed, healthcare professions typically involve demanding work schedules and precise, meticulous, and repetitive tasks, often requiring prolonged static postures that contribute to neuro-musculoskeletal strain and fatigue, particularly among dentists [55][56][57]. This fact is also supported by studies that have found a high prevalence of musculoskeletal disorders in this group [58][59][60], particularly among those with more than 10 years’ clinical practice and working more than 40 h per week [60]. Jacquier-Bret and Gorce [58] also found that dentists are among the professionals most exposed to musculoskeletal disorders, with a higher prevalence of symptoms in the spine, particularly in the cervical and lumbar regions, which is also supported by Blume et al. [59]. In addition, dental students are also susceptible to develop musculoskeletal disorders, and there are studies that support this evidence by linking this susceptibility to the adoption of more sedentary behaviours due to the use of new technologies that decrease their mobility [60][61]. On the other hand, Blume et al. [59] describe an association between musculoskeletal disorders and poor posture adopted by these students during dental procedures, particularly when these involve static postures, especially in the cervical region, due to the limited visibility of the patient’s mouth, which leads to the maintenance of an extended and protracted cervical posture.
Hence, there is an urgent need to bring visibility to wearables that incorporate corrective feedback, as they can aware changes in the daily work routines and, consequently, in the quality of life and work of healthcare professionals. This could result in fewer injuries, lower absenteeism rates, reduced healthcare costs, and increased productivity [55].
In addition to healthcare professionals, there are other fields, particularly in the construction industry, who have a high prevalence of WRMSDs due to daily exposure to excessive effort, sustained and incorrect postures, handling and carrying heavy loads, repetitive tasks, and vibration from work tools [13][14][57]. Based on scientific evidence, it is widely recognised that the construction industry is one of the most dangerous and physically demanding occupations in terms of ergonomics, with a high rate of early retirement and a significant proportion of ageing workers, which increases susceptibility to WRMSDs [20][62].

2.4. Feedback Source and Type

Wearables have emerged as a solution for the prevention of WRMSDs due to their versatility, particularly because of the real-time feedback they can provide [44]. This feedback should provide personalised information, and it is important to ensure that workers understand and adjust their posture based on the stimulus received [63]. Currently, there are different types of sensory feedback, including haptic, visual, and auditory feedback [9], with haptic feedback being considered the most suitable and common approach [9][31][32][64]. This is consistent with the results of the present study, in which half of the authors used haptic feedback by generating vibratory stimuli. On the other hand, Lee et al. [7] found different results, with sound being the preferred variable. However, considering that sound feedback has a greater potential to distract the worker, the choice of haptic feedback seems to be more appropriate [64].
On the other hand, Wang et al. [32] recommended the combined use of multiple types of feedback to minimise potential disadvantages and fill gaps. This recommendation is in line with the study by Bootsman et al. [65], which used all three types of sensory feedback. Although this approach has its advantages, it is important to consider the suitability of the region and context for monitoring and applying feedback.
Given that both auditory and haptic feedback do not require visual attention during the task [32], they appear to be a valuable combination. However, in this specific case, the auditory feedback may have been drowned out by the noise of other equipment in the neonatal intensive care unit, which is typically loud. It is therefore important to carefully assess the pertinence of the feedback used, taking into account the aforementioned factors [20].
Consequently, the use of auditory feedback in the study by Yan et al. [66] may not have been the most suitable choice, as it involved construction tasks where, in addition to the existing noise, some hazardous tasks require full concentration. This may be the reason why the studies by Lind et al. [40][67] opted for haptic feedback for industrial workers.

2.5. Results after Feedback Application

The included studies demonstrated that there was generally an improvement in posture after the application of haptic feedback in real-world settings, which is congruent with the studies by Kuo et al. [68] and Lind et al. [53], although these were conducted in laboratory settings. The studies by Ribeiro et al. [69], Yan et al. [66], and Doss et al. [70] reported postural improvements following auditory feedback, which is supported by the laboratory study by Boocock et al. [71]. On the other hand, Ribeiro et al. [72] concluded that this type of feedback did not promote improvement during work, suggesting that haptic feedback may have been more appropriate for this class of professionals.
