Active Sensor Research Articles

Exposure time and point cloud quality prediction for active 3D imaging sensors using Gaussian process regression

Setting an optimal image exposure is crucial for acquiring dense point clouds using 3D active optical sensor systems such as structured light sensors [structured light sensors (SLSs)] and active stereo sensors. One of the most common and seamless ways to optimize the image brightness of an image exposure is to adjust the camera’s exposure time. However, optimizing the image exposure alone is ineffective for acquiring surfaces of large-scale objects with a complex topology if a spatial understanding of the scene is neglected. Hence, the present paper proposes a data-driven approach using two Gaussian processes [Gaussian processes (GPs)] regression models to select a proper exposure time considering the nonlinear correlations between image exposure and the scene spatial relationships. To model these correlations, our study introduces first the generic synthesization of seven inputs and two target variables. Then, based on these inputs, two independent GPs are designed: one for predicting the measurement quality and one for estimating the exposure time. The performance and generalizability of both models are thoroughly evaluated using an SLS and an active stereo sensor. The evaluation demonstrated that the point cloud quality models adequately matched observations with an R2 exceeding 90%. Specifically, the models predicted point cloud quality with an root mean square error (RMSE) of 10%. Additionally, the assessment of the performance of the exposure time models showed a model fit with an R2 above 97%. The exposure time prediction accuracy, as evidenced by the RMSE values, was within 10% of the corresponding exposure time range for each sensor. The present research shows the potential and effectiveness to completely automate the assessment of a point cloud quality and the selection of exposure times with the help of data-driven models.

Precision Beekeeping Systems: State of the Art, Pros and Cons, and Their Application as Tools for Advancing the Beekeeping Sector.

The present review aims to summarize the more recent scientific literature and updated state of the art on the research effort spent in adapting hardware-software tools to understand the true needs of honeybee colonies as a prerequisite for any sustainable management practice. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis was also performed with the aim of identifying the key factors that could support or impair the diffusion of precision beekeeping (PB) systems. Honeybee husbandry, or beekeeping, is starting to approach precision livestock farming (PLF), as has already happened in other animal husbandry sectors. A transition from the current paradigm of rational beekeeping to that of precision beekeeping (PB) is thus expected. However, due to the peculiarities of this species and the related farming practices, the PB technological systems (PB systems) are still undergoing a development process that, to some extent, limits their large-scale practical application. Several physical-chemical (weight, temperature, humidity, sound, gases) and behavioral traits (flight activity, swarming) of the hive are reviewed in light of the evolution of sensors, communication systems, and data management approaches. These advanced sensors are equipped with a microprocessor that records data and sends it to a remote server for processing. In this way, through a Wireless Sensor Network (WSN) system, the beekeeper, using specific applications on a personal computer, tablet, or smartphone, can have all the above-mentioned parameters under remote control. In general, weight, temperature, and humidity are the main hive traits monitored by commercial sensors. Surprisingly, flight activity sensors are rarely available as an option in modular PB systems marketed via the web. The SWOT analysis highlights that PB systems have promising strength points and represent great opportunities for the development of beekeeping; however, they have some weaknesses, represented especially by the high purchasing costs and the low preparedness of the addressed operators, and imply some possible threats for beekeeping in terms of unrealistic perception of the apiary status if they applied to some hives only and a possible adverse impact on the honeybees' colony itself. Even if more research is expected to take place in the next few years, indubitably, the success of commercial PB systems will be measured in terms of return on investment, conditioned especially by the benefits (higher yields, better colonies' health) that the beekeeper will appraise as a consequence of their use.

