1. Introduction
Industrial workers get constantly exposed to different agents in their workplaces, such as dust and vapors, which act as irritants, carcinogens, or immunological agents [
1,
2]. The National Institute for Occupational Safety and Health (NIOSH) from United States of America (USA) declares that the prevention of exposure to dangerous particles using Personal Protective Equipment (PPE) and the monitoring of environmental conditions are the primary methods of controlling respiratory diseases on workers. However, workers may still be affected by those dangerous particles due to extended time exposure, defective PPE, and previous worker’s health condition [
3]. Respiratory conditions due to toxic agents have been reported as one of the three leading causes of occupational illnesses in the United States, leading to 508.4 thousand cases within all industries during 2020 [
4]. According to the U.S. Bureau of Labor Statistics, in 2020, 672 workers died due to exposure to harmful substances or environments [
5].
Workers’ health monitoring is crucial for preventing respiratory illnesses. Medical respiratory techniques used for this purpose are radiography, spirometry, questionnaires, and bio-markers [
3]. However, those methods do not perform continuous measurements in actual conditions since they require workers to leave their tasks to perform the corresponding measurements. Since PPE are critical for industrial workers, there is commercially available equipment that quantifies the respirator fitting and detects air leaks, such as the PortaCount
® Respirator Fit Tester (TSI Incorporated, Shoreview, MN, USA) [
6], which evaluates the seal between the face and the respirator. The equipment performs the test outside the work area and before the workday, so if the respirator loses its fit or is damaged, it is not possible to monitor the exposure to potentially harmful environments during the work-shift [
7].
To assess the issue concerning real-time and on-site breathing monitoring, wearable systems have been developed for clinical applications mostly. Researchers have developed new devices that can measure breathing variables in a non-invasive way. Different sensors have been used to interpret changes in the body (or a field near the body) to track breathing. The chest movements during breathing produce changes of capacitance in the thorax cycle that can be measured using impedance measurement systems [
8,
9,
10]. Other devices estimate the mechanical oscillations of the thorax during breathing using inertial sensors, strain gauges, inductance plethysmography, or flexible resistive bands [
11,
12,
13,
14,
15]. In general, the systems are accurate and can perform breathing monitoring correctly. However, these devices may not be suitable for industrial environments as the electrodes positioned over the worker’s body may change their position during the activities. Moreover, their clothes cannot be intervened for adding strain gauges, and adhesive stripes may cause discomfort while working. Other solutions measure temperature near the respiratory tract where expired air has a greater temperature than inspired, suitable for wearables and non-invasive embedded systems [
16,
17].
Industrial respirators and face masks are a key implement to develop wearable breathing monitoring systems. Xu et al. [
18] developed a small sensor to be used in the exhalation valve of a KN95 mask. The sensor measures the periodic airflow temperature variations caused by exhaled hot air and inhaled cool air in respiratory cycles. The work focuses on the sensor and algorithm development for assessing the user’s breathing frequency. Although this sensor may be suitable to use in the industrial environment, it does not allow for assessing the respirator fitting and the filter clogging level. Lo Presti et al. [
19] developed a fiber Bragg grating-based sensor based on nasal airflow changes detection from humidity signals. Although the sensor offers a comfortable and minimally invasive way of monitoring breathing frequency, it is located under the nostrils and is attached to the user’s ears, which is not suitable for workers during their shifts. In [
20], a biomask with integrated sensors is presented as a device compatible with electroencephalography, electromyography, electrooculography, and electrocardiogram for patients in clinical environments. The study [
21] shows a patent for a sensorized mask for CPAP machines that measures physiological variables. These two proposals show innovative designs of sensorized respirators with clinical applications, but none can be applied to industrial environments. A patent [
22] shows a wearable mask fit monitor that measures particle concentrations inside and outside the respirator. This device does not indicate the breathing rate measurement during work-shifts.
The main breathing monitoring techniques in clinical practice are pulse oximetry, pressure/volume curves, electric impedance tomography, and diaphragmatic electromyography, which have shown high reliability in assessing the patient respiratory system [
23]. However, none of these measurement techniques and devices have been applied to workers in industrial sites due to these devices require special conditions in a controlled environment.
