Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions
Abstract
:1. Introduction
2. COPD
2.1. COPD Diagnosis
2.2. COPD Indicators and Monitoring
- Activities of Daily Living (ADL): COPD is typically accompanied by decreased ADL [23], which makes ADL assessment one of the best COPD evaluation methods. Furthermore, it is vital for COPD patients to increase their ADLs [24]. A study implemented real-time activity classification by placing sensors on the forearm, thigh, and sternum [25]. Wearable technologies such as smart vests and t-shirts were developed to reduce the number of sensors required, and cloud-connected platforms were designed for remote monitoring and interactions [26,27].
- Volatile Organic Compounds (VOCs): VOCs, such as isoprene and hexadecane, are typical kinds of the COPD biomarkers in exhaled breath [28,29]. A portable spectrometer has been proposed for chemical analysis [30]. However, there are contrary opinions on using VOC profiles for COPD diagnosis. Research shows that VOC profiles could identify patients with COPD accurately [31,32,33], while it was also observed by some studies that VOC profiles cannot distinguish smokers, including former smokers, from COPD patients [34,35].
- Blood Lactate Level: The blood lactate level is another COPD biomarker [36]. It has been reported that people with COPD tend to have a higher blood lactate level than their healthy counterparts while doing the same activities at the same intensity [36]. As a result, lactic acid has been proposed and used as a biomarker of COPD severity [37]. Several novel approaches using flexible electronics were developed to measure the lactic acid through human tears, saliva, and sweat [38,39,40]. However, in tests, it was found that these electronics were not very comfortable to wear, and their practicality needs further discussion [41].
- Saliva: Dysphagia is regarded as one of the high-risk phenotypes for the prediction of COPD exacerbation by some studies [42]. Research has found, as a less invasive way to screen dysphagia, that a repetitive saliva swallowing test cut-off value of 5 could be a strong predictor of COPD exacerbation [43]. Compared to bio-samples like blood and sputum, saliva is relatively easy to use, especially for home monitoring. A novel biosensor called COPD saliva-based point-of-care monitor has been designed to enable patients to undergo testing at home and identify exacerbation in time [44,45].
- Respiration: As aforementioned, the main symptoms of COPD are related to the patient’s respiratory condition. This follows from the medical explanation of why breathing will change is that sternomastoid muscles, which are accessory muscles, are used during the exacerbation period of COPD [46]. Research has explored the consistency and accuracy of breathing sounds at various airflow levels and predetermined bodily sites in individuals with COPD [47]. A conclusion was drawn that the most reliable interval of air flow is 0.4–0.6 L/s, and this applies to respiratory sound parameters at all anatomic locations [47]. Moreover, it is recommended to be considered in computerized auscultation for future use [47]. On the other hand, research shows that the respiration characteristics of COPD differ from other dyspnea diseases [48]. Furthermore, a study found the potential of computerized analyses of respiratory sounds in respiratory status monitoring for people suffering from COPD, and this study achieved “75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at [sic] medical attention” [49].
- Cough and Sputum Production: Chronic cough and sputum production are not only very common in subjects with COPD, but have also been suggested as being predictive of disease progression, exacerbations, and hospitalizations [50]. Research on using chronic cough or phlegm to predict or identify COPD risk severity achieved some success [51,52]. It has been pointed out that each of them is an independent and statistically significant predictor of COPD [51]. Experiments successfully identified a subgroup of participants at a high COPD risk, irrespective of smoking habits. The study contends that the occurrence of a chronic cough or phlegm serves as an early indicator of COPD in a significant number of patients, regardless of their smoking status [51]. Sumner et al. considered cough only in a study, and a comparison was made of 68 randomly selected current and ex-smokers with COPD and smokers without COPD and healthy nonsmokers, using a custom-built cough sound recording device over 24 h. An outcome of this study was that objective cough monitoring is viable. Moreover, it offers a great prospect that cough monitoring can provide timely feedback for COPD interventions and also allows for adapting the new strategy in development [52].
2.3. Environmental Risks
3. Industry 4.0-Compliant Management
3.1. IoT and Data Analysis
- Adherence to and staying current with the latest regulations or directives;
- Protection of workers by identifying workplace hazards and near misses;
- Monitoring of training activities of employees on important EHS policies and procedures;
- Improvement of employee exposure information.