TRACES OF GLYPHOSATE IN CORN (Zea mays L.) AND AVOCADO (Persea americana Mill.) WEEDS SUPPORTED BY VANT AND RAMAN SPECTROSCOPY

Since the 1990s, radiometric and biophotonic sensor applications have provided reliable alternatives and rapid non-invasive diagnostics for the detection and delimitation of pests and diseases, as well as pesticide traces. Active sensors have been shown to be more accurate in detecting the heterogeneity of environmental factors based on thermal and infrared characteristics, chlorophyll fluorescence, and plant reflectance. Unmanned Aerial Vehicles (UAVs) equipped with infrared cameras locate and delimit weed abundance and diversity using Normalized Difference Vegetation Index (NDVI) algorithms, which highlight heterogeneous chlorophyll activity and herbicide-limited photosynthesis in crops. The resulting map serves as the foundation for collecting plant samples. In this study, the technology of these sensors was applied to determine glyphosate traces using Raman spectroscopy, which allowed a quick, low-cost, simple, and practical diagnosis with immediate results. These are essential characteristics for surveillance and monitoring activities, as well as the basis for a geo-referenced inventory of pesticides in production units. Of the 44 weed samples collected, eight were positive for glyphosate traces: two in corn and six in avocado. Weeds are identified as positive to glyphosate traces in Raman spectroscopy in the carotenoid and phenylpropanoid reduction ranges (1186 and 1213 cm-1) as a result of a decrease in the immune response. The vibrations presented by the amines in glyphosate and the C-OH of the carboxyl group are detected in the range of 1565 and 1567 cm-1.

A hybrid system for identifying the presence of clouds on the horizon line

Common passive surveys for the presence of clouds in the sky are carried out using the digital processing of photographs taken using fish-eye type lenses. Another type of survey is carried out using active measuring systems, e.g. thermal imaging, active laser distance sensors, etc. A common feature of these sensors is the high cost of the measuring apparatus, which is hardly acceptable for small local short-term weather forecasting stations. The main task of these weather stations is to forecast local dynamic weather phenomena that have (or may have) an impact on the use or operation of technical facilities (e.g. photovoltaic farms, cranes, etc.) or agricultural plant protection measures. This paper proposes a hybrid method for detecting clouds and predicting changes in cloud cover on the horizon line. The proposed method is based on an analysis of colour photographs of the cross-section of the sky using chrominance distribution by the RGB method. In the next step, the CCT colour temperature and the ALS-based ambient light intensity resulting from spectral analysis are determined for the cross-section of the sky being identified. The survey results indicate the dependence of the variability of CCT and ALS parameters for classifying objects on the horizon line. The above analysis makes it possible to distinguish the sky on the horizon, a cloud, or agglomeration elements. The data obtained from the surveys show that the proposed structure of the hybrid system for analysing the presence and movement of clouds on the horizon line can provide the basis for further development of data processing algorithms for a passive sensor of the apparatus for detecting clouds and other objects on the horizon line.

Reading Between the Heat

Stress impacts our physical and mental health as well as our social life. A passive and contactless indoor stress monitoring system can unlock numerous important applications such as workplace productivity assessment, smart homes, and personalized mental health monitoring. While the thermal signatures from a user's body captured by a thermal camera can provide important information about the "fight-flight" response of the sympathetic and parasympathetic nervous system, relying solely on thermal imaging for training a stress prediction model often lead to overfitting and consequently a suboptimal performance. This paper addresses this challenge by introducing ThermaStrain, a novel co-teaching framework that achieves high-stress prediction performance by transferring knowledge from the wearable modality to the contactless thermal modality. During training, ThermaStrain incorporates a wearable electrodermal activity (EDA) sensor to generate stress-indicative representations from thermal videos, emulating stress-indicative representations from a wearable EDA sensor. During testing, only thermal sensing is used, and stress-indicative patterns from thermal data and emulated EDA representations are extracted to improve stress assessment. The study collected a comprehensive dataset with thermal video and EDA data under various stress conditions and distances. ThermaStrain achieves an F1 score of 0.8293 in binary stress classification, outperforming the thermal-only baseline approach by over 9%. Extensive evaluations highlight ThermaStrain's effectiveness in recognizing stress-indicative attributes, its adaptability across distances and stress scenarios, real-time executability on edge platforms, its applicability to multi-individual sensing, ability to function on limited visibility and unfamiliar conditions, and the advantages of its co-teaching approach. These evaluations validate ThermaStrain's fidelity and its potential for enhancing stress assessment.

