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Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated. [ABSTRACT FROM AUTHOR]

This paper briefly overviews and discusses the existing techniques using antennas for passive sensing, starting from the antenna operating principle and antenna structural design to different antenna-based sensing mechanisms. The effects of different electrical properties of the material used to design an antenna, such as conductivity, loss tangent, and resistivity, are discussed to illustrate the fundamental sensing mechanisms. Furthermore, the key parameters, such as operating frequency and antenna impedance, along with the factors affecting the sensing performance, are discussed. Overall, passive sensing using an antenna is mainly achieved by altering the reflected wave characteristics in terms of center frequency, return loss, phase, and received/reflected signal strength. The advantages and drawbacks of each technique are also discussed briefly. Given the increasing relevance, millimeter-wave antenna sensors and resonator sensors are also discussed with their applications and recent advancements. This paper primarily focuses on microstrip-based radiating structures and insights for further sensing performance improvement using passive antennas, which are outlined in this study. In addition, suggestions are made for the current scientific and technical challenges, and future directions are discussed. [ABSTRACT FROM AUTHOR]

Introduction: Existing theories and empirical works link phone use with anxiety; however, most leverage subjective self-reports of phone use (e.g., validated questionnaires) that may not correspond well with true behavior. Moreover, most works linking phone use with anxiety do not interrogate associations within a temporal framework. Accordingly, the present study sought to investigate the utility of passively sensed phone use as a longitudinal predictor of anxiety symptomatology within a population particularly vulnerable to experiencing anxiety. Methods: Using data from the GLOBEM study, which continuously collected longitudinal behavioral data from a college cohort of N = 330 students, weekly PHQ-4 anxiety subscale scores across 3 years (2019–2021) were paired with median daily phone use records from the 2 weeks prior to anxiety self-report completion. Phone use was operationalized through unlock duration which was passively curated via Apple’s “Screen Time” feature. GPS-tracked location data was further utilized to specify whether an individual’s phone use was at home or away from home. Within-individual and temporal associations between phone use and anxiety were modeled within an ordinal mixed-effects logistic regression framework. Results: While there was no significant association between anxiety levels and either median total phone use or median phone use at home, participants in the top quartile of median phone use away from home were predicted to exhibit clinically significant anxiety levels 20% more frequently than participants in the bottom quartile during the first study year; however, this association weakened across successive years. Importantly, these associations remained after controlling for age, physical activity, sleep, and baseline anxiety levels and were not recapitulated when operationalizing phone use with unlock frequency. Conclusions: These findings suggest that phone use may be leveraged as a means of mitigating or coping with anxiety in social situations outside the home, while pandemic-related developments may also have attenuated this behavior later in the study. Nevertheless, the present results suggest promise in interrogating a larger suite of objectively measured phone use behaviors within the context of social anxiety.

Abstract Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21–0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators ( https://nda.nih.gov/abcd/ ). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use.

Introduction: University students are particularly susceptible to developing high levels of stress, which occur when environmental demands outweigh an individual's ability to cope. The growing advent of mental health smartphone apps has led to a surge in use by university students seeking ways to help them cope with stress. Use of these apps has afforded researchers the unique ability to collect extensive amounts of passive sensing data including GPS and step detection. Despite this, little is known about the relationship between passive sensing data and stress. Further, there are no established methodologies or tools to predict stress from passive sensing data in this group. Methods: In this study, we establish a clear machine learning-based methodological pipeline for processing passive sensing data and extracting features that may be relevant in the context of mental health. Results: We then use this methodology to determine the relationship between passive sensing data and stress in university students. Discussion: In doing so, we offer the first proof-of-principle data for the utility of our methodological pipeline and highlight that passive sensing data can indeed digitally phenotype stress in university students. [ABSTRACT FROM AUTHOR]

Youth screen media activity is a growing concern, though few studies include objective usage data. Through the longitudinal, U.S.-based Adolescent Brain Cognitive Development (ABCD) Study, youth (mage = 14; n = 1415) self-reported their typical smartphone use and passively recorded three weeks of smartphone use via the ABCD-specific Effortless Assessment Research System (EARS) application. Here we describe and validate passively-sensed smartphone keyboard and app use measures, provide code to harmonize measures across operating systems, and describe trends in adolescent smartphone use. Keyboard and app-use measures were reliable and positively correlated with one another (r = 0.33) and with self-reported use (rs = 0.21–0.35). Participants recorded a mean of 5 h of daily smartphone use, which is two more hours than they self-reported. Further, females logged more smartphone use than males. Smartphone use was recorded at all hours, peaking on average from 8 to 10 PM and lowest from 3 to 5 AM. Social media and texting apps comprised nearly half of all use. Data are openly available to approved investigators (https://nda.nih.gov/abcd/). Information herein can inform use of the ABCD dataset to longitudinally study health and neurodevelopmental correlates of adolescent smartphone use. [ABSTRACT FROM AUTHOR]

Dehydration is a common problem in the aging population. Medical professionals can detect dehydration using either blood or urine tests. This requires experimental tests in the lab as well as urine and blood samples to be obtained from the patients. This paper proposed 100 GHz millimeter wave radiometry for early detection of dehydration. Reflectance measurements were performed on healthy and dehydrated patients of both genders (120 males and 80 females) in the aging population. Based on the cause of dehydration, the patient groups were divided into three categories: (1) patients dehydrated due to less thirst sensation, (2) patients dehydrated due to illnesses (vomiting and diarrhea), and (3) patients dehydrated due to diabetes. Reflectance measurements were performed on eight locations: (1) the palm, (2) the back of the hand, (3) the fingers, (4) the inner wrist, (5) the outer wrist, (6) the volar side of the arm, (7) the dorsal surface of the arm, and (8) the elbow. Skin dehydrated due to vomiting and diarrhea was found to have lower reflectance at all the measurement locations compared with healthy and other types of dehydrated skin. The elbow region showed the highest difference in reflectance between healthy and dehydrated skin. This indicates that radiometric sensitivity is sufficient to detect dehydration in a few seconds. This will reduce the patient's waiting time and the healthcare professional's intervention time as well as allow early treatment of dehydration, thus avoiding admission to hospitals. [ABSTRACT FROM AUTHOR]

