Consumer-grade Sensors Research Articles

Selfredepth

Depth maps produced by consumer-grade sensors suffer from inaccurate measurements and missing data from either system or scene-specific sources. Data-driven denoising algorithms can mitigate such problems; however, they require vast amounts of ground truth depth data. Recent research has tackled this limitation using self-supervised learning techniques, but it requires multiple RGB-D sensors. Moreover, most existing approaches focus on denoising single isolated depth maps or specific subjects of interest highlighting a need for methods that can effectively denoise depth maps in real-time dynamic environments. This paper extends state-of-the-art approaches for depth-denoising commodity depth devices, proposing SelfReDepth, a self-supervised deep learning technique for depth restoration, via denoising and hole-filling by inpainting of full-depth maps captured with RGB-D sensors. The algorithm targets depth data in video streams, utilizing multiple sequential depth frames coupled with color data to achieve high-quality depth videos with temporal coherence. Finally, SelfReDepth is designed to be compatible with various RGB-D sensors and usable in real-time scenarios as a pre-processing step before applying other depth-dependent algorithms. Our results demonstrate our approach’s real-time performance on real-world datasets shows that it outperforms state-of-the-art methods in denoising and restoration performance at over 30 fps on Commercial Depth Cameras, with potential benefits for augmented and mixed-reality applications.

Smartphone-Based Vibration Monitoring Studies of a Pedestrian Link Bridge

Vibration-based structural health monitoring has gained a new pace with the rise of emerging consumer-grade sensor technologies. Accelerometers embedded in smartphones are among those which provide ubiquitous data potential with minimal burden to administrators. This research presents the preliminary findings of modal identification results obtained from smartphone sensors positioned on a single-span pedestrian link bridge. While a good number of modal identification studies are available in the literature, most of them correspond to short-term tests which are unlikely to capture seasonal features impacting identification results. In this study, the authors summarise a large vibration dataset from the testbed bridge with repetitive measurements taken over a month. The identification results presented herein are early representations of the daily and monthly temporal behaviour of modal frequencies observed throughout the tests.

Automatic cybersickness detection by deep learning of augmented physiological data from off-the-shelf consumer-grade sensors

Cybersickness is still a prominent risk factor potentially affecting the usability of virtual reality applications. Automated real-time detection of cybersickness promises to support a better general understanding of the phenomena and to avoid and counteract its occurrence. It could be used to facilitate application optimization, that is, to systematically link potential causes (technical development and conceptual design decisions) to cybersickness in closed-loop user-centered development cycles. In addition, it could be used to monitor, warn, and hence safeguard users against any onset of cybersickness during a virtual reality exposure, especially in healthcare applications. This article presents a novel real-time-capable cybersickness detection method by deep learning of augmented physiological data. In contrast to related preliminary work, we are exploring a unique combination of mid-immersion ground truth elicitation, an unobtrusive wireless setup, and moderate training performance requirements. We developed a proof-of-concept prototype to compare (combinations of) convolutional neural networks, long short-term memory, and support vector machines with respect to detection performance. We demonstrate that the use of a conditional generative adversarial network-based data augmentation technique increases detection performance significantly and showcase the feasibility of real-time cybersickness detection in a genuine application example. Finally, a comprehensive performance analysis demonstrates that a four-layered bidirectional long short-term memory network with the developed data augmentation delivers superior performance (91.1% F1-score) for real-time cybersickness detection. To encourage replicability and reuse in future cybersickness studies, we released the code and the dataset as publicly available.

Abstract 3560: Quantified kinematics and wearable sensors to predict serious adverse events in patients undergoing cancer therapy trials