The terminal visual feedback identified in the studies by Thanathornwong et al. [73] and Thanathornwong et al. [74] also demonstrated significant postural improvements, although the stimulus was presented to the worker only at the end of the task. Based on these findings, it can be inferred that the choice of feedback depends on the tasks and the work context being evaluated.

References

  1. Seeberg, K.G.V.; Andersen, L.L.; Bengtsen, E.; Sundstrup, E. Effectiveness of workplace interventions in rehabilitating musculoskeletal disorders and preventing its consequences among workers with physical and sedentary employment: Systematic review protocol. Syst. Rev. 2019, 8, 1–7.
  2. Ciccarelli, M.; Corradini, F.; Germani, M.; Menchi, G.; Mostarda, L.; Papetti, A.; Piangerelli, M. SPECTRE: A deep learning network for posture recognition in manufacturing. J. Intell. Manuf. 2022, 34, 3469–3481.
  3. Lorenzini, M.; Ciotti, S.; Gandarias, J.M.; Fani, S.; Bianchi, M.; Ajoudani, A. Performance Analysis of Vibrotactile and Slide-and-Squeeze Haptic Feedback Devices for Limbs Postural Adjustment. In Proceedings of the 31st IEEE International Conference on Robot and Human Interactive Communication: Social, Asocial, and Antisocial Robots (RO-MAN), Napoli, Italy, 29–31 August 2022; pp. 707–713.
  4. Iñigo, I.; Lorenzo, M. Work-Related Musculoskeletal Disorders: Prevalence, Costs and Demographics in the EU—Salud y Seguridad en el Trabajo—EU-OSHA; 9789294791450; EU-OSHA: Luxembourg, 2020.
  5. Cerqueira, S.M.; Da Silva, A.F.; Santos, C.P. Smart Vest for Real-Time Postural Biofeedback and Ergonomic Risk Assessment. IEEE Access 2020, 8, 107583–107592.
  6. Sundstrup, E.; Seeberg, K.G.V.; Bengtsen, E.; Andersen, L.L. A Systematic Review of Workplace Interventions to Rehabilitate Musculoskeletal Disorders Among Employees with Physical Demanding Work. J. Occup. Rehabil. 2020, 30, 588–612.
  7. Lee, R.; James, C.; Edwards, S.; Skinner, G.; Young, J.L.; Snodgrass, S.J. Evidence for the Effectiveness of Feedback from Wearable Inertial Sensors during Work-Related Activities: A Scoping Review. Sensors 2021, 21, 6377.
  8. Antwi-Afari, M.F.; Li, H.; Yu, Y.; Kong, L. Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers. Autom. Constr. 2018, 96, 433–441.
  9. Cordella, F.; Di Luzio, F.S.; Lauretti, C.; Draicchio, F.; Zollo, L. A biofeedback-based posture correction system for working environments. In Proceedings of the 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2019, Naples, Italy, 4–6 June 2019; pp. 405–409.
  10. Eklund, J.; Forsman, M. Smart work clothes give better health—Through improved work technique, work organization and production technology. Adv. Comput. 2019, 820, 515–519.
  11. Tlili, F.; Haddad, R.; Bouallegue, R.; Shubair, R. Design and architecture of smart belt for real time posture monitoring. Internet Things 2022, 17, 100472.
  12. Poscia, A.; Moscato, U.; La Milia, D.I.; Milovanovic, S.; Stojanovic, J.; Borghini, A.; Collamati, A.; Ricciardi, W.; Magnavita, N. Workplace health promotion for older workers: A systematic literature review. BMC Health Serv. Res. 2016, 16, 415–428.
