Blood oxygen saturation$(\text{SpO}_{2})$is a vital marker for healthcare monitoring. Traditional$\text{SpO}_{2}$estimation methods often rely on complex clinical calibration, making them unsuitable for low-power, wearable applications. In this paper, we propose a transfer learning-based framework for the rapid adaptation of$\mathrm{SpO}_{2}$estimation to energy-efficient wearable devices using low-sampling-rate (25Hz) dual-channel photoplethysmography (PPG). We first pretrain a bidirectional Long ShortTerm Memory (BiLSTM) model with self-attention on a public clinical dataset, then fine-tune it using data collected from our wearable We-Be band and an FDA-approved reference pulse oximeter. Experimental results show that our approach achieves a mean absolute error (MAE) of 2.967% on the public dataset and 2.624% on the private dataset, significantly outperforming traditional calibration and non-transferred machine learning baselines. Moreover, using 25Hz PPG reduces power consumption by 40% compared to 100 Hz, excluding baseline draw. Our method also attains an MAE of$3.284\%$in instantaneous$\mathrm{SpO}_{2}$prediction, effectively capturing rapid fluctuations. These results demonstrate the rapid adaptation of accurate, low-power$\mathrm{S p O}_{2}$monitoring on wearable devices without the need for clinical calibration.

Accurate and generalizable blood pressure (BP) estimation is vital for the early detection and management of cardiovascular diseases. In this study, we enforce subjectlevel data splitting on a public multi-wavelength photoplethysmography (PPG) dataset and propose a generalizable BP estimation framework based on curriculum-adversarial learning. Our approach combines curriculum learning, which transitions from hypertension classification to BP regression, with domainadversarial training that confuses subject identity to encourage the learning of subject-invariant features. Experiments show that multi-channel fusion consistently outperforms single-channel models. On the four-wavelength PPG dataset, our method achieves strong performance under strict subject-level splitting, with mean absolute errors (MAE) of 14.2mmHg for systolic blood pressure (SBP) and 6.4mmHg for diastolic blood pressure (DBP). Additionally, ablation studies validate the effectiveness of both the curriculum and adversarial components. These results highlight the potential of leveraging complementary information in multi-wavelength PPG and curriculum-adversarial strategies for accurate and robust BP estimation.

Wearable photoplethysmography (PPG) is embedded in billions of devices, yet its optical waveform is easily corrupted by motion, perfusion loss, and ambient light—jeopardizing downstream cardiometric analytics. Existing signal-quality assessment (SQA) methods rely either on brittle heuristics or on data-hungry supervised models. We introduce the first fully unsupervised SQA pipeline for wrist PPG. Stage 1 trains a contrastive 1-D ResNet-18 on 276 h of raw, unlabeled data from heterogeneous sources (varying in device and sampling frequency), yielding optical-emitter- and motioninvariant embeddings (i.e., the learned representation is stable across differences in LED wavelength, drive intensity, and device optics, as well as wrist motion). Stage 2 converts each 512-D encoder embedding into a 4-D topological signature via persistent homology (PH) and clusters these signatures with HDBSCAN. To produce a binary signal-quality index (SQI), the acceptable PPG signals are represented by the densest cluster while the remaining clusters are assumed to mainly contain poor-quality PPG signals. Without re-tuning, the SQI attains Silhouette, Davies-Bouldin, and Calinski-Harabasz scores of$0.72,0.34$, and 6,173, respectively, on a stratified sample of 10,000 windows. In this study, we propose a hybrid self-supervised-learning-topological-dataanalysis (SSL-TDA) framework that offers a drop-in, scalable, cross-device quality gate for PPG signals.

Data reliability and algorithm robustness are both important for wearable devices. To validate the accuracy of a recently published research vehicle, WeBe band, we conducted a concurrent heart rate (HR) and galvanic skin response (GSR) validity study. WeBe band, Empatica E4 and MindWare, which is currently considered the gold standard for collecting these measures, are compared concurrently. Fifty healthy adult partic-ipants volunteered (female n=29, 49 in 18–25 age range, 1 in 26–30 age range; [mean (SD)]: height = 167.6 (8.9) cm, mass = 150.1 (33.1) lbs). Participants wore the WeBe band and the Empatica band on opposite wrists (alternating device placement between participants) and the MindWare electrodes were placed on the on chest, back, and palms. Each participant completed a study session (a total 51 minutes) that included sitting, standing, normal paced walking and faster paced walking. Data was processed and validity was measured though: mean absolute percent error (MAPE), Bland-Altman limits of aggreement (LOA) and concordance coefficient (rc). Results showed that WeBe band is valid under all conditions.

This paper presents a novel Electrodermal Activ-ity (EDA) signal acquisition system, designed to address the challenges of stress monitoring in contemporary society, where stress affects one in four individuals. Our system focuses on enhancing the accuracy and efficiency of EDA measurements, a reliable indicator of stress. Traditional EDA monitoring solutions often grapple with trade-offs between sensor placement, cost, and power consumption, leading to compromised data accuracy. Our innovative design incorporates an adaptive gain mechanism, catering to the broad dynamic range and high-resolution needs of EDA data analysis. The performance of our system was extensively tested through simulations and a custom Printed Circuit Board (PCB), achieving an error rate below 1 % and maintaining power consumption at a mere$\mathbf{700}\mu \mathbf{A}$under a 3.$7\mathbf{V}$power supply. This research contributes significantly to the field of wearable health technology, offering a robust and efficient solution for long-term stress monitoring.