Abstract Background: Serious adverse events (SAEs) are a critical determinant of drug toxicity in clinical trials. However the drivers of toxicity may be related to patient factors as much or more than drug related factors. Prior studies have shown that time in non-sedentary activity predicted toxicity in cancer patients receiving highly emetogenic chemotherapy. SAEs are a critical determinant of drug toxicity in clinical trials. However, predicting SAEs using conventional PS scales measurements, such as ECOG is challenging because measurements can vary among observers. Prior studies have used activity trackers to measure physical activity and showed that time spent in non-sedentary activity predicted toxicity in cancer patients receiving highly emetogenic chemotherapy. Here, we sought to extend these findings to early phase clinical trial participants by examining data collected in two single-arm, observational, prospective studies at USC Keck and MD Anderson centers. We explored the use of out-of-clinic wearable sensors as an objective metric of frailty so as to identify patients at risk for therapy complications. Methods: Adult patients diagnosed with cancer and enrolled in early stage clinical trials across USC Keck and MD Anderson centers were included in parallel single-arm, observational, prospective studies. Patients used a consumer grade wearable activity tracker (Fitbit) and the study examined associations between the number of severe adverse events to activity levels measured by metabolic equivalents (METs) and get-up-go times across the first 30 days. Trends were assessed using t-tests. Results: 36 eligible patients were included in METs activity analysis. There was a total of 16 recorded incidences of SAEs within the first 30 study days, with 10 patients experiencing at least one SAE. The median non-sedentary time for patients with no SAEs was 27.5 hours above 1.5 METs per week, compared to 21.5 hours for those with at least one SAE occurrence (p-value=0.0146, r2=0.1629). The median get-up-go times for those without SAEs was 10.0 seconds (n=26) and with SAEs was 9.8 seconds (n=10), (p-value=0.828, r2=0.0014). Conclusions: Non-sedentary time, as an objective measure of activity, can be used to identify those patients who are at greatest risk for SAEs in clinical trials. Widely available and inexpensive consumer grade sensors can be used as a stratification tool. Findings should be confirmed in larger patient cohorts across a wider variety of trial settings. Figure 1: Weekly non-sedentary hours vs. presence of SAE in first 30 days of trial enrollment Citation Format: Eli Tran, Luciano Nocera, Anand Kolatkar, Anthony El-Khoueiry, Jacob Thomas, Peter Kuhn, Andrew Zeng, Megumi Kai, Meghan Karuturi, Jorge Nieva. Quantified kinematics and wearable sensors to predict serious adverse events in patients undergoing cancer therapy trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3560.

Performance evaluation of radon active sensors and passive dosimeters at low and high radon concentrations

Radon is a naturally occurring radioactive gas that has the potential to accumulate in buildings and over time, causes lung cancer in humans. Present methods for radon measurements are disparate, which pose challenges to benchmark radon concentrations and to accurately assess the population's received dose. This paper presents a comprehensive performance evaluation of radon dosimeters and three grades of active radon sensors: consumer-, medium- and research-grade. The measurements were performed at relatively low (300 Bq/m3) and high (2′000–3′000 Bq/m3) radon levels. Tests were conducted in an atomic shelter, with stable temperature and humidity conditions. The active sensors differed in absolute accuracy and dynamic performance (time-dependent correlations) according to their grade. Research-grade sensors performed marginally better than medium-grade sensors, and significantly better than consumer-grade sensors. Relative to the reference, the error (percentage difference between the reference and the sensors) was below 5 % for research- and medium-grade sensors, and nearly 10 % for consumer-grade sensors at high radon levels. Performance of sensors diminished at low radon levels, except for research-grade sensors. Passive dosimeters generally performed better at high radon levels than at low ones. Their longer exposure time was associated with increased measurement reliability. These results highlight the need for understanding the purpose of measurements in order to select an adequate radon detector, and ultimately, reduce measurement and interpretation errors. This study raises awareness among researchers, radon professionals and the general public regarding the performances of different active radon sensors and passive dosimeters. It also sheds light on their respective scope of application.

Citizen and machine learning-aided high-resolution mapping of urban heat exposure and stress

Through conversion of land cover to more built-up, impervious surfaces, cities create hotter environments than their surroundings for urban residents, with large differences expected between different parts of the city. Existing measurements of ambient air temperature and heat stress, however, are often insufficient to capture the intra-urban variability in heat exposure. This study provides a replicable method for modeling air temperature, humidity, and moist heat stress over the urban area of Chapel Hill while engaging citizens to collect high-temporal and spatially-resolved air temperature and humidity measurements. We use low-cost, consumer-grade sensors combined with satellite remote sensing data and machine learning to map urban air temperature and relative humidity over various land-cover classes to understand intra-urban spatial variability of ambient heat exposure at a relatively high resolution (10 m). Our findings show that individuals may be exposed to higher levels of air temperature and moist heat stress than weather station data suggest, and that the ambient heat exposure varies according to land cover type, with tree-covered land the coolest and built-up areas the warmest, and time of day, with higher air temperatures observed during the early afternoon. Combining our resulting dataset with sociodemographic data, policymakers and urban planners in Chapel Hill can use data output from this method to identify areas exposed to high temperature and moist heat stress as a first step to design effective mitigation measures.