Automated High-Resolution Bathymetry from Sentinel-1 SAR Images in Deeper Nearshore Coastal Waters in Eastern Florida

Synthetic aperture radar (SAR) imagers are active microwave sensors that could overcome many challenges of passive optical bathymetry inversion, yet their capacity to yield accurate high-resolution bathymetric mapping is not studied sufficiently. In this study, we evaluate the feasibility of applying fast Fourier transform (FFT) to SAR data in coastal nearshore bathymetry derivation in Florida’s coastal waters. The study aims to develop a robust SAR bathymetry inversion framework across extensive spatial scales to address the dearth of bathymetric data in deeper nearshore coastal regions. By leveraging the Sentinel-1 datasets as a rich source of training data, our method yields high-resolution and accurate depth extraction up to 80 m. A comprehensive workflow to determine both the wavelength and peak wave period is associated with the proposed automated model compilation. A novel contour geometry-based spectral analysis technique for wavelength retrieval is presented that enables an efficient and scalable SAR bathymetry model. Multi-date SAR images were used to assess the robustness of the proposed depth-retrieval model. An accuracy assessment against the GMRT data demonstrated the high efficacy of the proposed approach, achieving a coefficient of determination (R2) above 0.95, a root-mean-square error (RMSE) of 1.56–10.20 m, and relative errors of 3.56–11.08% in automatically extracting the underwater terrain at every 50 m interval. A sensitivity analysis was conducted to estimate the uncertainty associated with our method. Overall, this study highlights the potential of SAR technology to produce updated, cost-effective, and accurate bathymetry maps of high resolution and to fill bathymetric data gaps worldwide. The code and datasets are made publicly available.

INSTANCE SEGMENTATION OF LIDAR DATA WITH VISION TRANSFORMER MODEL IN SUPPORT INUNDATION MAPPING UNDER FOREST CANOPY ENVIRONMENT

Abstract. Inundation mapping in forest and dense vegetated areas requires the ability to generate well defined Digital Terrain Models (DTM) to derive floodwater extent, depth, and duration. Due to the occlusion caused by overlapping leaves and branch structures of forest canopies, the ability to extract elevation point clouds through UAV and airborne optical imagery and photogrammetry is challenging. LiDAR is an active sensor that acquires direct 3D measurements by transmitting hundreds of thousands of laser measurements per second producing incredibly detailed mapping layers of not only the terrain but also forest attributes such as crown diameter, tree density and height that can support inundation mapping as well as hydrological models and monitoring of floods.In this research, we propose a methodology to map the inundated areas under canopies by using photon base Geiger Mode LiDAR point cloud dataset and a deep learning model to conduct instance segmentation of tree canopy. The method is to segment the vegetation from water and determine the gap fraction between trees to quantify the penetration through canopy for the detection of water pixels in vegetated areas. To conduct the segmentation Masked-attention Mask Transformer (Mask2Former) for universal segmentation model was implemented and trained to automate the extraction of tree crown segments from the LiDAR data. Furthermore, a semi-automatic experimental approach using a Canopy Height Model and watershed segmentation was applied to develop a rapid tree crown annotation strategy.

Kilometer-scale global warming simulations and active sensors reveal changes in tropical deep convection

Changes in tropical deep convection with global warming are a leading source of uncertainty for future climate projections. A comparison of the responses of active sensor measurements of cloud ice to interannual variability and next-generation global storm-resolving model (also known as k-scale models) simulations to global warming shows similar changes for events with the highest column-integrated ice. The changes reveal that the ice loading decreases outside the most active convection but increases at a rate of several percent per Kelvin surface warming in the most active convection. Disentangling thermodynamic and vertical velocity changes shows that the ice signal is strongly modulated by structural changes of the vertical wind field towards an intensification of strong convective updrafts with warming, suggesting that changes in ice loading are strongly influenced by changes in convective velocities, as well as a path toward extracting information about convective velocities from observations.