- Remote Health Condition Monitoring: Applying IoT for medical purposes, such as using wearable sensors for wireless monitoring, is a trending topic. A design of the Internet of Medical Things (IoMT) for remote respiratory rate monitoring of COPD patients was proposed to improve doctor–patient communication [67]. It used message queue telemetry transport protocol and could send clinical alarms according to configurable thresholds. Additionally, a smart vest was designed for breathing monitoring during the rest period, with embedded capacitive sensors [67]. Similarly, to make the vest more comfortable to wear, inkjet-printed sensor technology was used [68]. It is stretchable and wearable, and the design achieved high measurement accuracy at different postures and among different patients. In the research of these two smart vests, the only parameter considered was the breath rate of the wearer, and the measurements were supposed to be taken during the rest period. For the same purpose of remote respiration monitoring, much more physiological parameters were covered in the design of a smart mask proposed by Tipparaju et al. [69]. In this work, principle component analysis (PCA) was applied to analyze the respiration pattern of each participant, but neither the pathology basis nor how to use it for a particular disease was considered [69].
- Working Environment Control: To the best knowledge of the authors, universal examples of Industry 4.0-compliant environmental COPD risk control do not exist, probably due to the large variety of COPD substances. However, research on occupational exposure management IoT systems has been reported [70,71]. A project aimed at sustainable health management presented an IoT-based indoor environment monitoring system tracking O3 concentrations near photocopy machines [70]. The developed sensing node contains a Bluetooth module and a semiconductor O3 sensor, apart from which, the developed IoT system also includes gateway nodes and processing nodes. It was claimed that the design can be expanded to cover larger areas and more pollutants such as hydrocarbons and different-size particles [70]. Similarly, Fathallah et al. conducted work on occupational exposure estimation and proposed a real-time occupational exposure monitoring model [71]. It successfully quantified indoor worker exposure to formaldehyde and CO2 in real time using multi-pollutant sensor nodes and an indoor positioning system [71].
- ML-Assisted Assessment and Prediction: The introduction of ML to assist diagnosis is a new trend. Zarrin and Wenger developed an Artificial Neural Network (ANN) model for pattern recognition for COPD diagnosis [72]. In this study, eight fundamental parameters were considered: the viscosity of saliva samples, the ambient temperature, patient smoking background, cytokine level, pathogen load, mucin combinations, gender, and age. Moreover, the output was set to four different kinds of disease statuses: healthy, low probability, high probability, and COPD-diseased. After comparing to the actual states, an accuracy rate of 112 out of 200 was achieved [72]. Attempts of COPD readmission prediction have also been made. COPD patients were required to use accelerometer-based wrist-worn wearable devices during daily living and readmission risks for 30 days, and were predicted based on their physical activity, including the activity index and regularity index, and quality of activity [73]. The results from 16 COPD patients showed a sensitivity of 63% and a positive prediction rate of 37.78%, which can be considered a significant improvement in comparison to other clinical assessments [73].
3.2. Industry 4.0-Compliant Prevention Based on Underpinning OHS and Medical Management Approaches
- Elimination: physical removal of the hazard;
- Substitution: replacement of the hazard;
- Engineering Controls: isolation of people from the hazard;
- Administrative Controls: change the way people work;
- Personal Protective Equipment (PPE): protects the worker.
- The redefinition of medicine as an informative science;
- The interconnected domains composing complex diseases;
- The emerging technologies allowing for different approaches to understand and access patient data;
- New and powerful analytical systems.
4. Discussion
4.1. Identification of Opportunities and Challenges
- Health Condition Detection: current research on how digital technologies such as IoT and artificial intelligence can specially support the treatment of occupational-related COPD, rather than OHS management, more related to protection against and prevention of COPD. Systematic reviews reported that digital health interventions (DHIs) for COPD show some uptake problems, like low compliance rates and lack of personalization [77,78]. Some remote monitoring systems also present restricted utilization to specific times during the day [77]. Moreover, measurements should also be more adjustable to the requirements of the target population [78].
- Protection: active protection is one of the new trends in OHS 4.0; digital technologies like smart PPE and WSNs can provide more sources and types of data to support further analysis. However, COPD risk factors found in workplaces usually vary. Therefore, monitoring systems with fixed alarm values of one or two substances are not effective enough. It should be noted that some exposure exceeding critical values could be easily avoided by combining environmental monitoring systems with primary real-time intervention control, such as connecting traditional protection equipment like LEV systems and environmental sensors to cyber–physical systems (CPSs), allowing for reducing the exposure level in the workplace and maintaining it under WEL in real time. Regarding personal protection, Adjiski et al. devised smart underground mining PPE by introducing sensors and wireless communication modules into safety wear [75].