Effect of heatwaves on PM2.5 levels in apartments of low-income elderly population. A case study using low-cost air quality monitors

Heatwaves are known to result in negative health effects in general and especially in vulnerable populations. At the same time, the effect of high outdoor temperatures on indoor air quality is largely unknown. To start filling this knowledge gap, we recruited 24 seniors from 3 low-income housing sites in Elizabeth, NJ, to participate in a study, during which we deployed consumer-grade sensors in their apartments to monitor airborne particulate matter (PM2.5) and air temperature. Additionally, one empty apartment, used as a control, and an outdoor station were set up with the same type of sensors. Measurements were performed from July to September 2017. During this period, there were seven days when outdoor temperatures exceeded 90 °F (32 °C), our criterion for heatwave days.First, we found that the average hourly indoor PM2.5 levels varied among apartments and were greatly affected by the presence of smokers. During non-heatwave days, in apartments without smokers, the hourly median PM2.5 concentrations ranged from 4 μg/m3 to 12 μg/m3, while in apartments with smokers, the hourly median PM2.5 concentrations ranged from 14 μg/m3 to 90 μg/m3. More importantly, the indoor PM2.5 levels were higher (p < 0.05) during heatwave days. A statistically significant increase was observed for all apartments, regardless of the building site, presence of smokers, or type of air conditioning. Moreover, since human activity contributes to indoor PM2.5, we separated the data into an active period (6:00 a.m. to 10:00 p.m.) and the rest period (10:00 p.m. to the next day 6:00 a.m.); the PM2.5 increase during heatwaves was statistically significantly higher for both periods.Overall, our data suggest that higher ambient temperatures could be an important factor for indoor PM2.5 exposures. Future investigations should consider several exposure-modifying factors, such as the use of windows and AC, for a more accurate assessment of outdoor conditions affecting indoor exposures.

Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network.

Oxygen uptake (V˙O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant use by consumers due to their costs, difficulty of operation and their intervening in the physical integrity of their users. Therefore, it is important to develop approaches for the indirect estimation of V˙O2-based measurements of motion parameters, heart rate data and application-specific measurements from consumer-grade sensors. Typically, these approaches are based on linear regression models or neural networks. This study investigates how motion data contribute to V˙O2 estimation accuracy during unconstrained running and walking. The results suggest that a long short term memory (LSTM) neural network can predict oxygen consumption with an accuracy of 2.49 mL/min/kg (95% limits of agreement) based only on speed, speed change, cadence and vertical oscillation measurements from an inertial navigation system combined with a Global Positioning System (INS/GPS) device developed by our group, worn on the torso. Combining motion data and heart rate data can significantly improve the V˙O2 estimation resulting in approximately 1.7-1.9 times smaller prediction errors than using only motion or heart rate data.

Information fusion of stationary, mobile, and wearable consumer-grade sensors to confidently estimate bedroom ventilation rates

This study adds to the body of research investigating ventilation rates measured in residential dwellings. Using open-source Consumer-Grade Sensors (CGS), we measure Carbon Dioxide (CO2) to estimate ventilation rates under one of three conditions: CO2build-up when bedrooms were initially occupied, steady-state CO2 during the latter half of participants’ sleep episodes, and CO2decay after participants wake and vacate their bedroom. We do not ask participants to record their bedroom occupancy, but use information fusion to identify nightly occupied periods by cross-referencing home addresses to GPS measurements provided by their smartphones during sleep episodes identified by Fitbit wearable fitness trackers. We estimate 106 ventilation rates during build-up conditions which ranged from 0.04–1.36 h−1, 242 ventilation rates under steady-state conditions spanning 0.17–2.49 h−1, and 39 ventilation rates from decay conditions over a range of 0.06–1.05 h−1. In addition, we used occupied periods alongside times when GPS confirmed participants were away from home to label Indoor Air Quality (IAQ) data, and to train a Multi-Layer Perceptron (MLP) to classify occupancy based on CO2 and Total Volatile Organic Compound (TVOC) measurements. Using periods classified as occupied with at least 90% confidence, we estimated 116 additional steady-state ventilation rates spanning 0.25–2.49 h−1. Our results are consistent with more traditional and costly sensing approaches, and we conclude that information fusion of CGS is effective in measuring IAQ, detecting occupancy, and estimating ventilation rates.

