Subjective well-being (SWB) is vital for the personal growth of university students. Machine learning approach have been increasingly used in identifying SWB predictors for their ability to capture complex and multidimensional predictors. Still, the feature selection is not often justified from a theoretical perspective. Under the guidance of the conceptual model of psychology and public health, this study aims to apply machine learning to identify the top predictors of happiness and life satisfaction (LS) as the two components of SWB among a sample of university students. This cross-sectional study analyzed university students from the China Family Panel Studies, including 816 participants from the 2022 wave for model development and 724 from the 2020 wave for external validation. The development set was randomly split into a training set (70

This dataset was collected from first-generation immigrants between September 2022 and June 2023. Over a 28-day period, 39 participants aged 18 to 65, fluent in English and experiencing loneliness (UCLA Loneliness Scale score ≥ 28) contributed to the study. Data collection utilized Samsung Watch Active 2, Oura Ring, AWARE, and Centralive smartphone application. This dataset contains raw data from photoplethysmogram (PPG), inertial measurement unit (IMU) readings, air pressure, and processed data on heart rate, heart rate variability, sleep metrics (bedtime, stages, quality), physical activity (steps, active calories, activity types), and smartphone usage patterns (screen time, notifications, call and message logs). Participants also completed ecological momentary assessments (EMA) and weekly surveys, including instruments like the Beck Depression Inventory (BDI), Patient Health Questionnaire-9 (PHQ-9), Perceived Stress Scale, Sense of Coherence Scale, Social Connectedness Scale, Twente Engagement with E-Health Technologies questionnaire, and the UCLA Loneliness Scale. This dataset can be used to study the interplay between loneliness, mental well-being, and daily behaviors of immigrants in a real-world context.

BackgroundLoneliness is a distressing feeling that influences well-being. Immigrants' experience of acculturation to a new dominant culture places them at risk for maladaptive behaviors and daily rhythms leading to loneliness. Identifying daily-living features that causally influence loneliness is essential for developing effective preventive mental health screening.ObjectiveTo identify the important daily living-features related to loneliness for the development of robust screening solutions using causal machine learning for health providers working with first-generation immigrants.MethodsWe monitored 39 immigrants in Finland for 28 days using mobile devices and wearables under free-living conditions. Data included ecological momentary assessments of loneliness, social interactions, physical activity, sleep, and cardiac features. We estimated the average treatment effect (ATE) of each daily-living feature (treatment variable) on loneliness scores (outcome) and validated the robustness of causal estimates using three refutation techniques.ResultsOur results reveal the ATE of various daily-living features on loneliness. Features such as longer outgoing call durations (ATE = 0.197, p < 0.001), higher LF/HF ratio (ATE = 0.129, p < 0.0001), higher respiratory rate (ATE = 0.144, p < 0.001), and increased inactivity (ATE = 0.130, p < 0.001) causally increased loneliness. Conversely, certain features exhibit negative ATEs, such as higher activity calories (ATE = -0.174, p < 0.001), sleep RMSSD (ATE = -0.128, p < 0.001), longer home duration (ATE = -0.107, p < 0.001), and more sleep time (ATE = -0.103, p < 0.001) mitigated loneliness.ConclusionsDaily-living features, including social interactions, activity, sleep, and cardiac features, causally influence loneliness. Our findings provide a basis for loneliness screening targeting immigrant populations. Future work should refine the measurement and incorporate contextual information to establish more reliable causal links in real life.