  13. Simpson, L.; Maharaj, M.M.; Mobbs, R.J. The role of wearables in spinal posture analysis: A systematic review. BMC Musculoskelet. Disord. 2019, 20, 55.
  14. Conforti, I.; Ilaria, M.; Zaccaria, D.P.; Eduardo, P. Measuring Biomechanical Risk in Lifting Load Tasks Machine-Learning Approach. Sensors 2020, 20, 1557.
  15. Wilczyński, J.; Lipińska-Stańczak, M.; Wilczyński, I. Body Posture Defects and Body Composition in School-Age Children. Children 2020, 7, 204.
  16. Adesida, Y.; Papi, E.; McGregor, A.H. Exploring the role of wearable technology in sport kinematics and kinetics: A systematic review. Sensors 2019, 19, 1597.
  17. Liu, X.; Wei, Y.; Qiu, Y. Advanced Flexible Skin-Like Pressure and Strain Sensors for Human Health Monitoring. Micromachines 2021, 12, 695.
  18. Patel, M.; Pavic, A.; Goodwin, V.A. Wearable inertial sensors to measure gait and posture characteristic differences in older adult fallers and non-fallers: A scoping review. Gait Posture 2020, 76, 110–121.
  19. de-la-Fuente-Robles, Y.-M.; Ricoy-Cano, A.-J.; Albin-Rodriguez, A.-P.; Luis Lopez-Ruiz, J.; Espinilla-Estevez, M. Past, Present and Future of Research on Wearable Technologies for Healthcare: A Bibliometric Analysis Using Scopus. Sensors 2022, 22, 8559.
  20. Stefana, E.; Marciano, F.; Rossi, D.; Cocca, P.; Tomasoni, G. Wearable Devices for Ergonomics: A Systematic Literature Review. Sensors 2021, 21, 777.
  21. Paloschi, D.; Bravi, M.; Schena, E.; Miccinilli, S.; Morrone, M.; Sterzi, S.; Saccomandi, P.; Massaroni, C. Validation and Assessment of a Posture Measurement System with Magneto-Inertial Measurement Units. Sensors 2021, 21, 6610.
  22. Yoong, N.K.; Perring, J.; Mobbs, R.J. Commercial Postural Devices: A Review. Sensors 2019, 19, 5128.
  23. Aryal, A.; Ghahramani, A.; Becerik-Gerber, B. Monitoring fatigue in construction workers using physiological measurements. Autom. Constr. 2017, 82, 154–165.
  24. Schall, M.C.; Sesek, R.F.; Cavuoto, L.A. Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals. Hum. Factors 2018, 60, 351–362.
  25. Buisseret, F.; Dierick, F.; Van der Perre, L. Wearable Sensors Applied in Movement Analysis. Sensors 2022, 22, 8239.
  26. Clark, B.K.; Brakenridge, C.L.; Healy, G.N. The Importance of Research on Occupational Sedentary Behaviour and Activity Right Now. Int. J. Environ. Res. Public Health 2022, 19, 15816.
  27. Chander, H.; Burch, R.F.; Talegaonkar, P.; Saucier, D.; Luczak, T.; Ball, J.E.; Turner, A.; Arachchige, S.N.K.K.; Carroll, W.; Smith, B.K.; et al. Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics. Int. J. Environ. Res. Public Health 2020, 17, 3554.
  28. Papi, E.; Koh, W.S.; McGregor, A.H. Wearable technology for spine movement assessment: A systematic review. J. Biomech. 2017, 64, 186–197.
  29. Carey, S.; Ross, J.M.; Balasubramaniam, R. Auditory, tactile, and multimodal noise reduce balance variability. Exp. Brain Res. 2023, 241, 1241–1249.
  30. Lind, C.M.; Abtahi, F.; Forsman, M. Wearable Motion Capture Devices for the Prevention of Work-Related Musculoskeletal Disorders in Ergonomics-An Overview of Current Applications, Challenges, and Future Opportunities. Sensors 2023, 23, 4259.