General physicians misclassify depression in more than half of the cases. Researchers have explored the feasibility of leveraging passively collected data points, also called digital biomarkers, to provide more granular understanding of depression phenotypes as well as a more objective assessment of disease. This paper provides a systematic review following the PRISMA guidelines (Page et al., 2021) to understand which digital biomarkers might be relevant for passive screening of depression. Pubmed and PsycInfo were systematically searched for studies published from 2019 to early 2024, resulting in 161 records assessed for eligibility. Excluded were intervention studies, studies focusing on a different disease or those with a lack of passive data collection. 74 studies remained for a quality assessment, after which 27 studies were included. The review shows that depressed participants' real-life behavior such as reduced communication with others can be tracked by passive data. Machine learning models for the classification of depression have shown accuracies up to 0.98, surpassing the quality of many standardized assessment methods. Inconsistency of outcome reporting of current studies does not allow for drawing statistical conclusions regarding effectiveness of individual included features. The Covid-19 pandemic might have impacted the ongoing studies between 2020 and 2022. While digital biomarkers allow real-life tracking of participant's behavior and symptoms, further work is required to align the feature engineering of digital biomarkers. With shown high accuracies of assessments, connecting digital biomarkers with clinical practice can be a promising method of detecting symptoms of depression automatically. • This paper provides a PRISMA-guided systematic review of studies (2019-2024), showing model accuracy ranges of 0.52-0.98. • The review summarises constructs related to mood, location, smartphone use, communication, sleep quality and heart rate. • To be integrated into clinical practice, captured data needs to be reduced to the most relevant features. [ABSTRACT FROM AUTHOR]

Flexible strain sensors are essential components for wearable devices, human‐machine interaction systems, and intelligent soft robots. Current active strain sensors have high power consumption, while passive strain sensors exhibit limitations in detecting static or slowly‐varying stimulations. Here, inspired by the strain sensing behavior of Ruffini endings in natural skin, a new type of potentiometric, fully passive, and highly stretchable strain sensors are presented based on the mechanical regulation of ionically conductive percolating networks. Specifically, an artificial potential difference is first created with two electrochemical active electrodes, followed by encoding external strain stimulations into the potential difference variations using a new class of highly elastic and ionically conductive composites with configurable ionic percolating networks. Based on the proposed passive strain sensing mechanism, both static and slowly‐varying strain stimulations (up to 100%) can be monitored with ultralow power consumption (at nW level). Promising applications of the passive strain sensors in human motion monitoring, Morse code communication, and hand gesture recognition are demonstrated. This work opens up a new avenue to constructing novel strain sensing devices with high stretchability, ultralow power consumption, and desirable sensing performance. [ABSTRACT FROM AUTHOR]

Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures. [ABSTRACT FROM AUTHOR]

IntroductionUniversity students are particularly susceptible to developing high levels of stress, which occur when environmental demands outweigh an individual’s ability to cope. The growing advent of mental health smartphone apps has led to a surge in use by university students seeking ways to help them cope with stress. Use of these apps has afforded researchers the unique ability to collect extensive amounts of passive sensing data including GPS and step detection. Despite this, little is known about the relationship between passive sensing data and stress. Further, there are no established methodologies or tools to predict stress from passive sensing data in this group.MethodsIn this study, we establish a clear machine learning-based methodological pipeline for processing passive sensing data and extracting features that may be relevant in the context of mental health.ResultsWe then use this methodology to determine the relationship between passive sensing data and stress in university students.DiscussionIn doing so, we offer the first proof-of-principle data for the utility of our methodological pipeline and highlight that passive sensing data can indeed digitally phenotype stress in university students.Clinical trial registrationAustralia New Zealand Clinical Trials Registry (ANZCTR), identifier ACTRN12621001223820.

Acoustic vector sensors provide a measurement of both the scalar acoustic pressure as well as the acoustic particle velocity vector in the orthogonal Cartesian directions, allowing for direction of arrival estimation of an acoustic source. Typically, acoustic vector sensors are used in array configurations in place of hydrophones to increase the overall array gain and directivity. However, for this work we explore the use of more sparsely distributed singular acoustic vector sensors for the purpose of target tracking, not relying on a traditional array configuration. Measurement fusion of individual direction of arrival estimates from each of the various sensors is performed using the Maximum-Likelihood Probabilistic Multi-hypothesis Tracker, which estimates the target motion parameters that maximize the likelihood based on a batch of measurements. The Cramér-Rao lower bounds for angular error variances are computed about the estimates and used as the associated measurement error variances in the likelihood function. We evaluate the efficacy of angular measurement extraction and accuracy of tracking estimates for a variety of scenarios, including both single and multiple target cases.

