New medical technologies can transform healthcare, and automation of processes is becoming increasingly ubiquitous within the patient care sector. Many innovative ideas arise from academia, but regulations need to be taken into account if they want to reach the market and create a real impact. This is particularly relevant for applied fields, such as prosthetics, which continuously generates cutting-edge solutions. However, it remains unclear how well the regulatory pathway is supported within universities. This study applied a data-driven assessment of available online information regarding support of medical device regulations within universities. A total of 109,200 URLs were screened for regulatory information associated with universities in the UK and the USA. The results show that based on available online data, 55% of the selected universities in the UK and 35% in the USA did not provide any support for medical device regulations. There is a big discrepancy between universities in terms of the available support, as well as the kind of information that is made accessible by the academic institutes. It is suggested that increasing support for regulatory strategies during the early phases of research and development will likely yield a better translation of technologies into clinical care. Universities can play a more active role in this.

Objective This study aimed to determine the steady-state errors of oral-based temperature sensors, that are embedded in mouthguards, using a robust assessment process. Materials and methods Four electronic boards with temperature sensors were encapsulated in mouthguards made from ethylene-vinyl acetate (EVA). The error and time to reach steady-state temperature were determined using a thermostatic water bath during three different conditions (34, 38.5 and 43 °C). Subsequently, a case study of one volunteer wearing the instrumented mouthguard is presented. Results The water bath tests showed that a mean absolute error of 0.2 °C was reached after a maximum of 690 s across all test conditions. The case study yielded an absolute error was 0.2 °C after 1110 s. Conclusion These results show that an instrumented mouthguard with temperature sensing capabilities can yield a consistent steady-state error that is close to the clinical requirements across a range of temperatures. However, the time it takes to reach steady-state temperature needs to be considered for these systems to correctly interpret the outcomes.

Reliable monitoring of one’s response to exercise intensity is imperative to effectively plan and manage training, but not always practical in impact sports settings. This study aimed to evaluate if an inexpensive mobile cardio-respiratory monitoring system can achieve similar performance to a metabolic cart in estimating rated perceived exertion. Eight adult men volunteered to perform treadmill tests under different conditions. Cardiorespiratory data were collected using a metabolic cart and an instrumented oral-cavity device, as well as their ratings of perceived exertion. Pearson correlation corrected for repeated measurements and stepwise regression analysis were used to observe the relationship between the cardiorespiratory features and the ratings of perceived exertion and determine the proportion of the variance of exertion that could be explained by the measurements. Minute ventilation was found to be the most associated variable to perceived exertion, closely followed by a novel metric called the audio minute volume, which can be collected by the oral-cavity device. A generalised linear model combining minute ventilation, audio minute volume, heart rate and respiration rate accounted for 64% of the variance in perceived exertion, whilst a model with only audio minute volume accounted for 56%. Our study indicates that minute ventilation is key to estimating perceived exertion during indoor running exercises. Audio minute volume was also observed to perform comparably to a lab-based metabolic cart in estimating perceived exertion. This research indicates that mobile techniques offer the potential for real-world data collection of an athlete’s physiological load and estimation of perceived exertion.