Background: Rates of loneliness have risen sharply since the onset of the COVID-19 pandemic, largely due to disruptions in social relationships and daily routines, with college students experiencing some of the greatest increases. While prevention programs targeting loneliness have been developed, their success has been limited. One promising approach may lie in enhancing the quality of existing relationships rather than simply increasing social interactions during periods of acute loneliness. Relational savoring, an intervention rooted in attachment theory and positive psychology, aims to deepen feelings of connection by encouraging individuals to reflect on positive interpersonal experiences. Objective: This study aimed to evaluate the feasibility, acceptability, and preliminary outcomes of a mobile health adaptation of relational savoring, termed mSavorUs (developed by Amir Rahmani), designed to prevent and reduce loneliness among college students. Methods: A randomized controlled pilot study was conducted with a diverse sample of 29 college students (43.3% Latinx, 40% Asian American, and 16.7% White). The intervention leveraged a smart ring, smartwatch, and smartphone app to enable just-in-time delivery of relational savoring prompts, alongside continuous monitoring of loneliness-related indicators (eg, physiological activity, sleep, and behavior). Aim 1 involved a thematic analysis of participant feedback regarding the utility, benefits, and challenges of both mSavorUs and the monitoring tools. Aim 2 examined the intervention's effects on loneliness and perceived connectedness. Results: For aim 1, qualitative findings suggested that participants found the content of mSavorUs (developed by Amir Rahmani) rewarding and helpful; however, the timing of the intervention was often experienced as disruptive. For aim 2, quantitative analyses revealed no significant reductions in loneliness or increases in connectedness, indicating the need for adjustments to the intervention delivery method. Conclusions: Although participants found the intervention content valuable, the just-in-time delivery format may have limited its effectiveness. Future iterations should consider alternative timing or delivery strategies to maximize program benefits.

BackgroundDepression is the top contributor to global disability. Early detection of depression and depressive symptoms enables timely intervention and reduces their physical and social consequences. Prevalence estimates of depression approach 30% among college students. Passive, device-based sensing further enables detection of depressive symptoms at a low burden to the individual. ObjectiveWe leveraged an ensemble machine learning method (light gradient boosting machine) to detect depressive symptoms entirely through passive sensing. MethodsA diverse sample of undergraduate students (N=28; mean age 19.96, SD 1.23 y; 15/28, 54% women; 13/28, 46% Latine; 10/28, 36% Asian; 4/28, 14% non-Latine White; 11/28, 4% other) participated in an intensive longitudinal study. Participants wore 2 devices (an Oura ring for sleep and physiology data, and a Samsung smartwatch for physiology and movement data) and installed the AWARE software on their mobile devices, which collects passive sensing data such as screen time. Participants were derived from a randomized controlled trial of a positive psychology mobile health intervention. They completed a self-report measure of depressive symptoms administered weekly over a 19- to 22-week period. ResultsThe light gradient boosting machine model achieved an F1-score of 0.744 and a Cohen κ coefficient of 0.474, indicating moderate agreement between the predicted labels and the ground truth. The most predictive features of depressive symptoms were sleep quality and missed mobile interactions. ConclusionsFindings suggest that data collected from passive sensing devices may provide real-time, low-cost insight into the detection of depressive symptoms in college students and may present an opportunity for future prevention and perhaps intervention.

Loneliness is linked to wide ranging physical and mental health problems, including increased rates of mortality. Understanding how loneliness manifests is important for targeted public health treatment and intervention. With advances in mobile sending and wearable technologies, it is possible to collect data on human phenomena in a continuous and uninterrupted way. In doing so, such approaches can be used to monitor physiological and behavioral aspects relevant to an individual’s loneliness. In this study, we proposed a method for continuous detection of loneliness using fully objective data from smart devices and passive mobile sensing. We also investigated whether physiological and behavioral features differed in their importance in predicting loneliness across individuals. Finally, we examined how informative data from each device is for loneliness detection tasks. We assessed subjective feelings of loneliness while monitoring behavioral and physiological patterns in 30 college students over a 2-month period. We used smartphones to monitor behavioral patterns (e.g., location changes, type of notifications, in-coming and out-going calls/text messages) and smart watches and rings to monitor physiology and sleep patterns (e.g., heart-rate, heart-rate variability, sleep duration). Participants reported their loneliness feeling multiple times a day through a questionnaire app on their phone. Using the data collected from their devices, we trained a random forest machine learning based model to detect loneliness levels. We found support for loneliness prediction using a multi-device and fully-objective approach. Furthermore, behavioral data collected by smartphones generally were the most important features across all participants. The study provides promising results for using objective data to monitor mental health indicators, which could provide a continuous and uninterrupted source of information in mental healthcare applications.

Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies are primarily centered on immediate short-term affect detection using data from wearable and mobile devices. These studies typically focus on objective sensory measures, often neglecting other forms of self-reported information like diaries and notes. In this paper, we propose a multimodal deep learning model for affect status forecasting. This model combines a transformer encoder with a pre-trained language model, facilitating the integrated analysis of objective metrics and self-reported diaries. To validate our model, we conduct a longitudinal study, enrolling college students and monitoring them over a year, to collect an extensive dataset including physiological, environmental, sleep, metabolic, and physical activity parameters, alongside open-ended textual diaries provided by the participants. Our results demonstrate that the proposed model achieves predictive accuracy of 82.50% for positive affect and 82.76% for negative affect, a full week in advance. The effectiveness of our model is further elevated by its explainability.

Associative memory plays a crucial role in the cognitive capabilities of the human brain. The Bayesian Confidence Propagation Neural Network (BCPNN) is a cortex model capable of emulating brain-like cognitive capabilities, particularly associative memory. However, the existing GPU-based approach for BCPNN simulations faces challenges in terms of time overhead and power efficiency. In this paper, we propose a novel FPGA-based high performance computing (HPC) design for the BCPNN-based associative memory system. Our design endeavors to maximize the spatial and timing utilization of FPGA while adhering to the constraints of the available hardware resources. By incorporating optimization techniques including shared parallel computing units, hybrid-precision computing for a hybrid update mechanism, and the globally asynchronous and locally synchronous (GALS) strategy, we achieve a maximum network size of 150x10 and a peak working frequency of 100 MHz for the BCPNN-based associative memory system on the Xilinx Alveo U200 Card. The tradeoff between performance and hardware overhead of the design is explored and evaluated. Compared with the GPU counterpart, the FPGA-based implementation demonstrates significant improvements in both performance and energy efficiency, achieving a maximum latency reduction of 33.25x, and a power reduction of over 6.9x, all while maintaining the same network configuration.

Loneliness has sharply increased since the start of the global COVID-19 pandemic, in part, due to disruptions in social relationships and routines, with college students exhibiting the greatest increases. Preventions to address loneliness have been developed but have not achieved high rates of success, perhaps because a key factor in addressing loneliness is focusing on the quality of existing relationships as opposed to promoting social interactions during moments of peak loneliness. Relational savoring, an intervention grounded in principles of attachment theory and positive psychology, was designed to encourage savoring positive experiences and facilitate connectedness with others. The goal of this study was to evaluate the feasibility, acceptability and preliminary outcomes of an mhealth adaptation of relational savoring (mSavorUs)delivered to college students in the service of preventing loneliness. Using a randomized controlled design, this pilot study evaluated a just-in-time digital health prevention and intervention for loneliness prevention. A smart ring, smart watch, and smartphone application were used for ubiquitous monitoring of loneliness and health (i.e., physiology, sleep, behavior). Within a diverse sample of n=29 college students (43.3% Latinx, 40% Asian American, 16.7% White), we tested two aims. First, we examined the utility, benefits, and problems of each feature of the intervention along with the ubiquitous monitoring systems. Second, we examined whether the prevention resulted in reductions in feelings of loneliness and increased connectedness. Aim One qualitative results indicated that participants found the intervention to be rewarding and helpful, but found the timing of the intervention disruptive. Aim Two quantitative results did not reveal reductions in loneliness or increased connectedness, suggesting modifications are needed. Findings suggest that the content of the prevention program (mSavorUs) may be beneficial but the just-in-time-delivery modality reduce program benefits. The study was not preregistered.