  31. Fani, S.; Ciotti, S.; Bianchi, M. Multi-Cue Haptic Guidance Through Wearables for Enhancing Human Ergonomics. IEEE Trans. Haptics 2022, 15, 115–120.
  32. Wang, Q.; Markopoulos, P.; Yu, B.; Chen, W.; Timmermans, A. Interactive wearable systems for upper body rehabilitation: A systematic review. J. Neuroeng. Rehabil. 2017, 14, 17.
  33. Lim, S.; Yang, X. Real-time vibrotactile feedback system for reducing trunk flexion exposure during construction tasks. Appl. Ergon. 2023, 110, 104019.
  34. Svertoka, E.; Saafi, S.; Rusu-Casandra, A.; Burget, R.; Marghescu, I.; Hosek, J.; Ometov, A. Wearables for Industrial Work Safety: A Survey. Sensors 2021, 21, 3844.
  35. Huang, X.; Xue, Y.; Ren, S.; Wang, F. Sensor-Based Wearable Systems for Monitoring Human Motion and Posture: A Review. Sensors 2023, 23, 9047.
  36. Yang, M.; Liu, Y.; Yang, W.; Liu, J. Thread-Embedded-in-PDMS Wearable Strain Sensor for Real-Time Monitoring of Human Joint Motion. Micromachines 2023, 14, 2250.
  37. Norasi, H.; Tetteh, E.; Money, S.R.; Davila, V.J.; Meltzer, A.J.; Morrow, M.M.; Fortune, E.; Mendes, B.C.; Hallbeck, M.S. Intraoperative posture and workload assessment in vascular surgery. Appl. Ergon. 2021, 92, 103344.
  38. Owlia, M.; Kamachi, M.; Dutta, T. Reducing lumbar spine flexion using real-time biofeedback during patient handling tasks. Work 2020, 66, 41–51.
  39. Kamachi, M.; Owlia, M.; Dutta, T. Evaluating a wearable biofeedback device for reducing end-range sagittal lumbar spine flexion among home caregivers. Appl. Ergon. 2021, 97, 103547.
  40. Lind, C.M.; De Clercq, B.; Forsman, M.; Grootaers, A.; Verbrugghe, M.; Van Dyck, L.; Yang, L. Effectiveness and usability of real-time vibrotactile feedback training to reduce postural exposure in real manual sorting work. Ergonomics 2023, 66, 198–216.
  41. Chan, L.Y.T.; Chua, C.S.; Chou, S.M.; Seah, R.Y.B.; Huang, Y.; Luo, Y.; Dacy, L.; Bin Abd Razak, H.R. Assessment of shoulder range of motion using a commercially available wearable sensor—A validation study. mHealth 2022, 8, 1–12.
  42. Fan, X.; Lind, C.M.; Rhen, I.M.; Forsman, M. Effects of Sensor Types and Angular Velocity Computational Methods in Field Measurements of Occupational Upper Arm and Trunk Postures and Movements. Sensors 2021, 21, 5527.
  43. Choi, B.; Hwang, S.; Lee, S.H. What drives construction workers’ acceptance of wearable technologies in the workplace?: Indoor localization and wearable health devices for occupational safety and health. Autom. Constr. 2017, 84, 31–41.
  44. Donisi, L.; Cesarelli, G.; Pisani, N.; Ponsiglione, A.M.; Ricciardi, C.; Capodaglio, E. Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature. Diagnostics 2022, 12, 3048.
  45. Brambilla, C.; Lavit Nicora, M.; Storm, F.; Reni, G.; Malosio, M.; Scano, A. Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review. Bioengineering 2023, 10, 445.
  46. Wong, W.Y.; Wong, M.S. Trunk posture monitoring with inertial sensors. Eur. Spine J. 2008, 17, 743–753.
  47. Rusek, W.; Baran, J.; Leszczak, J.; Adamczyk, M.; Weres, A.; Baran, R.; Inglot, G.; Pop, T. The Influence of Body Mass Composition on the Postural Characterization of School-Age Children and Adolescents. Biomed. Res. Int. 2018, 2018, 9459014.