Design, fabrication, and calibration of the Building EnVironment and Occupancy (BEVO) Beacon: A rapidly-deployable and affordable indoor environmental quality monitor

Indoor Air Quality (IAQ) monitoring is essential to assess occupant exposure to the wide range of pollutants present in indoor environments. Accurate research-grade monitors are often used to monitor IAQ but the expense and logistics associated with these devices often limits the temporal and spatial scale of monitoring efforts. More affordable consumer-grade sensors – frequently referred to as low-cost sensors – can provide insight into IAQ conditions across greater scales but their accuracy and calibration requirements need further evaluation. In this paper, we present the Building EnVironment and Occupancy (BEVO) Beacon. The BEVO Beacon is entirely open-source, including the software, hardware, and design schematics which are all provided on GitHub. We created 20 of these standalone, stationary devices which measure up to 24 parameters at a one-minute resolution of which we focus on carbon dioxide, carbon monoxide, total volatile organic compounds, temperature, and size-resolved particulate matter. We investigated the efficacy of two different calibration approaches – device-specific and environment-averaged – for these sensors as well as also provide an extensive discussion considerations for each of the sensors. Calibrated sensors performed well when compared to reference monitors or calibrated gas standards. The CO sensors yielded the best agreement (r2=0.98-0.99), followed by temperature (r2=0.89-0.99), CO2 (r2=0.62-0.99), and PM2.5 (r2=-0.13-0.91). In all cases, the device-specific calibration approach yielded the most accurate results. We evaluated our devices through a successful 11-week field study where we monitored the IAQ in participants’ bedrooms. The work we present on consumer-grade sensors adds to the existing literature by considering sensor-specific calibration techniques and analysis. The BEVO Beacon adds to the successful line of similarly developed devices by providing an open-source framework that researchers can readily adapt and modify to their own applications.

A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human-Robot Interaction.

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human–robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of was achieved without data quality indication. The overall performance of the sensor system could be further improved to by monitoring the data quality, adding an additional layer of safety for human–robot interaction.

Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning.

Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development.

Insect Protein Content Analysis in Handcrafted Fitness Bars by NIR Spectroscopy. Gaussian Process Regression and Data Fusion for Performance Enhancement of Miniaturized Cost-Effective Consumer-Grade Sensors.

Future food supply will become increasingly dependent on edible material extracted from insects. The growing popularity of artisanal food products enhanced by insect proteins creates particular needs for establishing effective methods for quality control. This study focuses on developing rapid and efficient on-site quantitative analysis of protein content in handcrafted insect bars by miniaturized near-infrared (NIR) spectrometers. Benchtop (Büchi NIRFlex N-500) and three miniaturized (MicroNIR 1700 ES, Tellspec Enterprise Sensor and SCiO Sensor) in hyphenation to partial least squares regression (PLSR) and Gaussian process regression (GPR) calibration methods and data fusion concept were evaluated via test-set validation in performance of protein content analysis. These NIR spectrometers markedly differ by technical principles, operational characteristics and cost-effectiveness. In the non-destructive analysis of intact bars, the root mean square error of cross prediction (RMSEP) values were 0.611% (benchtop) and 0.545–0.659% (miniaturized) with PLSR, and 0.506% (benchtop) and 0.482–0.580% (miniaturized) with GPR calibration, while the analyzed total protein content was 19.3–23.0%. For milled samples, with PLSR the RMSEP values improved to 0.210% for benchtop spectrometer but remained in the inferior range of 0.525–0.571% for the miniaturized ones. GPR calibration improved the predictive performance of the miniaturized spectrometers, with RMSEP values of 0.230% (MicroNIR 1700 ES), 0.326% (Tellspec) and 0.338% (SCiO). Furthermore, Tellspec and SCiO sensors are consumer-oriented devices, and their combined use for enhanced performance remains a viable economical choice. With GPR calibration and test-set validation performed for fused (Tellspec + SCiO) data, the RMSEP values were improved to 0.517% (in the analysis of intact samples) and 0.295% (for milled samples).

Performance Evaluation of IMU and DVL Integration in Marine Navigation.