Wearable technology enables ubiquitous health monitoring where multivariate physiological and behavioral data can be captured over time. Such multivariate time series (MTS) data in healthcare applications needs technique to interpret the analysis results. However, existing deep learning models for MTS data analysis often lack interpretability, and current explainable AI (xAI) techniques fail to capture the temporal and inter-variable complexities inherent in MTS. This hinders the trust and integration of these AI-based systems in clinical decision-making. In this paper, we propose an attention-based xAI method to classify and interpret MTS data collected from wearable devices. Our approach leverages self-attention mechanisms and graph attention layers (GAT) to capture both temporal and inter-variable dependencies, providing interpretability at both the temporal and modality levels. We evaluate our method using a longitudinal affect status monitoring. The dataset was collected from 21 college students via wearable devices over one year. We train separate models for positive (PA) and negative affect (NA) prediction, and compare their performance with a Transformer-based method. Our method achieves robust classification performance, with 78.62% accuracy for PA and 76.30% for NA, while offering transparent explanations of its decisions. These findings highlight the potential of our xAI method for reliable and interpretable MTS classification in healthcare applications.

Internet-of-Things-based systems have recently emerged, enabling long-term health monitoring systems for the daily activities of individuals. The data collected from such systems are multivariate and longitudinal, which call for tailored analysis techniques to extract the trends and abnormalities in the monitoring. Different methods in the literature have been proposed to identify trends in data. However, they do not include the time dependency and cannot distinguish changes in long-term health data. Moreover, their evaluations are limited to lab settings or short-term analysis. Long-term health monitoring applications require a modeling technique to merge the multisensory data into a meaningful indicator. In this paper, we propose a personalized neural network method to track changes and abnormalities in multivariate health data. Our proposed method leverages convolutional and graph attention layers to produce personalized scores indicating the abnormality level (i.e., deviations from the baseline) of users' data throughout the monitoring. We implement and evaluate the proposed method via a case study on long-term maternal health monitoring. Sleep and stress of pregnant women are remotely monitored using a smartwatch and a mobile application during pregnancy and 3-months postpartum. Our analysis includes 46 women. We build personalized sleep and stress models for each individual using the data from the beginning of the monitoring. Then, we compare the two groups by measuring the data variations. The abnormality scores produced by the proposed method are compared with the findings from the self-report questionnaire data collected in the monitoring and abnormality scores generated by an autoencoder method. The proposed method outperforms the baseline methods in exploring the changes between high-risk and low-risk pregnancy groups. The proposed method's scores also show correlations with the self-report data. Consequently, the results indicate that the proposed method effectively detects the abnormality in multivariate long-term health monitoring.

By making deep analysis on the big data information of historical product, the technology of structured data classification based on deep learning can form knowledge for part capability type recognition and judgment, build a model of capability type recognition on the basis of deep neural network, and also develop a software system of capability type automatic recognition ,for assisting the recognition of part capability type. For part newly imported into the system, combined with its attached attribute information, this technology can automatically identify and recommend the capacity type, and forms a set of knowledge-based method for the recognition and judgment of part capacity type.

The rapid development of wearable technology has enabled remote photoplethysmography (PPG)-based health monitoring in everyday settings, offering real-time and continuous monitoring of cardiovascular parameters, such as heart rate (HR) and heart rate variability (HRV). However, PPG signals collected in daily life are prone to artifacts and noise, posing challenges to HR and HRV extraction. The existing HR and HRV extraction methods cannot effectively handle noisy PPG signals and ensure accurate results. Additionally, current Python packages were primarily designed for analyzing "clean" PPG signals, limiting their performance in handling artifacts and noise and resulting in unreliable HR and HRV measurements. In this paper, we propose a robust end-to-end PPG processing pipeline to reliably extract HR and HRV from PPG signals collected in free-living settings. The pipeline comprises three machine learning-based PPG analysis methods: signal quality assessment, reconstruction of noisy signal, and systolic peak detection. We assess the proposed PPG pipeline using a dataset including PPG and Electrocardiogram (ECG) signals recorded from 46 individuals by smartwatches. Our evaluation demonstrates the proposed pipeline’s superior performance compared to two established benchmark methods in terms of correlation and mean absolute error with ECG as the reference. We also provide the Python implementation of our pipeline for the research community to facilitate integration into their solutions.