Unmanned Aerial Vehicle (UAV) technology is increasingly gaining attention in localization systems due to its flexibility and mobility. However, traditional localization techniques often fail in complex environments where line-of-sight paths are obstructed. To address this challenge, this paper presents an innovative UAV-assisted high-precision multi-target localization system. The system utilizes UAVs equipped with Reconfigurable Intelligent Surfaces to create a reflective signal path, allowing a receiver sensor to capture these signals, creating favorable conditions for multi-target localization. Exploiting the sparsity of signals, we introduce a direct positioning algorithm that leverages Atomic Norm Minimization (ANM) to estimate the target’s location. To address the high complexity of traditional ANM methods, we propose a novel Coyote-ANM-based direct localization (CADL) approach. This method combines the coyote optimization algorithm with the alternating direction method of multipliers to achieve high-accuracy positioning with reduced computational complexity. Simulation results across various signal-to-noise ratio scenarios demonstrate that the proposed algorithm significantly improves localization accuracy, achieving lower root mean square error values and faster execution times compared to traditional methods.

Recent advancements in mobile health (mHealth) technology and the ubiquity of wearable devices and smartphones have expanded a market for digital health and have emerged as innovative tools for data collection on individualized behavior. Heterogeneous levels of device usage across users and across days within a single user may result in different degrees of underestimation in passive sensing data, subsequently introducing biases if analyzed without addressing this issue. In this work, we propose an unsupervised 2-Stage Pre-processing Algorithm for Passively Sensed mHealth Data (2SpamH) algorithm that uses device usage variables to infer the quality of passive sensing data from mobile devices. This article provides a series of simulation studies to show the utility of the proposed algorithm compared to existing methods. Application to a real clinical dataset is also illustrated.

As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest. [ABSTRACT FROM AUTHOR]

Background: Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them. Methods: Literature and expert opinions were integrated to formulate relevant guiding principles. Results: Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it. Conclusions: The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field. [ABSTRACT FROM AUTHOR]

Background: Mental health difficulties are highly prevalent worldwide. Passive sensing technologies and applied artificial intelligence (AI) methods can provide an innovative means of supporting the management of mental health problems and enhancing the quality of care. However, the views of stakeholders are important in understanding the potential barriers to and facilitators of their implementation. Objective: This study aims to review, critically appraise, and synthesize qualitative findings relating to the views of mental health care professionals on the use of passive sensing and AI in mental health care. Methods: A systematic search of qualitative studies was performed using 4 databases. A meta-synthesis approach was used, whereby studies were analyzed using an inductive thematic analysis approach within a critical realist epistemological framework. Results: Overall, 10 studies met the eligibility criteria. The 3 main themes were uses of passive sensing and AI in clinical practice, barriers to and facilitators of use in practice, and consequences for service users. A total of 5 subthemes were identified: barriers, facilitators, empowerment, risk to well-being, and data privacy and protection issues. Conclusions: Although clinicians are open-minded about the use of passive sensing and AI in mental health care, important factors to consider are service user well-being, clinician workloads, and therapeutic relationships. Service users and clinicians must be involved in the development of digital technologies and systems to ensure ease of use. The development of, and training in, clear policies and guidelines on the use of passive sensing and AI in mental health care, including risk management and data security procedures, will also be key to facilitating clinician engagement. The means for clinicians and service users to provide feedback on how the use of passive sensing and AI in practice is being received should also be considered. [ABSTRACT FROM AUTHOR]

Objective: Chronic pain is known to affect approximately a quarter of the United States population. Despite the prevalence of chronic pain, current chronic pain measures are often reliant on self reports and other similar subjective data. The purpose of this study was to examine chronic pain in a longitudinal and naturalistic setting for the potential to enhance chronic pain patient care by capturing more realistic and consistent data in everyday settings. Methods: The study sample consisted of a National Health and Nutrition Examination Survey (NHANES) sample from 2003–2004 with 4,240 participants that had an average age of 49.57(SD = 18.38) years. The NHANES sample had movement intensity data collected each minute over 7 days by wearing a hip mounted Actigraph accelerometer. This data was used to determine if chronic pain could be predicted by movement intensity. Results: A deep learning model for time series (i.e., convolutional long short-term memory network model) determined that participants’ chronic pain could be predicted with daily actigraphy movement intensity (AUC validation = 0.60, AUC test = 0.57). Additionally, participants with chronic pain had, on average, lower physical activity intensity (124.09 counts per minute, 95% CI: 112.58–135.59) than participants without chronic pain (152.29 counts per minute, 95% CI: 147.87–156.72). Conclusions: The results of this study demonstrated that passive wearable measures of physical activity could serve as a potential biomarker for chronic pain. Chronic pain could be predicted using daily activity movement intensity, which supports the value of passive data collection for assessment of chronic pain.

AbstractStructural health monitoring (SHM) of brittle structures will require versatile sensing instrumentation that can transmitting transient loadings into rapid electrical responses. This research investigated the sensing performance of CNT Buckypaper (CNT-BP) thin-films on stressed fiber-reinforced composites, which are integral materials to various industries of the infrastructure. Three-point bending experiments revealed an extraordinary gauge factor (∼40) and impressive response linearity. Electrical responses were instantaneous; moreover, low force impacts were detectable, the sensors provided clear indications of spatial recognition. Most importantly, the manufacturing methods are scalable and cost effective compared to commercialized strain gauges. This study examined micro-strain measurements (

This paper briefly overviews and discusses the existing techniques using antennas for passive sensing, starting from the antenna operating principle and antenna structural design to different antenna-based sensing mechanisms. The effects of different electrical properties of the material used to design an antenna, such as conductivity, loss tangent, and resistivity, are discussed to illustrate the fundamental sensing mechanisms. Furthermore, the key parameters, such as operating frequency and antenna impedance, along with the factors affecting the sensing performance, are discussed. Overall, passive sensing using an antenna is mainly achieved by altering the reflected wave characteristics in terms of center frequency, return loss, phase, and received/reflected signal strength. The advantages and drawbacks of each technique are also discussed briefly. Given the increasing relevance, millimeter-wave antenna sensors and resonator sensors are also discussed with their applications and recent advancements. This paper primarily focuses on microstrip-based radiating structures and insights for further sensing performance improvement using passive antennas, which are outlined in this study. In addition, suggestions are made for the current scientific and technical challenges, and future directions are discussed.