Global navigation satellite system (GNSS) spoofing poses a significant threat to maritime logistics. Many maritime electronic devices rely on GNSS time, positioning, and speed for safe vessel operation. In this study, inertial measurement unit (IMU) and Doppler velocity log (DVL) devices, which are important in the event of GNSS spoofing or outage, are considered in conventional navigation. A velocity integration method using IMU and DVL in terms of dead-reckoning is investigated in this study. GNSS has been widely used for ship navigation, but IMU, DVL, or combined IMU and DVL navigation have received little attention. Military-grade sensors are very expensive and generally cannot be utilized in smaller vessels. Therefore, this study focuses on the use of consumer-grade sensors. First, the performance of a micro electromechanical system (MEMS)-based yaw rate angle with DVL was evaluated using 60 min of raw data for a 50 m-long ship located in Tokyo Bay. Second, the performance of an IMU-MEMS using three gyroscopes and three accelerometers with DVL was evaluated using the same dataset. A gyrocompass, which is equipped on the ship, is used as a heading reference. The results proved that both methods could achieve less than 1 km horizontal error in 60 min.

Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches.

Inertial Measurement Units (IMUs) within an everyday consumer smartwatch offer a convenient and low-cost method to monitor the natural behaviour of hospital patients. However, their accuracy at quantifying limb motion, and clinical acceptability, have not yet been demonstrated. To this end we conducted a two-stage study: First, we compared the inertial accuracy of wrist-worn IMUs, both research-grade (Xsens MTw Awinda, and Axivity AX3) and consumer-grade (Apple Watch Series 3 and 5), and optical motion tracking (OptiTrack). Given the moderate to strong performance of the consumer-grade sensors, we then evaluated this sensor and surveyed the experiences and attitudes of hospital patients (N = 44) and staff (N = 15) following a clinical test in which patients wore smartwatches for 1.5–24 h in the second study. Results indicate that for acceleration, Xsens is more accurate than the Apple Series 5 and 3 smartwatches and Axivity AX3 (RMSE 1.66 ± 0.12 m·s−2; R2 0.78 ± 0.02; RMSE 2.29 ± 0.09 m·s−2; R2 0.56 ± 0.01; RMSE 2.14 ± 0.09 m·s−2; R2 0.49 ± 0.02; RMSE 4.12 ± 0.18 m·s−2; R2 0.34 ± 0.01 respectively). For angular velocity, Series 5 and 3 smartwatches achieved similar performances against Xsens with RMSE 0.22 ± 0.02 rad·s−1; R2 0.99 ± 0.00; and RMSE 0.18 ± 0.01 rad·s−1; R2 1.00± SE 0.00, respectively. Surveys indicated that in-patients and healthcare professionals strongly agreed that wearable motion sensors are easy to use, comfortable, unobtrusive, suitable for long-term use, and do not cause anxiety or limit daily activities. Our results suggest that consumer smartwatches achieved moderate to strong levels of accuracy compared to laboratory gold-standard and are acceptable for pervasive monitoring of motion/behaviour within hospital settings.

ON-SITE GEOMETRIC CALIBRATION OF THERMAL AND OPTICAL SENSORS FOR UAS PHOTOGRAMMETRY

Abstract. UAS imagery has become a widely used source of information in geomorphic research. When photogrammetric methods are applied to quantify geomorphic change, camera calibration is essential to ensure accuracy of the image measurements. Insufficient self-calibration based on survey data can induce systematic errors that can cause DEM deformations. The typically low geometric stability of consumer grade sensors necessitates in-situ calibration, as the reliability of a lab based calibration can be affected by transport. In this research a robust on-site workflow is proposed that allows the time-efficient and repeatable calibration of thermal and optical sensors at the same time. A stone building was utilised as calibration object with TLS scans for reference. The approach was applied to calculate eight separate camera calibrations using two sensors (DJI Phantom 4 Pro and Workswell WIRIS pro), two software solutions (Vision Measurement System (VMS) and Agisoft Metashape) and two different subsets of images per sensor. The presented results demonstrate that the approach is suitable to determine camera parameters for pre-calibrating photogrammetric surveys.

Real-World Consumer-Grade Sensor Signal Alignment Procedure Applied to High-Noise ECG to BCG Signal Synchronization.

