Carbon dioxide (CO${}_2$) monitoring in human subjects is of crucial importance in medical practice. Transcutaneous monitors based on the Stow-Severinghaus electrode make a good alternative to the painful and risky arterial “blood gases” sampling. Yet, such monitors are not only expensive, but also bulky and continuously drifting, requiring frequent recalibrations by trained medical staff. Aiming at finding alternatives, the full panel of CO${}_2$ measurement techniques is thoroughly reviewed. The physicochemical working principle of each sensing technique is given, as well as some typical merit criteria, advantages, and drawbacks. An overview of the main CO${}_2$ monitoring methods and sites routinely used in clinical practice is also provided, revealing their constraints and specificities. The reviewed CO${}_2$ sensing techniques are then evaluated in view of the latter clinical constraints and transcutaneous sensing coupled to a dye-based fluorescence CO${}_2$ sensing seems to offer the best potential for the development of a future non-invasive clinical CO${}_2$ monitor.

The accurate determination of blood gases—namely dioxygen (${\mathrm{O}}_2$) and carbon dioxide (${\mathrm{CO}}_2$)—is of crucial importance in medical care since it gives circulatory as well as ventilatory clues on the state of a patient.¹ In particular, in case of acute respiratory failure, chronic obstructive pulmonary disease or cystic fibrosis, the monitoring of parameters such as the arterial blood dioxygen saturation (${\mathrm{SatO}}_2$) and ${\mathrm{CO}}_2$ arterial pressure (${\mathrm{Pa}}_{{\mathrm{CO}}_2}$) can strongly affect patient handling and outcome.^{2 – 4}

Objective: present transcutaneous carbon dioxide (CO₂)—tcpCO₂—monitors suffer from limitations which hamper their widespread use, and call for a new tcpCO₂ measurement technique. However, the progress in this area is hindered by the lack of knowledge in transcutaneous CO₂ diffusion. To address this knowledge gap, this study focuses on investigating the influence of skin temperature on two key skin properties: CO₂ permeability and skin blood flow.Methods: a monocentric prospective exploratory study including 40 healthy adults was undertaken. Each subject experienced a 90 min visit split into five 18 min sessions at different skin temperatures—Non-Heated (NH), 35, 38, 41, and 44°C. At each temperature, custom sensors measured transcutaneous CO₂ conductivity and exhalation rate at the arm and wrist, while Laser Doppler Flowmetry (LDF) assessed skin blood flow at the arm.Results: the three studied metrics sharply increased with rising skin temperature. Mean values increased from the NH situation up to 44°C from 4.03 up to 8.88 and from 2.94 up to 8.11 m·s⁻¹ for skin conductivity, and from 80.4 up to 177.5 and from 58.7 up to 162.3 cm³·m⁻²·h⁻¹ for exhalation rate at the arm and wrist, respectively. Likewise, skin blood flow increased elevenfold for the same temperature increase. Of note, all metrics already augmented significantly in the 35–38°C skin temperature range, which may be reached without active heating—i.e. only using a warm clothing.Conclusion: these results are extremely encouraging for the development of next-generation tcpCO₂ sensors. Indeed, the moderate increase (× 2) in skin conductivity from NH to 44°C tends to indicate that heating the skin is not critical from a response time point of view, i.e. little to no skin heating would only result in a doubled sensor response time in the worst case, compared to a maximal heating at 44°C. Crucially, a skin temperature within the 35–38°C range already sharply increases the skin blood flow, suggesting that tcpCO₂ correlates well with the arterial paCO₂ even at such low skin temperatures. These two conclusions further strengthen the viability of non-heated tcpCO₂ sensors, thereby paving the way for the development of wearable transcutaneous capnometers.

Accurate phase extraction from sinusoidal signals is a crucial task in various signal processing applications. While prior research predominantly addresses the case of asynchronous sampling with unknown signal frequency, this study focuses on the more specific situation where synchronous sampling is possible, and the signal’s frequency is known. In this framework, a comprehensive analysis of phase estimation accuracy in the presence of both additive and phase noises is presented. A closed-form expression for the asymptotic Probability Density Function (PDF) of the resulting phase estimator is validated by simulations depicting Root Mean Square Error (RMSE) trends in different noise scenarios. This estimator is asymptotically efficient, converging rapidly to its Cramèr-Rao Lower Bound (CRLB). Three distinct RMSE behaviours were identified based on SNR, sample count (N), and noise level: (i) saturation towards a random guess at low Signal to Noise Ratio (SNR), (ii) linear decrease with the square roots of N and SNR at moderate noise levels, and (iii) saturation at high SNR towards a noise floor dependent on the phase noise level. By quantifying the impact of sample count, additive noise, and phase noise on phase estimation accuracy, this work provides valuable insights for designing systems requiring precise phase extraction, such as phase-based fluorescence assays or system identification.