Plutonic Nature of a Transcrustal Magmatic System: Evidence from Ultrahigh Resolution Sr-Disequilibria in Plagioclase Microantecrysts from the Southern Taupo Volcanic Zone, New Zealand

Abstract The residence timescales of antecrystic minerals contribute a key piece of information regarding the petrologic evolution of transcrustal magmatic systems and may be inferred using a combination of observations derived from microanalytical chemistry and diffusion modelling. Here, we present state-of-the-art stacked CMOS-type active pixel sensor (SCAPS) isotopographic images of tephra-hosted plagioclase microantecrysts from Tongariro Volcanic Centre in the southern Taupo Volcanic Zone, New Zealand. These crystals exhibit high-frequency Sr and anorthite zonation at sub-micron spatial resolution. We also find that all crystals display high-frequency intracrystalline Sr chemical potential variations, indicating that they have not resided at magmatic temperature for diffusive relaxation to advance significantly. To quantify crystal residence times at the well-constrained magmatic temperatures of these tephras, we first forward-modeled intracrystalline Sr diffusion over time using numerical methods. Results were then analyzed using novel spatial Fourier-transform techniques developed to understand the systematics the diffusive decay of Sr disequilibria in the spatial frequency domain. This ultimately permitted the estimation of Sr concentration profiles at crystal formation, prior to uptake into the carrier melt at the onset of eruption. Our data imply residence times of hours to days for the studied microantecrysts. This is inconsistent with long antecryst residence times in magmatic mushes at elevated temperatures, pointing instead to a cool plutonic nature of the magmatic plumbing system beneath the southern Taupo Volcanic Zone.

Multi-analyte fluorescence sensing based on a post-synthetically functionalized two-dimensional Zn-MOF nanosheets featuring excited-state proton transfer process

Covalent post-synthetic modification of metal–organic frameworks (MOFs) represents an underexplored but promising avenue for allowing the addition of specific fluorescent recognition elements to produce the novel MOF-based sensory materials with multiple-analyte detection capability. Here, an excited-state proton transfer (ESPT) active sensor 2D-Zn-NS-P was designed and constructed by covalent post-synthetic incorporation of the excited-state tautomeric 2-hydroxypyridine moiety into the ultrasonically exfoliated amino-tagged 2D Zn-MOF nanosheets (2D-Zn-NS). The water-mediated ESPT process facilitates the highly accessible active sites incorporated on the surface of 2D-Zn-NS-P to specifically respond to the presence of water in common organic solvents via fluorescence turn-on behavior, and accurate quantification of trace amount of water in acetonitrile, acetone and ethanol was established using the as-synthesized nanosheet sensor with the detection sensitivity (<0.01% v/v) superior to the conventional Karl Fischer titration. Upon exposure to Fe3+ or Cr2O72−, the intense blue emission of the aqueous colloidal dispersion of 2D-Zn-NS-P was selectively quenched even in the coexistence of common inorganic interferents. The prohibition of the water-mediated ESPT process and local emission, induced by the coordination of ESPT fluorophore with Fe3+ or by Cr2O72− competitively absorbs the excitation energy, was proposed to responsible for the fluorescence turn-off sensing of the respective analytes. The present study offers the attractive prospect to develop the ESPT-based fluorescent MOF nanosheets by covalent post-synthetic modification strategy as multi-functional sensors for detection of target analytes.