  48. Knight, J.F.; Baber, C. A tool to assess the comfort of wearable computers. Hum. Factors 2005, 47, 77–91.
  49. Gioberto, G.; Dunne, L. Theory and characterization of a top-thread coverstitched stretch sensor. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Seoul, Republic of Korea, 14–17 October 2012; pp. 3275–3280.
  50. Park, S.Y.; Yoo, W.G. Effect of EMG-based feedback on posture correction during computer operation. J. Occup. Health 2012, 54, 271–277.
  51. Alberto, R.; Draicchio, F.; Varrecchia, T.; Silvetti, A.; Iavicoli, S. Wearable monitoring devices for biomechanical risk assessment at work: Current status and future challenges—A systematic review. Int. J. Environ. Res. Public Health 2018, 15, 2001.
  52. Barone, V.J.; Yuen, M.C.; Kramer-Boniglio, R.; Sienko, K.H. Sensory garments with vibrotactile feedback for monitoring and informing seated posture. In Proceedings of the RoboSoft 2019—2019 IEEE International Conference on Soft Robotics, Seoul, Republic of Korea, 14–18 April 2019; pp. 391–397.
  53. Lind, C.M.; Diaz-Olivares, J.A.; Lindecrantz, K.; Eklund, J. A Wearable Sensor System for Physical Ergonomics Interventions Using Haptic Feedback. Sensors 2020, 20, 6010.
  54. Yang, L.; Grooten, W.J.A.; Forsman, M. An iPhone application for upper arm posture and movement measurements. Appl. Ergon. 2017, 65, 492–500.
  55. Alnaser, M.Z.; Almaqsied, A.M.; Alshatti, S.A. Risk factors for work-related musculoskeletal disorders of dentists in Kuwait and the impact on health and economic status. Work 2021, 68, 213–221.
  56. Holzgreve, F.; Fraeulin, L.; Betz, W.; Erbe, C.; Wanke, E.M.; Brüggmann, D.; Nienhaus, A.; Groneberg, D.A.; Maurer-grubinger, C.; Ohlendorf, D. A RULA-Based Comparison of the Ergonomic Risk of Typical Working Procedures for Dentists and Dental Assistants of General Dentistry, Endodontology, Oral and Maxillofacial Surgery, and Orthodontics. Sensors 2022, 22, 805.
  57. Kawtharani, A.A.; Chemeisani, A.; Salman, F.; Haj Younes, A.; Msheik, A. Neck and Musculoskeletal Pain Among Dentists: A Review of the Literature. Cureus 2023, 15, 1–11.
  58. Jacquier-Bret, J.; Gorce, P. Prevalence of Body Area Work-Related Musculoskeletal Disorders among Healthcare Professionals: A Systematic Review. Int. J. Environ. Res. Public Health 2023, 20, 841.
  59. Blume, K.S.; Holzgreve, F.; Fraeulin, L.; Erbe, C.; Betz, W.; Wanke, E.M.; Brueggmann, D.; Nienhaus, A.; Maurer-Grubinger, C.; Groneberg, D.A.; et al. Ergonomic Risk Assessment of Dental Students-RULA Applied to Objective Kinematic Data. Int. J. Environ. Res. Public Health 2021, 18, 10550.
  60. Gandolfi, M.G.; Zamparini, F.; Spinelli, A.; Risi, A.; Prati, C. Musculoskeletal Disorders among Italian Dentists and Dental Hygienists. Int. J. Environ. Res. Public Health 2021, 18, 2705.
  61. Sezer, B.; Kartal, S.; Sıddıkoğlu, D.; Kargül, B. Association between work-related musculoskeletal symptoms and quality of life among dental students: A cross-sectional study. BMC Musculoskelet. Disord. 2022, 23, 41.