- Assessment: the integration of traditional assessment methods and ML algorithms can improve accuracy and help optimizing management. COPD risk assessment in workplaces needs to be more personalized and dynamic. The lack of personalization of current approaches, which use the same standard for different workers at different ages and different jobs can result in misdiagnoses. With the idea of new conceptual OHS management, digital technologies such as data fusion (e.g., sound and temperature) and ML show high potential for assessment assistance and decision optimization. For example, without motion working state recognition, health condition monitoring could be meaningless. Moreover, unlike other industrial diseases, such as HAVS and MSDs, there are no “ergonomic tools” for COPD, nor well-developed analysis and assessment standards. In addition, it is hard to diagnose or predict COPD as it is a “chronic” disease, influenced by several factors, including various substances and lifestyle habits like exercise and smoking.
4.2. Future Trends and a Vision of Implementation
- Real-Time Monitoring: Continuous health condition monitoring is necessary to analyze the influences of COPD risk factors on workers. Motion monitoring is also needed for working state recognition, while real-time environmental monitoring helps identify the COPD risk factors and workers’ positions.
- Dynamic Exposure Assessment: Data fusion technology can combine the information acquired from the wearable sensors and environmental sensors, using it to establish what happened where, when, and to whom. Exposure assessments like exposure profiles and hot points should be carried out considering job types and layout.
- Effective and Targeted Intervention: Intervention must be performed in a personalized way. Instead of pre-setting values, ML algorithms can help producing effective and targeted intervention standards based on different health conditions and jobs. For example, intervention including alarm, LEV control, and smoke cessation suggestions could be delivered through CPS in the workplace. This way, the lung function prediction of each worker and COPD risk assessment of a workplace could be performed without long-time observations.
5. Conclusions
- Heath condition detection methods from industrial perspective are needed for the purpose of occupational protection. Moreover, for different target populations, measurements should be adjusted for a better uptake.
- Traditional hazard assessments rely on manual periodic checks, which are both time-consuming and expensive, and lead to less accurate results. Sensor-based hazard monitoring is supposed to deal with a wide range of hazards. Dynamic WELs, i.e., exposure thresholds varying with time, should be calculated and derived to drive active protection or real-time intervention control introduced by CPS.
- COPD is a chronic disease with complex causes and varies from person to person. Compared to other diseases, it is difficult to convert current COPD diagnosis criteria into computer algorithms. A personalized diagnosis taking an individual’s physical states and circumstances into account is vital for accurate conclusion in decision making.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Occupation | Substance | |
---|---|---|
Agriculture | Brick making | Cadmium dust |
Construction | Dock workers | Organic dusts |
Textiles | Quarries | Grain and flour dust |
Mining | Welders | Welding fumes |
Stonemasonry | Cadmium | Cadmium fumes |
Rubber | Plastics | Silica dust |
Petroleum workers | Foundry workers | Mineral dust |
Flour and grain workers in the food industry |
Variable | Standard | Indication | Sensor |
---|---|---|---|
FEV1 | 15% or 500 mL decline in one year [55] | COPD alarm | Portable spirometer [79,85,86] |
Respiration rate | 25 breaths per min (bpm) [85,88] | COPD exacerbation | Acoustic sensor [88] |
Cough | 3 months per year for 2 years [89] | Chronic bronchitis | Microphone [90] |
Activity | N/A | Working, walking, or sitting | Accelerometer |
COPD substance | N/A | COPD substance concentration | Air composition analysis |
Position | WELs | Workers’ positions | Wi-Fi, Bluetooth, etc. |
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Jiang, Z.; Bakker, O.J.; Bartolo, P.J. Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions. Sensors 2024, 24, 5734. https://doi.org/10.3390/s24175734
Jiang Z, Bakker OJ, Bartolo PJ. Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions. Sensors. 2024; 24(17):5734. https://doi.org/10.3390/s24175734
Chicago/Turabian StyleJiang, Zhihao, Otto Jan Bakker, and Paulo JDS Bartolo. 2024. "Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions" Sensors 24, no. 17: 5734. https://doi.org/10.3390/s24175734