Dehydration is a common problem in the aging population. Medical professionals can detect dehydration using either blood or urine tests. This requires experimental tests in the lab as well as urine and blood samples to be obtained from the patients. This paper proposed 100 GHz millimeter wave radiometry for early detection of dehydration. Reflectance measurements were performed on healthy and dehydrated patients of both genders (120 males and 80 females) in the aging population. Based on the cause of dehydration, the patient groups were divided into three categories: (1) patients dehydrated due to less thirst sensation, (2) patients dehydrated due to illnesses (vomiting and diarrhea), and (3) patients dehydrated due to diabetes. Reflectance measurements were performed on eight locations: (1) the palm, (2) the back of the hand, (3) the fingers, (4) the inner wrist, (5) the outer wrist, (6) the volar side of the arm, (7) the dorsal surface of the arm, and (8) the elbow. Skin dehydrated due to vomiting and diarrhea was found to have lower reflectance at all the measurement locations compared with healthy and other types of dehydrated skin. The elbow region showed the highest difference in reflectance between healthy and dehydrated skin. This indicates that radiometric sensitivity is sufficient to detect dehydration in a few seconds. This will reduce the patient’s waiting time and the healthcare professional’s intervention time as well as allow early treatment of dehydration, thus avoiding admission to hospitals.

Structural health monitoring (SHM) of brittle structures will require versatile sensing instrumentation that can transmitting transient loadings into rapid electrical responses. This research investigated the sensing performance of CNT Buckypaper (CNT-BP) thin-films on stressed fiber-reinforced composites, which are integral materials to various industries of the infrastructure. Three-point bending experiments revealed an extraordinary gauge factor (∼40) and impressive response linearity. Electrical responses were instantaneous; moreover, low force impacts were detectable, the sensors provided clear indications of spatial recognition. Most importantly, the manufacturing methods are scalable and cost effective compared to commercialized strain gauges. This study examined micro-strain measurements (<1%) of carbon fiber composites, in which bending strain of 0.002% could be detected. In addition, damage progressions of glass fiber composites were recorded in response to low-impact energies. Less than 20 J of impact energy was detectable by the sensor, and the severity could be assessed from drastic changes in the sensing behavior. [ABSTRACT FROM AUTHOR]

Cracking is an important factor affecting the performance and life of large structures. In order to maximize personal safety and reduce costs, it is highly necessary to carry out research on crack monitoring technology. Sensors based on Radio Frequency Identification (RFID) antennas have the advantages of wireless and low cost, which makes them highly competitive in the field of structure health monitoring (SHM). Thus, this study systematically summarizes the research progress of crack monitoring based on RFID technology in recent years. Firstly, this study introduces the causes of cracks and the traditional monitoring methods. Further, this study summarizes several main RFID-based crack monitoring and detection methods, including crack monitoring based on chipless RFID technology, passive RFID technology, and ultra-high-frequency (UHF) RFID technology, including the implementation methods, as well as the advantages and disadvantages of those technologies. In addition, for RFID-based crack monitoring applications, the two most commonly used materials are concrete materials and metal materials, which are also illustrated in detail. In general, this study can provide technical support and a theoretical basis for crack monitoring and detection to ensure the safety of engineering structures.

Background: Prior literature links passively sensed information about a person's location, movement, and communication with social anxiety. These findings hold promise for identifying novel treatment targets, informing clinical care, and personalizing digital mental health interventions. However, social anxiety symptoms are heterogeneous; to identify more precise targets and tailor treatments, there is a need for personal sensing studies aimed at understanding differential predictors of the distinct subdomains of social anxiety. Our objective was to conduct a large-scale smartphone-based sensing study of fear, avoidance, and physiological symptoms in the context of trait social anxiety over time. Methods: Participants (n = 1013; 74.6 % female; M age = 40.9) downloaded the LifeSense app, which collected continuous passive data (e.g., GPS, communication, app and device use) over 16 weeks. We tested a series of multilevel linear regression models to understand within- and between-person associations of 2-week windows of passively sensed smartphone data with fear, avoidance, and physiological distress on the self-reported Social Phobia Inventory (SPIN). A shifting sensor lag was applied to examine how smartphone features related to SPIN subdomains 2 weeks in the future (distal prediction), 1 week in the future (medial prediction), and 0 weeks in the future (proximal prediction). Results: A decrease in time visiting novel places was a strong between-person predictor of social avoidance over time (distal β = −0.886, p = .002; medial β = −0.647, p = .029; proximal β = −0.818, p = .007). Reductions in call- and text-based communications were associated with social avoidance at both the between- (distal β = −0.882, p = .002; medial β = −0.932, p = .001; proximal β = −0.918, p = .001) and within- (distal β = −0.191, p = .046; medial β = −0.213, p = .028) person levels, as well as between-person fear of social situations (distal β = −0.860, p

Background: Use of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources. Methods: Participants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time. Results: ENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7–8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection. Conclusions: Simpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months. [ABSTRACT FROM AUTHOR]