  62. Antwi-Afari, M.F.; Li, H.; Chan, A.H.S.; Seo, J.; Anwer, S.; Mi, H.-Y.; Wu, Z.; Wong, A.Y.L. A science mapping-based review of work-related musculoskeletal disorders among construction workers. J. Safety Res. 2023, 85, 114–128.
  63. Oppici, L.; Grütters, K.; Garofolini, A.; Rosenkranz, R.; Narciss, S. Deliberate Practice and Motor Learning Principles to Underpin the Design of Training Interventions for Improving Lifting Movement in the Occupational Sector: A Perspective and a Pilot Study on the Role of Augmented Feedback. Front. Sports Act Living 2021, 3, 746142.
  64. Lorenzini, M.; Kim, W.; De Momi, E.; Ajoudani, A. An Online Method to Detect and Locate an External Load on the Human Body with Applications in Ergonomics Assessment. Sensors 2020, 20, 4471.
  65. Bootsman, R.; Markopoulos, P.; Qi, Q.; Wang, Q.; Timmermans, A.A.A. Wearable technology for posture monitoring at the workplace. Int. J. Hum. Comput. 2019, 132, 99–111.
  66. Yan, X.; Li, H.; Li, A.R.; Zhang, H. Wearable IMU-based real-time motion warning system for construction workers’ musculoskeletal disorders prevention. Autom. Constr. 2017, 74, 2–11.
  67. Lind, C.M.; Yang, L.; Abtahi, F.; Hanson, L.; Lindecrantz, K.; Lu, K.; Forsman, M.; Eklund, J. Reducing postural load in order picking through a smart workwear system using real-time vibrotactile feedback. Appl. Ergon. 2020, 89, 103188.
  68. Kuo, Y.L.; Huang, K.Y.; Kao, C.Y.; Tsai, Y.J. Sitting posture during prolonged computer typing with and without a wearable biofeedback sensor. Int. J. Environ. Res. Public Health 2021, 18, 5430.
  69. Ribeiro, D.C.; Sole, G.; Abbott, J.H.; Milosavljevic, S. The effectiveness of a lumbopelvic monitor and feedback device to change postural behavior: A feasibility randomized controlled trial. J. Orthop. Sports Phys. Ther. 2014, 44, 702–711.
  70. Doss, R.; Robathan, J.; Abdelmalek, D.; Holmes, M.W.R. Posture Coaching and Feedback during Patient Handling in a Student Nurse Population. IISE Trans. 2018, 6, 116–127.
  71. Boocock, M.; Naudé, Y.; Taylor, S.; Kilby, J.; Mawston, G. Influencing lumbar posture through real-time biofeedback and its effects on the kinematics and kinetics of a repetitive lifting task. Gait Posture 2019, 73, 93–100.
  72. Ribeiro, D.C.; Milosavljevic, S.; Terry, J.; Abbott, J.H. Effectiveness of a lumbopelvic monitor and feedback device to change postural behaviour: The ELF cluster randomised controlled trial. Occup. Environ. Med. 2020, 77, 462–469.
  73. Thanathornwong, B.; Suebnukarn, S.; Ouivirach, K. A system for predicting musculoskeletal disorders among dental students. Int. J. Occup. Saf. Ergon. 2014, 20, 463–475.
  74. Thanathornwong, B.; Suebnukarn, S.; Songpaisan, Y.; Ouivirach, K. A system for predicting and preventing work-related musculoskeletal disorders among dentists. Comput. Methods Biomech. Biomed. Engin. 2014, 17, 177–185.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Public, Environmental & Occupational Health
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Vânia Figueira
,
Sandra Silva
,
Inês Costa
,
Bruna Campos
,
João Salgado
,
Liliana Pinho
,
Marta Freitas
,
Paulo Carvalho
,
João Marques
,
Francisco Pinho
View Times: 67
Revisions: 2 times (View History)
Update Date: 29 Feb 2024
Table of Contents
    1000/1000
    Hot Most Recent
    Feedback