Background: For many applications in research, material development and testing, physical skin models are preferable to the use of human skin, because more reliable and reproducible results can be obtained.Purpose: This article gives an overview of materials applied to model physical properties of human skin to encourage multidisciplinary approaches for more realistic testing and improved understanding of skin–material interactions.Methods: The literature databases Web of Science, PubMed and Google Scholar were searched using the terms ‘skin model’, ‘skin phantom’, ‘skin equivalent’, ‘synthetic skin’, ‘skin substitute’, ‘artificial skin’, ‘skin replica’, and ‘skin model substrate.’ Articles addressing material developments or measurements that include the replication of skin properties or behaviour were analysed.Results: It was found that the most common materials used to simulate skin are liquid suspensions, gelatinous substances, elastomers, epoxy resins, metals and textiles. Nano‐ and micro‐fillers can be incorporated in the skin models to tune their physical properties.Conclusion: While numerous physical skin models have been reported, most developments are research field‐specific and based on trial‐and‐error methods. As the complexity of advanced measurement techniques increases, new interdisciplinary approaches are needed in future to achieve refined models which realistically simulate multiple properties of human skin.

The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems represent highly promising solutions for fatigue monitoring as they enable continuous, long-term monitoring of biomedical signals in unattended settings, with the required comfort and non-intrusiveness. This is a p rerequisite for the development of accurate models for fatigue monitoring in real-time. However, monitoring fatigue through wearable devices imposes unique challenges. To provide an overview of the current state-of-the-art in monitoring variables associated with fatigue via wearables and to detect potential gaps and pitfalls in current knowledge, a systematic review was performed. The Scopus and PubMed databases were searched for articles published in English since 2015, having the terms “fatigue,” “drowsiness,” “vigilance,” or “alertness” in the title, and proposing wearable device-based systems for non-invasive fatigue quantification. Of the 612 retrieved articles, 60 satisfied the inclusion criteria. Included studies were mainly of short duration and conducted in laboratory settings. In general, researchers developed fatigue models based on motion (MOT), electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), galvanic skin response (GSR), electromyogram (EMG), skin temperature (T_sk), eye movement (EYE), and respiratory (RES) data acquired by wearable devices available in the market. Supervised machine learning models, and more specifically, binary classification models, are predominant among the proposed fatigue quantification approaches. These models were considered to perform very well in detecting fatigue, however, little effort was made to ensure the use of high-quality data during model development. Together, the findings of this review reveal that methodological limitations have hindered the generalizability and real-world applicability of most of the proposed fatigue models. Considerably more work is needed to fully explore the potential of wearables for fatigue quantification as well as to better understand the relationship between fatigue and changes in physiological variables.

Non-invasive, multi-parameter methods to estimate core body temperature offer several advantages for monitoring thermal strain, although further work is required to identify the most relevant predictor measures. This study aimed to compare the validity of an existing and two novel multi-parameter rectal temperature prediction models. Thirteen healthy male participants (age 30.9 ± 5.4 years) performed two experimental sessions. The experimental procedure comprised 15 min baseline seated rest (23.2 ± 0.3°C, 24.5 ± 1.6% relative humidity), followed by 15 min seated rest and cycling in a climatic chamber (35.4 ± 0.2°C, 56.5 ± 3.9% relative humidity; to +1.5°C or maximally 38.5°C rectal temperature, duration 20–60 min), with a final 30 min seated rest outside the chamber. In session 1, participants exercised at 75% of their heart rate maximum (HR max) and wore light athletic clothing (t-shirt and shorts), while in session 2, participants exercised at 50% HR max, wearing protective firefighter clothing (jacket and trousers). The first new prediction model, comprising the input of 18 non-invasive measures, i.e., insulated and non-insulated skin temperature, heat flux, and heart rate (“Max-Input Model”, standard error of the estimate [SEE] = 0.28°C, R² = 0.70), did not exceed the predictive power of a previously reported model which included six measures and no insulated skin temperatures (SEE = 0.28°C, R² = 0.71). Moreover, a second new prediction model that contained only the two most relevant parameters (heart rate and insulated skin temperature at the scapula) performed similarly (“Min-Input Model”, SEE = 0.29, R² = 0.68). In conclusion, the “Min-Input Model” provided comparable validity and superior practicality (only two measurement parameters) for estimating rectal temperature versus two other models requiring six or more input measures.

Background: Skin temperature (T_skin) is commonly measured using T_skin sensors affixed directly to the skin surface, although the influence of setup variables on the measured outcome requires clarification. Objectives: The two distinct objectives of this systematic review were (1) to examine measurements from contact T_skin sensors considering equilibrium temperature and temperature disturbance, sensor attachments, pressure, environmental temperature, and sensor type, and (2) to characterise the contact T_skin sensors used, conditions of use, and subsequent reporting in studies investigating sports, exercise, and other physical activity. Data sources and study selection: For the measurement comparison objective, Ovid Medline and Scopus were used (1960 to July 2016) and studies comparing contact T_skin sensor measurements in vivo or using appropriate physical models were included. For the survey of use, Ovid Medline was used (2011 to July 2016) and studies using contact temperature sensors for the measurement of human T_skin in vivo during sport, exercise, and other physical activity were included. Study appraisal and synthesis methods: For measurement comparisons, assessments of risk of bias were made according to an adapted version of the Cochrane Collaboration's risk of bias tool. Comparisons of temperature measurements were expressed, where possible, as mean difference and 95% limits of agreement (LoA). Meta-analyses were not performed due to the lack of a common reference condition. For the survey of use, extracted information was summarised in text and tabular form. Results: For measurement comparisons, 21 studies were included. Results from these studies indicated minor (0.5°C) measurement bias within the subgroups of attachment type, applied pressure, environmental conditions, and sensor type. The 95% LoA were often within 1.0°C for in vivo studies and 0.5°C for physical models. For the survey of use, 172 studies were included. Details about T_skin sensor setup were often poorly reported and, from those reporting setup information, it was evident that setups widely varied in terms of type of sensors, attachments, and locations used. Conclusions: Setup variables and conditions of use can influence the measured temperature from contact T_skin sensors and thus key setup variables need to be appropriately considered and consistently reported.