Strain-based condition evaluation has garnered as a crucial method for the structural health monitoring (SHM) of large-scale engineering structures. The use of traditional wired strain sensors becomes tedious and time-consuming due to their complex wiring operation, more workload, and instrumentation cost to collect sufficient data for condition state evaluation, especially for large-scale engineering structures. The advent of wireless and passive RFID technologies with high efficiency and inexpensive hardware equipment has brought a new era of next-generation intelligent strain monitoring systems for engineering structures. Thus, this study systematically summarizes the recent research progress of cutting-edge RFID strain sensing technologies. Firstly, this study introduces the importance of structural health monitoring and strain sensing. Then, RFID technology is demonstrated including RFID technology's basic working principle and system component composition. Further, the design and application of various kinds of RFID strain sensors in SHM are presented including passive RFID strain sensing technology, active RFID strain sensing technology, semi-passive RFID strain sensing technology, Ultra High-frequency RFID strain sensing technology, chipless RFID strain sensing technology, and wireless strain sensing based on multi-sensory RFID system, etc., expounding their advantages, disadvantages, and application status. To the authors' knowledge, the study initially provides a systematic comprehensive review of a suite of RFID strain sensing technology that has been developed in recent years within the context of structural health monitoring. [ABSTRACT FROM AUTHOR]

Passive sensing technology enables Wi-Fi-based human activity recognition (HAR), which has been widely noted in recent years. This letter presents a novel Wi-Fi-based class-incremental HAR system that allows for the gradual addition of new activity categories. To the best of our knowledge, this is the first attempt to recognize all previously learned activities under the constraint of limited samples for both the original and newly added activity classes. It is challenging in: 1) how to prevent catastrophic forgetting of old activities and 2) how to leverage as few samples as possible to accurately recognize new activities. Therefore, a phased training and update strategy is proposed to avoid the knowledge-forgetting issue. Furthermore, to alleviate the unsatisfactory performance problem caused by insufficient samples of new categories, we design an amplitude-phase enhanced convolution neural network (CNN), which integrates an attention mechanism and dual loss (DL) function to enhance the feature discrimination and the generalization capability of the model. Extensive experiments show that our system can operate with promising perceptual accuracy in different datasets. [ABSTRACT FROM AUTHOR]

Loneliness among college students is an increasingly prevalent issue. While technology-based methods for detection using behavioural patterns have been proposed, there remains an opportunity for improvement as insufficient attention has been given to the individual behavioral differences among students. Loneliness is a highly subjective experience and people’s routines differ, making it a challenge for generic models to accurately determine its presence and severity. In response to this challenge, it is particularly helpful to identify subgroups within the population that exhibit similar behavioral characteristics, enabling a more nuanced understanding and detection of loneliness. This paper introduces a novel approach to loneliness detection, leveraging a data set gathered through passive sensing using mobile phones, which provides a rich source of user behavioral data. We utilized unsupervised clustering to find subgroups of students exhibiting similar behavioral patterns over time within the data set. This approach is essential for continuous monitoring, identifying changes in behavioral patterns, and facilitating the early detection of loneliness. Using data from 41 students’ smartphones, we created group-specific classification models to identify loneliness. Group-based prediction models for loneliness detection have shown significant improvement in accuracy over generalized models. These findings can lead to the development of more effective, tailored methods for loneliness detection in diverse populations. This study emphasizes the importance of personalized approaches in mental health interventions and highlights the potential of passive sensing data in creating tailored loneliness detection methods.

In this article, we proposed a passive vibration sensing system using a threshold‐less deformed helix ferroelectric liquid crystal (DHFLC) cell. Combining the DHFLC cell, piezoelectric film, and optical detector, the system can transform the slight vibration into light intensity signal without a power supply. The system can reach the sensitivity of 165 V/(m/s) and the linearity <1% full range output. The proposed system will be combined with optical fibers to play an essential role in various vibration‐sensing array applications. [ABSTRACT FROM AUTHOR]

Smartphone embedded sensors have created unprecedented opportunities to study human behavior in natural conditions through continuous mobile sensing. However, continuous mobile sensing poses critical energy challenge to smartphone's daily usage. There is an urgent need to enhance energy efficiency of mobile sensing applications while capture sufficient data to accurately predict user state. In this work, we propose an adaptive passive sensing framework to control low-level sensing cycles using an off-policy reinforcement learning (RL) algorithm namely Q-learning with linear approximation and decaying exploration. We propose two different formulations to meet different energy efficiency demands with various designs in their state spaces, action spaces, and reward signals. Using real continuous mobile sensing data from 220 participants for more than 2 weeks, we show consistently better performances on energy saving for our proposed RL strategies when compared to four different baseline methods. To verify the impacts of our proposed strategies on data utility, we predict social anxiety and daily negative affect using active data collected during the same study window. Our proposed RL strategies show equivalent prediction performance when compared to the baseline strategies and continuous sensing. [ABSTRACT FROM AUTHOR]

As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.

BackgroundFood craving relates to unhealthy eating behaviors such as overeating or binge eating and is thus a promising target for digital interventions. Yet, craving varies strongly across the day and is more likely in some contexts (external, internal) than in others. Prediction of food cravings ahead of time would enable preventive interventions.ObjectiveThe objective of this study was to investigate whether upcoming food cravings could be detected and predicted from passive smartphone sensor data (excluding geolocation information) without the need for repeated questionnaires.MethodsMomentary food craving ratings, given six times a day for 14 days by 56 participants, served as the dependent variable. Predictor variables were environmental noise, light, device movement, screen activity, notifications, and time of the day recorded from 150 to 30 min prior to these ratings.ResultsIndividual high vs. low craving ratings could be predicted on the test set with a mean area under the curve (AUC) of 0.78. This outperformed a baseline model trained on past craving values in 85% of participants by 14%. Yet, this AUC value is likely the upper bound and needs to be independently validated with longer data sets that allow a split into training, validation, and test sets.ConclusionsCraving states can be forecast from external and internal circumstances as these can be measured through smartphone sensors or usage patterns in most participants. This would allow for just-in-time adaptive interventions based on passive data collection and hence with minimal participant burden.

