Intelligent Thermoregulation in Personal Protective Equipment ^†

Abstract

With the prospect of deploying intelligent thermal management in protective equipment, strategies for integrating heating and cooling actuators with modular temperature controls and automatic temperature regulation systems based on feedback from the individual’s personal and environmental parameters are discussed.

1. Introduction

Prolonged exposure to extreme hot or cold temperatures in the workplace entails physical risks for workers, particularly in sectors such as firefighting, construction, mining, primary processing, metal products manufacturing, forestry, agriculture, and the food industry [1]. Exposure to extreme temperatures can lead to heat stress, which occurs when the human thermoregulatory system is unable to maintain body temperature between 36 and 37 °C [2]. As well as being a direct cause of serious injury in the workplace, exposure to extreme temperatures can indirectly lead to accidents and other types of injury. Indeed, thermophysiological stress can impair cognitive functioning, decision making, and task performance [1]. Certain physical abilities may also diminish, such as manual function, in the case of extreme cold. This can impair task performance and increase the risk of accidents or intensify the hazardous situation [3]. Despite the textile industry’s efforts to offer better protection against extreme temperatures in the workplace, most personal protective equipment suffers greatly from a lack of thermal comfort, especially as it must meet certain specifications depending on the industrial sector [4]. In some cases, the materials used in the design of personal protective equipment tend to hinder the adequate dissipation of body heat, thereby increasing the risk of occupational illness or injury [5]. Furthermore, according to numerous research, the current context of climate change could accentuate the impact of thermal constraints on the health of workers. Thus, protecting workers against thermal risks becomes more important [6]. It is therefore essential to develop new tools to ensure that thermal risk management is adapted to the individual situation of the worker and their work environment. In such a context, integrated textronic technologies (e-textiles) have great potential to address many of these problems.

2. Current Electrical Heating and Cooling Technologies

Flexible and stretchable conductive and electronic textiles can indeed contribute to the development of thermal regulation systems integrated into personal protective equipment. They can be used in the development of heating and cooling elements, as well as sensors measuring external and internal garment temperatures or body temperature. Coated metallized yarns, textile substrates coated with conductive polymers, carbon fibers, or carbon-based compositions are among the textronics that enable the creation of woven, knitted, or embroidered heating elements [7,8]. Cooling systems by liquid circulation, air or gas circulation, and air ventilation, as well as thermoelectric units, have been widely used in recent decades to develop cooling clothing [9,10]. Despite these recent advances, the use of these technologies in personal protective equipment remains very marginal, and most of the current commercial solutions are dedicated to the fields of sport and leisure [11]. In their current design, these systems seem unsuitable and difficult to use in a workplace health and safety context. Indeed, some integrated electric cooling systems remain heavy and bulky due to the size of their cooling tank and their circulation system [9]. Additionally, an efficient power supply for electrical functions throughout the working day is a major challenge in both heating and cooling systems [12]. From a performance point of view, some integrated heating systems may present a risk of overheating during the execution of active tasks [13], while some cooling systems appear to cool skin temperature only locally, depending on the location of the cooling actuator [14]. Furthermore, most marketed systems have temperature control devices with limited temperature variation ranges.

3. Hybrid Systems

To overcome the shortcomings of current technologies, the simultaneous use of functional heating or cooling materials with active electrical systems has been explored. In such configurations, phase-change materials (PCMs) combined with electric actuators can significantly optimize temperature distribution and energy consumption [15,16,17]. The hybrid system can even establish self-regulating temperature mechanisms, in which the electrical system, aided by an integrated temperature sensor, is automatically switched on only when the PCM stops adjusting the temperature. Conversely, when the temperature reaches a certain level, allowing the PCM to recover its initial phase and resume temperature readjustment, the electrical system switches off [15,17]. As the thermal regulation capacity of integrated PCM is highly dependent on the amount of material used, inserting large numbers of PCM pockets into clothing generally makes the garment heavy [18], and heavy garments are unsuitable for workers performing active tasks throughout the working day. On the other hand, the microencapsulated PCM coating on the textile surface showed a low thermal effect, while having low resistance to washing [18]. Research efforts are therefore still needed to improve the overall phase change enthalpy and thermal band of PCMs, to achieve a lasting thermal release effect compatible with several hours of continuous activity.