Considering that in some industries, such as the mining industry, the work shifts can last up to 12 hours [
24,
25], it is essential to implement continuous breathing monitoring and environmental monitoring techniques to complement PPE [
3,
6,
26]. The continuous monitoring during several hours summed to the strict regulations on the use of wireless networks represents a challenge for implementing new monitoring devices within the workplace, due to the presence of several networks with low bandwidth and high saturation [
27,
28].
This paper proposes a novel wearable breathing electronic sensor inside a half-face-piece respirator. This sensor inside a PPE commonly used in industrial environments [
5] measures the pressure (P), temperature (T), and relative humidity (rH) to continuously monitor workers’ breathing and environmental conditions in their workplace. From now on the paper we use P for pressure signal, T for temperature signal and rH for relative humidity signal. The objective is to evaluate the level of protection of their PPE, discriminating between fitting conditions and differentiates levels of filter clogging.
4. Discussion
In the present work, a reusable embedded electronic device was designed and implemented for breathing activity monitoring inside an industrial reusable respirator. The proposed system allows measuring the changes in the pressure (P), temperature (T) and relative humidity (rH) in the confined space volume inside a standard industrial respirator, and sending the real-time measurement data wirelessly to an external device for processing and data analytics. Breathing frequency, fitting condition and filter’s clogging level are estimated and reported using the measured data. These data can be used to develop modern plans and strategies with the aim of improving the occupational safety and health throughout the real-time and on-line monitoring of workers’ breathing conditions.
Proposed hardware design considers a battery energy autonomy that exceeds the power requirements for monitoring during a standard working day shift (8 to 12 h [
24]). The MCU, pressure/temperature/relative humidity sensors, and the Bluetooth BLE link consume 9.9 mW average during connection and send of data. This allows an autonomy up to 30 h using a 3.7 V-100 mAh battery using a sample frequency of 10 Hz. Furthermore, the hardware was embedded in epoxy resin to homologate the IP66 ingress protection code (protected from total dust ingress, protected from high-pressure water jets from any direction), thus enabling to wear the device in adverse industrial environment conditions in different reusable respirators.
The proposed processing technique is an easy-to-implement method in embedded electronic devices. For measured signals, there is no need for further processing over raw data than the application of a zero-phase 4th Butterworth filter (passband 0.16–0.36 Hz), and a gradient-based algorithm to detect local maxima and minima for T and rH signals, and detection of inflection points for AF and P signals in each breathing cycle. These characteristics make the algorithm suitable for real-time applications. Furthermore, a high linear correlation (0.98) respect to the the reference AF signal was obtained using the three signals (rH, T and P signals) to calculate the breathing frequency (see
Figure 11,
Figure 12 and
Figure 13). Thus, it is possible to accurately calculate the instantaneous breathing rate using any of the signals acquired with the embedded electronic device. The minimum estimation error for breathing activity (0.01 ± 1.3 BPM) was obtained in experimental tests using the P signal as input for the prediction model (see
Figure 15). It should be pointed out that the breathing rate calculation can be used for assessing the workers breathing in the workplace during their normal activities and to early-detect possible breathing disorders or deviations in operating conditions.
Using the proposed system, it is possible to discriminate between a fitted and loose condition of the respirator. The test protocol to evaluate the fitting condition considers a first period using a fitted respirator (Initial good fitted condition), then the respirator was loosen and next the respirator was again fitted (Final fitted condition). For the rH and P signals it was possible to separate the Initial Fitted to Loose condition, whereas for the T signal it was not possible to discriminate between fitting conditions. The Initial Fitted to Final Fitted condition t-test was performed to evaluate if the signals peak to peak values returned to their initial distribution when the respirator was adjusted a second time. This was observed only for the P signal. Detecting the fitting condition of a respirator in real-time can help to avoid the exposure of a worker to a harsh environment conditions of dust and gases.