More and more data are being collected using combined active (e.g., surveys) and passive (e.g., smartphone sensors) ambulatory assessment methods. Fine-grained temporal data, such as smartphone sensor data, allow gaining new insights into the dynamics of social interactions in day-to-day life and how these are associated with psychosocial phenomena – such as loneliness. So far, however, smartphone sensor data have often been aggregated over time, thus, not doing justice to the fine-grained temporality of these data. In this article, we demonstrate how time-stamped sensor data of social interactions can be modeled with multistate survival models. We examine how loneliness is associated with (a) the time between social interaction (i.e., interaction rate) and (b) the duration of social interactions in a student population (Nparticipants = 45, Nobservations = 74,645). Before a 10-week ambulatory assessment phase, participants completed the UCLA loneliness scale, covering subscales on intimate, relational, and collective loneliness. Results from the multistate survival models indicated that loneliness subscales were not significantly associated with differences in social interaction rate and duration – only relational loneliness predicted shorter social interaction encounters. These findings illustrate how the combination of new measurement and modeling methods can advance knowledge on social interaction dynamics in daily life settings and how they relate to psychosocial phenomena such as loneliness. [ABSTRACT FROM AUTHOR]

Objective: Anorexia nervosa (AN) is commonly experienced alongside difficulties of emotion regulation (ER). Previous works identified physical activity (PA) as a mechanism for AN sufferers to achieve desired affective states, with evidence towards mitigation of negative affect. However, temporal associations of PA with specific emotional state outcomes are unknown. Method: Using lag‐ensemble machine learning and feature importance analyses, 888 affect‐based ecological momentary assessments across N = 75 adolescents with AN (N = 44) and healthy controls (N = 31) were analysed to explore significance of past PA, measured through passively collected wrist‐worn actigraphy, with subsequent self‐report momentary affect change across 9 affect constructs. Results: Among AN adolescents, later lags (≥2.5 h) were important in predicting change across negative emotions (hostility, sadness, fear, guilt). AN‐specific model performance on held‐out test data revealed the holistic "negative affect" construct as significantly predictable. Only joviality and self‐assurance, both positively‐valenced constructs, were significantly predictable among healthy‐control‐specific models. Discussion: Results recapitulated previous findings regarding the importance of PA in negative ER for AN individuals. Moreover, PA was found to play a uniquely prominent role in predicting negative affect 4.5–6 h later among AN adolescents. Future research into the PA‐ER dynamic will benefit from targeting specific negative emotions across greater temporal scales. Key Points: Physical activity (PA) plays an important role in the regulation of negative emotion for those with anorexia nervosa (AN).Compared with healthy controls (HC), negative affect among adolescents with AN is uniquely predicted by PA 4.5–6 h prior.Further exploration of delayed associations between PA and specific negative affect states may help to better understand the dynamics of emotion regulation (ER) in those with AN. [ABSTRACT FROM AUTHOR]

Background: Remote measurement technologies (RMTs) such as smartphones and wearables can help improve treatment for depression by providing objective, continuous, and ecologically valid insights into mood and behavior. Engagement with RMTs is varied and highly context dependent; however, few studies have investigated their feasibility in the context of treatment. Objective: A mixed methods design was used to evaluate engagement with active and passive data collection via RMT in people with depression undergoing psychotherapy. We evaluated the effects of treatment on 2 different types of engagement: study attrition (engagement with study protocol) and patterns of missing data (engagement with digital devices), which we termed data availability. Qualitative interviews were conducted to help interpret the differences in engagement. Methods: A total of 66 people undergoing psychological therapy for depression were followed up for 7 months. Active data were gathered from weekly questionnaires and speech and cognitive tasks, and passive data were gathered from smartphone sensors and a Fitbit (Fitbit Inc) wearable device. Results: The overall retention rate was 60%. Higher-intensity treatment (Χ² 1=4.6; P=.03) and higher baseline anxiety (t56.28= -2.80, 2-tailed; P=.007) were associated with attrition, but depression severity was not (t50.4= -0.18; P=.86). A trend toward significance was found for the association between longer treatments and increased attrition (U=339.5; P=.05). Data availability was higher for active data than for passive data initially but declined at a sharper rate (90%-30% drop in 7 months). As for passive data, wearable data availability fell from a maximum of 80% to 45% at 7 months but showed higher overall data availability than smartphone-based data, which remained stable at the range of 20%-40% throughout. Missing data were more prevalent among GPS location data, followed by among Bluetooth data, then among accelerometry data. As for active data, speech and cognitive tasks had lower completion rates than clinical questionnaires. The participants in treatment provided less Fitbit data but more active data than those on the waiting list. Conclusions: Different data streams showed varied patterns of missing data, despite being gathered from the same device. Longer and more complex treatments and clinical characteristics such as higher baseline anxiety may reduce long-term engagement with RMTs, and different devices may show opposite patterns of missingness during treatment. This has implications for the scalability and uptake of RMTs in health care settings, the generalizability and accuracy of the data collected by these methods, feature construction, and the appropriateness of RMT use in the long term. [ABSTRACT FROM AUTHOR]