4. Feedback Activated Temperature Control Systems

Another strategy for improving current systems is to implement self-regulation methods. As is the case with currently marketed products, self-control of the electric actuator using an integrated temperature sensor and power switching process can facilitate the achievement of different temperature levels, a uniform temperature distribution band, and the control of the preset temperature [13,19]. As a result, the switch control system, which refers to temperature in real time, will maintain the target temperature in the circuit regardless of weather conditions and battery voltage level [13]. To offer a more customized solution, some experts suggest self-regulating systems with closed-loop skin temperature feedback using thermal sensors such as thermistors placed immediately under each heated or cooled zone, combined with a microcontroller-based control approach [19,20]. The system can adapt the power supplied to maintain the same equivalent skin temperature in the various thermoregulated zones of the garment [19]. In this type of system, the electric thermal actuator is activated when the skin temperature rises or falls above a certain threshold and is then deactivated when the skin temperature reaches an appropriate level [19,20].

Despite the potential comfort offered by this type of system, its use in a thermal stress prevention strategy for workers exposed to extreme temperatures can seem risky, especially since measured skin temperature may be very different from core body temperature. Indeed, the variation in the core and skin temperature is dissimilar, and this difference can be as much as 2.5 °C [21]. Then, the self-regulating mechanism may adjust the system temperature to an optimal skin temperature, while body temperature could evolve towards a thermophysiological stress situation. Not only is skin temperature highly dependent on fluctuations in ambient temperature [22], but skin temperature measured on one part of the body may differ by a few degrees from that measured on other parts [23]. In addition, sweat evaporation can lead to a reduced relationship between core and skin temperature, as well as degraded thermal contact between skin and sensor [22]. In addition to physiological aspects, the type of sensor and method of attachment can greatly influence the results of temperature measurements. Skin condition, air velocity, ambient humidity, sensor dimensions, sensor positioning, and mechanical stress are all factors that can influence skin temperature measurements [24]. Furthermore, for textile-integrated electronics, the performance of integrated thermal sensors can be strongly influenced by the structure of the textile [25]. Figure 1 illustrates a recent exploration of incorporating temperature sensors into textile structures. A few algorithms have been proposed for estimating core body temperature from skin temperature measurements [26,27]. However, these models use average skin temperature measured at different locations as an approximation of core temperature without considering the importance of each sensor measurement and require the deployment of several sensors at different body locations to build the model [28].

5. Sensor Network and Data Exploitation

Beyond the design of integrated retroactive heating and cooling systems, it is possible to use arrays of sensors in both bidirectional segments and disparate distributed individual sensors to feed machine learning algorithms. Instead of establishing a direct correlation between skin and core temperature, sensory data such as activity level, average skin temperature, and ambient temperature can be used to estimate the degree of thermoregulation required to achieve a target skin temperature. Using the correlation between collected skin temperatures and well-known thermal stress indices such as the Physiological Stress Index (PSI), dynamic Bayesian network, and decision tree algorithms, can be used to determine the risk of heat stress. Then, these thermal stress prediction algorithms will be used to trigger thermal actuators in the garment, with the aim of avoiding the occurrence of thermal stress [29]. In addition to Bayesian networks, neural networks, and multilinear regression models that treat the core temperature as a numerical variable, it is possible to consider core temperature as a two-category safety and hazard variable. By transforming the regression problem into a classification, new models could be established by discretizing core temperature in its range of integer units, and then building and learning a Bayesian network on the discretized data [28]. As a result, closed-loop skin temperature feedback self-regulation systems can be tuned to keep core temperature within a safe range. To avoid deploying too many sensors, the correct location of integrated thermal sensors can be decided by performing feature selection on the dataset, while ranking features using a recursive feature selection algorithm according to their importance in core temperature estimation. Different models can be trained with different feature subsets, using a feature selection algorithm such as the Random Forest algorithm to choose the best feature set [28]. Despite the disparity of skin temperature measurements, linear regression analysis via deep neural network architecture can be used to learn to predict core temperature from data collected by the sensor network. This type of approach will not only overcome the difficulties associated with the calibration and accuracy of skin temperature measurements but will also enable the temperature at a given point to be predicted from a reading at another point [30]. Figure 2 showcases an optimized deep neural network architecture employed for learning to predict temperature based on data gathered from an array of temperature sensors. Thanks to algorithms capable of modeling the trends governing a dataset, it will now be possible to optimize measurement accuracy while evaluating the error originating from each sensor in the matrix. By learning the behavior of sensors at different temperature levels, and thus predicting their respective performances, the machine learning algorithm will be able to detect divergent measurements and propose better temperature detection according to the specific compartment of the garment. In this way, temperature control can be tailored to each heating or cooling zone.

6. Conclusions

We have presented an overview of the feasibility of implementing intelligent thermal management systems based on feedback from workers’ personal and environmental parameters. Such an analysis clarifies the gap that needs to be bridged to facilitate the adaptation and integration of these systems with personal protective equipment.