In the realm of physiological-based affective computing, significant progress has been witnessed in machine learning over the last two decades. Nevertheless, the lack of consistency in measurement tools and data organization across diverse datasets poses a challenge when integrating new datasets for algorithm testing, research, and result comparison across multiple datasets. Despite the expansion of artificial intelligence-driven affective computing, a notable gap remains in the form of a comprehensive toolkit tailored for both machine learning researchers and psychologists who are new to the field of machine learning. In response to these challenges, we introduce a Python toolkit designed to fulfill two key roles: establishing a standardized benchmark for affective computing datasets and offering an all-encompassing toolkit for machine learning in physiological signal based affective computing. This toolkit encompasses vital components essential to the machine learning process, encompassing tasks like dataset integration and interpretation, signal preprocessing, feature derivation, post-processing, classification models, and evaluation metrics. Our proposed toolkit is designed for working with seven publicly available datasets, embracing five different modalities and incorporating twenty diverse machine learning models spanning from conventional options like the support vector machine (SVM) to cutting-edge deep learning models. To the best of our knowledge, the proposed toolkit stands as the pioneering initiative for creating a standardized dataset benchmarking system and a comprehensive solution tailored for machine learning applications in affective computing. The open-source codebase for the proposed toolkit is accessible via https://github.com/rjfang/pyAffeCT.

In modern society, people are exposed to various stressors and negative emotions daily and they may cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, this paper delves into the potential of modern wearable technologies as a tool for real-time health monitoring. The advent of ubiquitous sensing has ushered in an era where physiological and behavioral measurements can be continuously recorded in daily life. One significant physiological marker is the Galvanic Skin Response (GSR), which exhibits noteworthy changes under different emotional states. We propose a machine learning-based emotion recognition framework. It includes a preprocessing stage that eliminates noise and extracts 87 features from the GSR data. To account for individual differences in physiological responses, we also introduce a novel normalization procedure per subject. Finally, a subset of dominant and discriminative features enhances the proposed framework’s performance. We conducted experiments on two datasets, the wearable stress and affect detection dataset (WESAD) for stress detection, and the multimodal MAHNOB-HCI dataset for emotion recognition. The results show that the Leave-One-Out method is capable of detecting stress with 97.03% accuracy. Moreover, the proposed method classifies arousal and valence with an accuracy of 82.20% and 82.57%, respectively.

Physiological signal-based pattern recognition has progressed significantly, such as automated pain assessment and stress detection. Public datasets provide a research platform to conduct machine learning studies. However, models trained from public datasets easily overfit that specific dataset and do not apply to unseen data collected in real-life scenarios. This paper proposes to use the transfer learning-based domain generalization technique to generalize the models to solve this issue. Data from different training domains are generalized, i.e., the dissimilarity is minimized by the proposed approach such that the model trained is generalized. We proved that the generalized model is more adaptive to new unseen data. Experiments have been done on the BioVid heat pain dataset and WESAD stress dataset, and results showed that our proposed methods significantly improve the model performance on new unseen data.

Stress detection is an emerging field. WESAD is a commonly used public dataset for automated stress detection. It contains physiological signals including ECG, EDA, EMG, ACC, BVP, EDA, and skin temperature. The time window approach is used to extract features from time-series physiological signals. We find in previous studies that a 60-second time window with a 0.25-second window shift is widely used, but such window settings may cause redundancy and over-fitting. Thus, we propose to use (1) new window settings and (2) normalization per subject to tackle this problem. The experiment results show that our proposed methods significantly increase the classification performance.

Stress is an inevitable part of our lives in modern society since in many situations people are exposed to various stressors daily. According to studies, long-term stress can cause mental and physical diseases such as depression, anxiety, high blood pressure, heart attacks, and stroke. Therefore, stress detection is one of the crucial areas of study to maintain a healthy life. Recently, by developing commercial wearable technologies, real-time and continuous data collection for personal stress monitoring becomes more feasible. Under stress conditions, there are notable changes in physiological signals such as heart rate, respiration, perspiration, and eye pupil dilation. Previous studies have shown that Electrodermal Activity (EDA), also known as Galvanic Skin Response (GSR), can identify stress. EDA measures changes in perspiration by detecting the changes in the electrical conductivity of the skin. This paper focuses on stress detection using only EDA wearable sensors and applied machine learning techniques. First, 87 different features are extracted from EDA signals. Then, the data are normalized per subject because of differences in individuals’ physiological responses. Finally, five dominant features in stress detection are selected. We used a publicly available dataset, namely, the wearable stress and affect detection dataset (WESAD) in this study. The results show that the One-Leave-Out method is capable of detecting stress with 97.03% accuracy.