Regarding the clogging level of filters, only using the P signal a statistical difference between the groups 0 to 60%, 80% and 100% clogging level was found. This statistical difference allows to detect three different clogging levels of filters from a reusable industrial respirator. Today, according to the state-of-the-art, there is no device or test or combination of both which can detect clogging levels of filters of an industrial reusable respirator during the work-shift when workers are on duty. The proposed embedded electronic device for breathing activity monitoring can be use to detect the clogging level in real-time and generating early-detection alerts accordingly in cases where the filters are reaching dangerous clogging levels and the workers breathing can be impaired. Laboratory tests have shown that the proposed embedded electronic device could make real-time on-line breathing frequency estimations with good accuracy and precision, discriminates the industrial respirator fitting condition considering two opposite fitting conditions (Fitted and loosened respirator) and identifies of up to three filter clogging levels.
To our knowledge, currently, there are no devices available at the market that allow continuous breathing monitoring in industrial environments, neither to evaluate the respirator and filters performance. Compared to medical respiratory techniques and commercially available equipment, the proposed electronic device allows a continuous and in-site measurement of the worker’s breathing. This continuous monitoring will allow to generate alerts in case of breathing problems and to make plans to reduce breathing diseases.
Table 6 shows a performance comparison of breathing rate estimation between our proposal and state-of-art systems. Even though our system has not the best performance to calculate the breathing rate, but 98.9% is high enough for this industrial application. In addition, the embedded electronic device allows for the respirator fitting and filter clogging assessment, which any state-of-art systems do not. While this paper presents promising results, it does have some limitations. The proposed embedded electronic device for workers’ breathing monitoring was only tested with ten healthy subjects breathing in a sit position. Its mandatory to validated with a higher number of subjects in an industrial environment to prove its applicability outside the laboratory environment. Future work may include, and is not limited to, adding new sensors, test new positioning for sensors, and/or integrates inertial sensors for more comprehensive analysis of breathing activity, fitting or clogging of filters of industrial reusable respirators. Additionally, the measured data can be exploited using new data-driven machine learning models to add new features such as better forecast horizon for future clogging levels and prediction of remaining useful life of filters. Another interesting topic for industrial testing and scale-up is the performance evaluation using different wireless communication technologies (WiFi, ZigBee or LoRa instead of Bluetooth to link the device with mobile smartphones or similar). Next steps also include pilot testing at industrial scale in order to validate the system performance, and to adapt the proposed system to other environmental extreme conditions to which workers may be exposed at industrial environments.
5. Conclusions
This work proposes a functional building block with embedded electronics capable of measuring and monitoring the breathing activity variables and wear condition of workers when installed in the confined space volume inside an industrial reusable respirator. The design includes pressure, temperature and relative humidity sensors and a Bluetooth BLE communication link to send the collected signals wirelessly to an external device (server cloud, server on-premise or mobile smartphone) for further processing and automated data analytics. After signal pre-processing, instantaneous breathing rate is calculated and respirator fitting and clogging levels of the filter can be estimated. The embedded electronic device was tested with ten healthy participants, presenting promising results about the calculation of breathing rates and the estimation of filters clogging levels and respirator fitting. The results showed that P, T, and rH signals can be used for an accurate rate breathing estimation and detect fitted or loose respirator status. The minimum estimation error for breathing activity (0.01 ± 1.3 BPM) was obtained in experimental tests using the P signal as input for the prediction model. It should be pointed out that the breathing frequency calculation can be used for assessing the workers breathing in the workplace during their normal activities and to early-detect possible breathing disorders or deviations in operating conditions. P was the only signal that allowed to estimate differences between three different filter clogging levels (<60%, 80% and 100% clogging). With the proposed system in this paper we cannot detect filter clogging under 60%. The embedded electronic device is designed to connect with a smartphone to alert workers locally about breathing problems or PPE failure. In addition, this proposed system can alert any issue to supervisors through an internet connection. Then, in case of an internet connection problem, the worker’s supervisor will not be alerted immediately. In industrial sites, the internet connection may fail due to electric noise, network saturation, and areas without internet coverage. For special applications, different wireless communication technologies must be studied and added to the electronic device for a proper work.
Today, according to the state-of-the-art, there is no device or test or combination of both which can detect clogging levels of filters of an industrial reusable respirator during the work-shift when workers are on duty. The proposed embedded electronic device for breathing activity monitoring can be use to detect the high clogging level in real-time and generating early-detection alerts accordingly in cases where the filters are reaching dangerous clogging levels and the workers breathing can be impaired. Those alerts can be sent to the supervisor and the same worker using a mobile phone.