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1. Moisture Based Perspiration Level Estimation

   https://doi.org/10.1145/3267305.3274177  

   Jia, Ji; Xu, Chengtian; Pan, Shijia; Xia, Stephen; Wei, Peter; Noh, Hae Young; Zhang, Pei; Jiang, Xiaofan (October 2018, Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers)

   null (Ed.)

   Perspiration level monitoring enables numerous applications such as physical condition estimation, personal comfort monitoring, health/exercise monitoring, and inference of environmental conditions of the user. Prior works on perspiration (sweat) sensing require users to manually hold a device or attach adhesive sensors directly onto their skin, limiting user mobility and comfort. In this paper, we present a low-cost and novel wearable sensor system that is able to accurately estimate an individual's sweat level based on measuring moisture. The sensor is designed in a threadlike form factor, allowing it to be sewn into the seams of clothing, rather than having to act as a standalone sensor that the user must attach to their body. The system is comprised of multiple cotton-covered conductive threads that are braided into one sensor. When a person sweats, the resistance between the braided conductive threads changes as moisture becomes trapped in the cotton covering of the threads. The braided three-dimensional structure allows for robust estimation of perspiration level in the presence of external forces that may cause sensor distortion, such as motion. We characterize the relationship between the volume of sweat and measured resistance between the braided threads. Finally, we weave our sensors into the fabric of a shirt and conduct on-body experiments to study users' sweating level through various activities. 

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2. Learning-Based Frequency Estimation Algorithms.

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3. Clustering-based inter-regional correlation estimation

   https://doi.org/10.1016/j.csda.2023.107876  

   Lbath, Hanâ; Petersen, Alexander; Meiring, Wendy; Achard, Sophie (March 2024, Computational Statistics & Data Analysis)

   A novel non-parametric estimator of the correlation between grouped measurements of a quantity is proposed in the presence of noise. The main motivation is functional brain network construction from fMRI data, where brain regions correspond to groups of spatial units, and correlation between region pairs defines the network. The challenge resides in the fact that both noise and intra-regional correlation lead to inconsistent inter-regional correlation estimation using classical approaches. While some existing methods handle either one of these issues, no nonparametric approaches tackle both simultaneously. To address this problem, a trade-off between two procedures is proposed: correlating regional averages, which is not robust to intra-regional correlation; and averaging pairwise inter-regional correlations, which is not robust to noise. To that end, the data is projected onto a space where Euclidean distance is used as a proxy for sample correlation. Hierarchical clustering is then leveraged to gather together highly correlated variables within each region prior to inter-regional correlation estimation. The convergence of the proposed estimator is analyzed, and the proposed approach is empirically shown to surpass several other popular methods in terms of quality. Illustrations on real-world datasets that further demonstrate its effectiveness are provided. 

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4. Neural Network-based Estimation of the MMSE

   https://doi.org/10.1109/ISIT45174.2021.9518063  

   Diaz, Mario; Kairouz, Peter; Liao, Jiachun; Sankar, Lalitha (October 2021, International Symposium on Information Theory)

   null (Ed.)

   The minimum mean-square error (MMSE) achievable by optimal estimation of a random variable S given another random variable T is of much interest in a variety of statistical contexts. Motivated by a growing interest in auditing machine learning models for unintended information leakage, we propose a neural network-based estimator of this MMSE. We derive a lower bound for the MMSE based on the proposed estimator and the Barron constant associated with the conditional expectation of S given T . Since the latter is typically unknown in practice, we derive a general bound for the Barron constant that produces order optimal estimates for canonical distribution models. 

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5. Deep-Learning Based Estimation of Dielectrophoretic Force

   https://doi.org/10.3390/mi13010041  

   Ajala, Sunday; Jalajamony, Harikrishnan; Fernandez, Renny (January 2022, Micromachines)

   The ability to accurately quantify dielectrophoretic (DEP) force is critical in the development of high-efficiency microfluidic systems. This is the first reported work that combines a textile electrode-based DEP sensing system with deep learning in order to estimate the DEP forces invoked on microparticles. We demonstrate how our deep learning model can process micrographs of pearl chains of polystyrene (PS) microbeads to estimate the DEP forces experienced. Numerous images obtained from our experiments at varying input voltages were preprocessed and used to train three deep convolutional neural networks, namely AlexNet, MobileNetV2, and VGG19. The performances of all the models was tested for their validation accuracies. Models were also tested with adversarial images to evaluate performance in terms of classification accuracy and resilience as a result of noise, image blur, and contrast changes. The results indicated that our method is robust under unfavorable real-world settings, demonstrating that it can be used for the direct estimation of dielectrophoretic force in point-of-care settings. 

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