Study Objectives We sought to develop behavioral sleep measures from passively sensed human-smartphone interactions and retrospectively evaluate their associations with sleep disturbance, anxiety, and depressive symptoms in a large cohort of real-world patients receiving virtual behavioral medicine care. Methods Behavioral sleep measures from smartphone data were developed: daily longest period of smartphone inactivity (inferred sleep period [ISP]); 30-day expected period of inactivity (expected sleep period [ESP]); regularity of the daily ISP compared to the ESP (overlap percentage); and smartphone usage during inferred sleep (disruptions, wakefulness during sleep period). These measures were compared to symptoms of sleep disturbance, anxiety, and depression using linear mixed-effects modeling. More than 2300 patients receiving standard-of-care virtual mental healthcare across more than 111 000 days were retrospectively analyzed. Results Mean ESP duration was 8.4 h (SD = 2.3), overlap percentage 75% (SD = 18%) and disrupted time windows 4.85 (SD = 3). There were significant associations between overlap percentage (p < 0.001) and disruptions (p < 0.001) with sleep disturbance symptoms after accounting for demographics. Overlap percentage and disruptions were similarly associated with anxiety and depression symptoms (all p < 0.001). Conclusions Smartphone behavioral measures appear useful to longitudinally monitor sleep and benchmark depressive and anxiety symptoms in patients receiving virtual behavioral medicine care. Patterns consistent with better sleep practices (i.e. greater regularity of ISP, fewer disruptions) were associated with lower levels of reported sleep disturbances, anxiety, and depression. [ABSTRACT FROM AUTHOR]

Abstract Background This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively. Aims Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology. Results Overall, 118 studies were assessed as eligible. Considering the terms employed, “EMA”, “ESM”, and “DP” were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps’ information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales. Conclusions Findings suggest links between a person’s digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person’s broader contextual and developmental circumstances in relation to their digital data/records.

Impact force identification (IFI) through deconvolution methods from measured structural responses poses a challenge due to the ill-posed nature of the inversion problem. Regularisation techniques, such as Tikhonov, sparse, and wavelet regularisation, offer potential solutions to mitigate the ill-posedness. However, determining suitable regularisation parameters is time-consuming. This study presents an adaptive wavelet-regularised time-domain deconvolution method for efficient IFI, based on wavelet transform and multi-resolution analysis. By analysing impact sensor signals using wavelets, adaptive impact windows covering the entire impact duration are generated, reducing signal length for deconvolution. Multi-resolution analysis of sensor signals enables identification of wavelet bases for the multi-resolution representation of impact force history. Regularisation is achieved by filtering out insignificant wavelet bases based on their energy contributions. Validation is performed through experiments involving small-mass hammer and large-mass drop tower impacts, comparing against various deconvolution methods. Results demonstrate the superior computational efficiency and comparable accuracy of the proposed adaptive method in IFI across different time windows. Notably, accurate force deconvolution for large-mass impacts confirms the effectiveness of the deconvolution methods for IFI, particularly when the structure undergoes significant local deformation. • An adaptive wavelet-regularised deconvolution method for impact force identification. • Dominant frequency analysis allows impact window estimation and signal downsampling. • Multi-resolution response analysis reveals insignificant wavelets in force representation. • Experimental validation on an impacted structure undergoing significant deformation. • Proposed method exhibits superior computational efficiency and comparable accuracy. [ABSTRACT FROM AUTHOR]

The present work is framed inside a broader activity aimed at improving the accuracy of numerical models in predicting the crashworthiness behavior of flexible fuel tanks. This paper describes a comprehensive experimental and numerical study aimed at estimating the impact force of a test article, consisting of a soft nylon bag filled with water, subjected to crash impact tests. In order to understand and improve response predictions, the test article drops freely from different heights, and then strikes onto a rigid plate which is instrumented with different types of sensors. Strain gauges, piezoceramic sensors, and fiber optics are used to measure the strain induced by the impact force during the experiments. To tune the test matrix and the measurement chain parameters, numerical computations are carried out to predict the dynamics of drop impact through FE explicit analyses. Through analysis and comparison with experimental results, a relationship between strain and impact energy correlated with the drop height is established, and the overall accuracy of the entire measurement chain is assessed to determine the effectiveness of such a methodology in a full-scale test on a flexible fuel tank structure.

Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of depressed mood, we propose HADD, which provides new capabilities. First, HADD supports multimodal data analysis in order to enhance the accuracy of ubiquitous depressed mood detection by analyzing not only objective sensor data, but also subjective EMA (ecological momentary assessment) data collected by using mobile devices. In addition, HADD improves upon the accuracy of state-of-the-art ML algorithms for depressed mood detection via effective feature selection, data augmentation, and two-stage outlier detection. In our evaluation, HADD significantly enhanced the accuracy of a comprehensive set of ML models for depressed mood detection. [ABSTRACT FROM AUTHOR]

Electromagnetic spectrum sensing is quite important for communication safety in commercial wireless communication, as well as for the equipment paralysis of rivals and self-protection in modern warfare. Specifically, a passive electric field sensor featured with broadband, remote sensing capabilities and small size is urgently needed to realize spectrum sensing. Here, we demonstrate a photonic electric field sensor operating in the frequency range of 10 MHz to 26.5 GHz. Based on the electric field sensor, distortion-free sensing of on–off keying signals centered at 3 GHz is achieved under a bit rate up to 40 Mb/s. In addition, remote electromagnetic spectrum sensing is also demonstrated up to 70 km, where the signal-to-noise ratio is measured to be larger than 20 dB. [ABSTRACT FROM AUTHOR]