Pain is a subjective experience with interpersonal perception sensitivity differences. Pain sensitivity is of scientific and clinical interest, as it is a risk factor for several pain conditions. Resting heart rate variability (HRV) is a potential pain sensitivity measure reflecting the parasympathetic tone and baroreflex function, but it remains unclear how well the prediction can achieve. This work investigated the relationship between different ultra-short-term HRV features and various pain sensitivity representations from heat and electrical pain tests. From leave-subject-out cross-validated results, we found that HRV can better predict a composite pain sensitivity score built from different tests and measures than a single measure in terms of the agreement between predictions and observations. Heat pain sensitivity was more possibly predicted than electrical pain. SDNN, RMSSD and LF better predicted the composite pain sensitivity score than other feature combinations, consis-tent with pain's physical and emotional attributes. It should be emphasized that the validity is probably limited within HRV at the resting state rather than an arbitrary measurement. This work implies a potential pain sensitivity prediction possibility that may be worth further validation.

Photoplethysmography (PPG) is a non-invasive technique used in wearable devices to collect various vital signs, including heart rate and heart rate variability. The signal is highly susceptible to motion artifacts, which is inevitable in health monitoring and may lead to inaccurate decision-making. Studies in the literature proposed time series analysis, signal decomposition, and machine learning methods to reconstruct PPG signals or reduce noise. However, they are limited to short-term noisy signals or to noise caused by certain physical activities. In this paper, we propose a deep convolutional generative adversarial network (GAN) method to reconstruct distorted PPG signals. Our method exploits the temporal information extracted from the corrupted signal and preceding data to perform PPG reconstruction. The model is trained and tested using data collected by smartwatches in a home-based health monitoring application. We evaluate the proposed GAN method in comparison to three state-of-the-art PPG reconstruction methods. The evaluation includes noisy PPG signals with different durations and SNR values. The proposed method outperforms the other methods by obtaining the least error rates. The results indicate that the proposed method is effective for improving PPG signal quality to produce reliable heart rate and heart rate variability.

Recently, memristors have been widely used to implement Spiking Neural Networks (SNNs), which is promising in edge computing scenarios. However, most memristor-based SNN implementations adopt simplified spike-timing-dependent plasticity (STDP) for the online learning process. It is challenging for memristor-based implementations to support the trace-based STDP learning rules that have been widely used in neuromorphic applications. This paper proposed a versatile memristor-based architecture to implement the synaptic-level trace-based STDP learning rules. Especially, the similarity between synaptic trace dynamics and the memristor nonlinearity is explored and exploited to emulate the trace variables of trace-based STDP. As two typical trace-based STDP learning rules, the pairwise STDP and the triplet STDP, are simulated on two typical nonlinear bipolar memristor devices. The simulation results show that the behavior of physical memristor devices can be well estimated (below 6% in terms of the relative root-mean-square error), and the memristor-based in-circuit computation for trace-based STDP learning rules can achieve a high correlation coefficient over 98%.

The Bayesian Confidence Propagation Neural Network (BCPNN) has been applied in higher level of cognitive intelligence (e.g. working memory, associative memory). However, in the spike-based version of this learning rule the pre-, postsynaptic and coincident activity is traced in three low-passfiltering stages, the calculation processes of weight update are very computationally intensive. In this paper, a hardware architecture of the updating process for lazy update mode is proposed for updating 8 local synaptic state variables. The parallelism by decomposing the calculation steps of formulas based on the inherent data dependencies is optimized. The FPGA-based hardware accelerator of BCPNN is designed and implemented. The experimental results show the updating process on FPGA can be accomplished within 110 ns with a clock frequency of 200 MHz, the updating speed is greatly enhanced compared with the CPU test. The trade-off between performance, accuracy and resources on dedicated hardware is evaluated, and the impact of the module reuse on resource consumption and computing performance is evaluated.