In recent years, consumer-grade sensors that measure health relevant physiological signals have become widely available and are increasingly used by consumers and researchers alike. While this allows for multiple novel, potentially highly beneficial, large-scale health monitoring applications, quality of these data streams is oftentimes suboptimal. This makes alignment of different high-frequency data streams from multiple, non-connected sensors, a difficult task. In this work we describe a noise-robust framework to align high-frequency signals from different sensors, that share some underlying characteristic, obtained in a free-living, non-clinical, home environment. We demonstrate the approach on the basis of a single-lead, medical-grade, mobile electrocardiography device and a consumer-grade sleep sensor that allows for ballistocardiography. Both commercially available sensors measure the physiological process of a heartbeat. We show, on the basis of real-world data with multiple people and sensors, that the two highly noisy and sometimes dissimilar signals could in most cases be aligned with considerable precision. As a result, we could reduce mean heartbeat peak-to-peak difference by 58.1% on average and increase signal correlation by 0.40 on average.

Low-Burden Mobile Monitoring, Intervention, and Real-Time Analysis Using the Wear-IT Framework: Example and Usability Study.

BackgroundMobile health (mHealth) methods often rely on active input from participants, for example, in the form of self-report questionnaires delivered via web or smartphone, to measure health and behavioral indicators and deliver interventions in everyday life settings. For short-term studies or interventions, these techniques are deployed intensively, causing nontrivial participant burden. For cases where the goal is long-term maintenance, limited infrastructure exists to balance information needs with participant constraints. Yet, the increasing precision of passive sensors such as wearable physiology monitors, smartphone-based location history, and internet-of-things devices, in combination with statistical feature selection and adaptive interventions, have begun to make such things possible.ObjectiveIn this paper, we introduced Wear-IT, a smartphone app and cloud framework intended to begin addressing current limitations by allowing researchers to leverage commodity electronics and real-time decision making to optimize the amount of useful data collected while minimizing participant burden.MethodsThe Wear-IT framework uses real-time decision making to find more optimal tradeoffs between the utility of data collected and the burden placed on participants. Wear-IT integrates a variety of consumer-grade sensors and provides adaptive, personalized, and low-burden monitoring and intervention. Proof of concept examples are illustrated using artificial data. The results of qualitative interviews with users are provided.ResultsParticipants provided positive feedback about the ease of use of studies conducted using the Wear-IT framework. Users expressed positivity about their overall experience with the framework and its utility for balancing burden and excitement about future studies that real-time processing will enable.ConclusionsThe Wear-IT framework uses a combination of passive monitoring, real-time processing, and adaptive assessment and intervention to provide a balance between high-quality data collection and low participant burden. The framework presents an opportunity to deploy adaptive assessment and intervention designs that use real-time processing and provides a platform to study and overcome the challenges of long-term mHealth intervention.

IoT Ambient Assisted Living: Scalable Analytics Architecture and Flexible Process

With the recent advances in IoT, Ambient Assisted Living (AAL) became an active field that attempts to assist individuals in their Activities of Daily Living (ADL). One researched venue deriving from these advances is how the technology and analytics could benefit the prevention and treatment of chronic diseases in the escalating number of elderly people experiencing health issues. Many architectures are proposed in the literature, but they lack modularity and flexibility for different types of sensors and do not have a way of selecting the appropriate algorithms to perform a given task. In this paper, we propose a four layered and highly modular architecture for health analytics of elderly people. Moreover, we propose a novel automated process for selecting the appropriate algorithms for a task at hand. In the final analysis, we evaluate the approach by implementing part of the architecture on fog nodes and the cloud. Finally, we deploy affordable consumer grade sensors in an apartment in order to move toward the use of the system proposed.

Acoustic diagnostics of electrical origin fault modes with readily available consumer‐grade sensors

Acoustic diagnostics, traditionally associated with mechanical fault modes, can potentially solve a wider range of monitoring applications. Typically, fault modes are induced purposefully by the researcher through physical component damage whilst the system is shutdown. This study presents low-cost real-time fault diagnostics of a previously unreported acute electrical origin fault that manifests sporadically during system operation with no triggering intervention. A suitability study into acoustic measurements from readily available consumer-grade sensors for low-cost real-time diagnostics of audible faults, and a brief overview of the theory and configuration of the wavelet packet transform (including optimal wavelet selection methods) and empirical mode decomposition processing algorithms is also included. The example electrical origin fault studied here is an unpredictable current instability arising with the pulse-width modulation controller of a BrushLess DC motor. Experimental trials positively detect 99.9% of the 1160 resultant high-bandwidth torque transients using acoustic measurements from a USB microphone and a smartphone. While the use of acoustic techniques for detecting emerging electrical origin faults remains largely unexplored, the techniques demonstrated here can be readily adopted for the prevention of catastrophic failure of drive and power electronic components.