Introduction: Schizophrenia and Major Depressive Disorder (MDD) are highly burdensome mental disorders, with significant cost to both individuals and society. Despite these disorders representing distinct clinical categories, they are each heterogenous in their symptom profiles, with considerable transdiagnostic features. Although movement and sleep abnormalities exist in both disorders, little is known of the precise nature of these changes longitudinally. Passively-collected longitudinal data from wearable sensors is well suited to characterize naturalistic features which may cross traditional diagnostic categories (e.g., highlighting behavioral markers not captured by self-report information).Methods: The present analyses utilized raw minute-level actigraphy data from three diagnostic groups: individuals with schizophrenia (N = 23), individuals with depression (N = 22), and controls (N = 32), respectively, to interrogate naturalistic behavioral differences between groups. Subjects' week-long actigraphy data was processed without diagnostic labels via unsupervised machine learning clustering methods, in order to investigate the natural bounds of psychopathology. Further, actigraphic data was analyzed across time to determine timepoints influential in model outcomes.Results: We find distinct actigraphic phenotypes, which differ between diagnostic groups, suggesting that unsupervised clustering of naturalistic data aligns with existing diagnostic constructs. Further, we found statistically significant inter-group differences, with depressed persons showing the highest behavioral variability.Limitations: However, diagnostic group differences only consider biobehavioral trends captured by raw actigraphy information.Conclusions: Passively-collected movement information combined with unsupervised deep learning algorithms shows promise in identifying naturalistic phenotypes in individuals with mental health disorders, specifically in discriminating between MDD and schizophrenia. [ABSTRACT FROM AUTHOR]

Background: Suicide is a severe public health problem, resulting in a high number of attempts and deaths each year. Early detection of suicidal thoughts and behaviors (STBs) is key to preventing attempts. We discuss passive sensing of digital and behavioral markers to enhance the detection and prediction of STBs. Objective: The paper presents the protocol for a systematic review that aims to summarize existing research on passive sensing of STBs and evaluate whether the STB prediction can be improved using passive sensing compared to prior prediction models. Methods: A systematic search will be conducted in the scientific databases MEDLINE, PubMed, Embase, PsycINFO, and Web of Science. Eligible studies need to investigate any passive sensor data from smartphones or wearables to predict STBs. The predictive value of passive sensing will be the primary outcome. The practical implications and feasibility of the studies will be considered as secondary outcomes. Study quality will be assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). If studies are sufficiently homogenous, we will conduct a meta-analysis of the predictive value of passive sensing on STBs. Results: The review process started in July 2022 with data extraction in September 2022. Results are expected in December 2022. Conclusions: Despite intensive research efforts, the ability to predict STBs is little better than chance. This systematic review will contribute to our understanding of the potential of passive sensing to improve STB prediction. Future research will be stimulated since gaps in the current literature will be identified and promising next steps toward clinical implementation will be outlined. [ABSTRACT FROM AUTHOR]

The 3D time-of-flight (ToF) cameras have recently received a lot of attention due to their wide range of applications. Despite remarkable advancements in ToF imaging, state-of-the-art ToF cameras are still afflicted by the power hungriness of their illumination sources. To tackle this problem, we exploited existing lighting infrastructure, which ensures the ubiquitous presence of modulated light sources in indoor spaces that serve as opportunity illuminators. We explored the bistatic geometry for passive imaging using the pulse-based ToF approach. Our work is inspired by the recently introduced visible light communication (VLC) or light-fidelity (Li-Fi) infrastructure. VLC allows the infrastructure to provide indoor simultaneous illumination, communication, and sensing (SICS). To this end, we designed a bistatic geometry for the purpose of attaining passive 3D imaging. Such capabilities are achieved by exploiting the pulse shape of the autocorrelation function of real optical signals generated by VLC/Li-Fi modules (e.g., OpenVLC and LiFiMAX). We demonstrated passive imaging by means of matched filtering. In this work, we also studied different sampling strategies in the time-shift domain, including uniform, random, and sparse rulers, which is another step forward toward preserving high depth accuracy with a minimal number of measurements. The proposed methodology achieved successful depth reconstruction with negligible root-mean-square error (RMSE) for the low signal-to-noise ratio (SNR) of the measurements. Parametric models, such as Gaussian and sum of sines, are used to characterize the cross correlation functions and allow for robust parametric depth retrieval from a few measurements. Moreover, we attained a 20-mm worst case error for a target at 25 cm. The experiment proved that the bistatic passive depth reconstruction is feasible. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for multi-class classification and uncertainty quantification of impact events on a flat composite plate with a structural health monitoring (SHM) system by using a Bayesian neural network (BNN). Most of the existing research in passive sensing has focused on deterministic approaches for impact detection and characterization. However, there are variability in impact location, angle and energy in real operational conditions which results in uncertainty in the diagnosis. Therefore, this paper proposes a reliability-based impact characterization method based on BNN for the first time. Impact data are acquired by a passive sensing system of piezoelectric (PZT) sensors. Features extracted from the sensor signals, such as their transferred energy, frequency at maximum amplitude and time interval of the largest peak, are used to develop a BNN for impact classification (i.e., energy level). To test the robustness and reliability of the proposed model to impact variability, it is trained with perpendicular impacts and tested by variable angle impacts. The same dataset is further applied in a method called multi-artificial neural network (multi-ANN) to compare its ability in uncertainty quantification and its computational efficiency against the BNN for validation of the developed meta-model. It is demonstrated that both the BNN and multi-ANN can measure the uncertainty and confidence of the diagnosis from the prediction results. Both have very high performance in classifying impact energies when the networks are trained and tested with perpendicular impacts of different energy and location, with 94% and 98% reliable predictions for BNN and multi-ANN, respectively. However, both metamodels struggled to detect new impact scenarios (angled impacts) when the data set was not used in the development stage and only used for testing. Including additional features improved the performance of the networks in regularization; however, not to the acceptable accuracy. The BNN significantly outperforms the multi-ANN in computational time and resources. For perpendicular impacts, both methods can reach a reliable accuracy, while for angled impacts, the accuracy decreases but the uncertainty provides additional information that can be further used to improve the classification. [ABSTRACT FROM AUTHOR]