An official website of the United States government
Abstract The erratic, intermittent, and unpredictable nature of sweat production, resulting from physiological or psychological fluctuations, poses intricacies to consistently and accurately sample and evaluate sweat biomarkers. Skin‐interfaced microfluidic devices that rely on colorimetric mechanisms for semi‐quantitative detection are particularly susceptible to these inaccuracies due to variations in sweat secretion rate or instantaneous volume. This work introduces a skin‐interfaced colorimetric bifluidic sweat device with two synchronous channels to quantify sweat rate and biomarkers in real‐time, even during uncertain sweat activities. In the proposed bifluidic‐distance metric approach, with one channel to measure sweat rate and quantify collected sweat volume, the other channel can provide an accurate analysis of the biomarkers based on the collected sweat volume. The closed channel design also reduces evaporation and resists contamination from the external environment. The feasibility of the device is highlighted in a proof‐of‐the‐concept demonstration to analyze sweat chloride for evaluating hydration status and sweat glucose for assessing glucose levels. The low‐cost yet highly accurate device provides opportunities for clinical sweat analysis and disease screening in remote and low‐resource settings. The developed device platform can be facilely adapted for the other biomarkers when corresponding colorimetric reagents are exploited.
more »
« less
Physiological characterization of electrodermal activity enables scalable near real-time autonomic nervous system activation inference
Electrodermal activities (EDA) are any electrical phxenomena observed on the skin. Skin conductance (SC), a measure of EDA, shows fluctuations due to autonomic nervous system (ANS) activation induced sweat secretion. Since it can capture psychophysiological information, there is a significant rise in the research work for tracking mental and physiological health with EDA. However, the current state-of-the-art lacks a physiologically motivated approach for real-time inference of ANS activation from EDA. Therefore, firstly, we propose a comprehensive model for the SC dynamics. The proposed model is a 3D state-space representation of the direct secretion of sweat via pore opening and diffusion followed by corresponding evaporation and reabsorption. As the input to the model, we consider a sparse signal representing the ANS activation that causes the sweat glands to produce sweat. Secondly, we derive a scalable fixed-interval smoother-based sparse recovery approach utilizing the proposed comprehensive model to infer the ANS activation enabling edge computation. We incorporate a generalized-cross-validation to tune the sparsity level. Finally, we propose an Expectation-Maximization based deconvolution approach for learning the model parameters during the ANS activation inference. For evaluation, we utilize a dataset with 26 participants, and the results show that our comprehensive state-space model can successfully describe the SC variations with high scalability, showing the feasibility of real-time applications. Results validate that our physiology-motivated state-space model can comprehensively explain the EDA and outperforms all previous approaches. Our findings introduce a whole new perspective and have a broader impact on the standard practices of EDA analysis.
more »
« less
- PAR ID:
- 10409024
- Editor(s):
- Berry, Hugues
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 18
- Issue:
- 7
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1010275
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Sweat secretion and evaporation from the skin dictate the human ability to thermoregulate and thermal comfort in hot environments and impact skin interactions with cosmetics, textiles and wearable electronics/sensors. However, sweating has mostly been investigated using macroscopic physiological methods, leaving micro-to-macroscale sweating dynamics unexplored. We explore these processes by using a coupled micro-imaging and transport measurement approach used in engineering studies of phase change processes. Specifically, we used a comprehensive set of ‘macroscale’ physiological measurements (ventilated capsule sweat rate (SR), galvanic skin conductance and dielectric epidermis hydration) complemented by three microscale imaging techniques (visible light, midwave infrared and optical coherence tomography imaging). Inspired by industrial jet cooling devices, we also explore an ‘air jet’ (versus cylindrical) capsule for measuring SR. To enable near-simultaneous application of these methods, we studied forehead sweating dynamics of six supine subjects undergoing passive heating, cooling and secondary heating. The relative dynamics of the physiological measurements agree with prior observations and can be explained using imaged microscale sweating dynamics. This comprehensive study provides new insights into the biophysical dynamics of sweating onset and following cyclic porewise, transition and filmwise sweating modes and highlights the roles of stratum corneum hydration, salt deposits and microscale hair.more » « less
-
Abstract We introduce a lifted $$\ell _1$$ (LL1) regularization framework for the recovery of sparse signals. The proposed LL1 regularization is a generalization of several popular regularization methods in the field and is motivated by recent advancements in re-weighted $$\ell _1$$ approaches for sparse recovery. Through a comprehensive analysis of the relationships between existing methods, we identify two distinct types of lifting functions that guarantee equivalence to the $$\ell _0$$ minimization problem, which is a key objective in sparse signal recovery. To solve the LL1 regularization problem, we propose an algorithm based on the alternating direction method of multipliers and provide proof of convergence for the unconstrained formulation. Our experiments demonstrate the improved performance of the LL1 regularization compared with state-of-the-art methods, confirming the effectiveness of our proposed framework. In conclusion, the LL1 regularization presents a promising and flexible approach to sparse signal recovery and invites further research in this area.more » « less
-
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. We develop a novel online learning framework, leveraging variational inference and sequential Monte Carlo, which enables flexible and accurate Bayesian joint filtering. Our method provides an approximation of the filtering posterior which can be made arbitrarily close to the true filtering distribution for a wide class of dynamics models and observation models. Specifically, the proposed framework can efficiently approximate a posterior over the dynamics using sparse Gaussian processes, allowing for an interpretable model of the latent dynamics. Constant time complexity per sample makes our approach amenable to online learning scenarios and suitable for real-time applications.more » « less
-
Multi-output Gaussian process (GP) regression has been widely used as a flexible nonparametric Bayesian model for predicting multiple correlated outputs given inputs. However, the cubic complexity in the sample size and the output dimensions for inverting the kernel matrix has limited their use in the large-data regime. In this paper, we introduce the factorial stochastic differential equation as a representation of multi-output GP regression, which is a factored state-space representation as in factorial hidden Markov models. We propose a structured mean-field variational inference approach that achieves a time complexity linear in the number of samples, along with its sparse variational inference counterpart with complexity linear in the number of inducing points. On simulated and real-world data, we show that our approach significantly improves upon the scalability of previous methods, while achieving competitive prediction accuracy.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1010275