Study of MAPS silicon detector prototypes for the ALICE Inner Tracking System upgrade

The ALICE experiment, which is located at the Large Hadron Collider (LHC) at CERN, has planned an upgrade of the Inner Tracking System (ITS), called ITS3, which will be installed during the LHC Long Shutdown 3, in 2026–2028. The cornerstone of the upgrade is a new 65 nm CMOS pixel chip, produced using Monolithic Active Pixel Sensor (MAPS) technology and the stitching technology to extend the chip length to 26 cm. The produced chips will be bent and they will replace the three innermost layers of the existing detector with new ultra-light, truly cylindrical layers. The ITS3 will improve tracking performance, especially at low transverse momentum, thanks to better track impact-parameter resolution, improved by a factor two with respect to the present ITS in LHC Run 3. In addition, the detector will be closer to the interaction point and will have a much lower material budget, of 0.05% X0/layer. This proceedings paper presents the final configuration and structure of the ITS3, the challenges related to its design and construction, and the results of the current R&D program on the sensor design and characterization. Finally, it also reports the results of beam tests preformed at CERN, where an analog pixel test chip (APTS Source Follower) was investigated in terms of detection efficiency.

Active‐Source Seismic Imaging of Fault Re‐Activation and Leakage: An Injection Experiment at the Mt Terri Rock Laboratory, Switzerland

AbstractWe conducted a time‐lapse seismic experiment utilizing automated active seismic source and sensor arrays to monitor a reactivated fault within the Opalinus clay formation at the Mont Terri Rock Laboratory (Switzerland), an analog caprock for geologic carbon storage. A series of six brine injections were conducted into the so‐called Main Fault to reactivate it. Seismic instrumentation in five monitoring boreholes on either side of the fault was used to continuously probe changes in P‐wave travel‐times associated with fault displacement and leakage. We performed time‐lapse travel‐time tomography on five hundred sequential data sets; this revealed a zone of decreased P‐wave velocity, up to 16 m/s, during each injection cycle, followed by a velocity increase during shut‐in. These results demonstrate varying elastic property perturbations, both spatially and temporally, along the fault plane during reactivation. We then interpreted these velocity changes in terms of fault dilation induced by pressurized fluids along the fault.

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose.

Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e-/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Implementation of Remote Activity Sensing to Support a Rehabilitation Aftercare Program: Observational Mixed Methods Study With Patients and Health Care Professionals.

Physical activity is central to maintaining the quality of life for patients with complex chronic conditions and is thus at the core of neurorehabilitation. However, maintaining activity improvements in daily life is challenging. The novel Stay With It program aims to promote physical activity after neurorehabilitation by cultivating self-monitoring skills and habits. We examined the implementation of the Stay With It program at the Valens Rehabilitation Centre in Switzerland using the normalization process theory framework, focusing on 3 research aims. We aimed to examine the challenges and facilitators of program implementation from the perspectives of patients and health care professionals. We aimed to evaluate the potential of activity sensors to support program implementation and patient acceptance. Finally, we aimed to evaluate patients' engagement in physical activity after rehabilitation, patients' self-reported achievement of home activity goals, and factors influencing physical activity. Patients were enrolled if they had a disease that was either chronic or at risk for chronicity and participated in the Stay With It program. Patients were assessed at baseline, the end of rehabilitation, and a 3-month follow-up. The health care professionals designated to deliver the program were surveyed before and after program implementation. We used a mixed methods approach combining standardized questionnaires, activity-sensing data (patients only), and free-text questions. This study included 23 patients and 13 health care professionals. The diverse needs of patients and organizational hurdles were major challenges to program implementation. Patients' intrinsic motivation and health care professionals' commitment to refining the program emerged as key facilitators. Both groups recognized the value of activity sensors in supporting program implementation and sustainability. Although patients appreciated the sensor's ability to monitor, motivate, and quantify activity, health care professionals saw the sensor as a motivational tool but expressed concerns about technical difficulties and potential inaccuracies. Physical activity levels after patients returned home varied considerably, both within and between individuals. The self-reported achievement of activity goals at home also varied, in part because of vague definitions. Common barriers to maintaining activity at home were declining health and fatigue often resulting from heat and pain. At the 3-month follow-up, 35% (8/23) of the patients withdrew from the study, with most citing deteriorating physical health as the reason and that monitoring and discussing their low activity would negatively affect their mental health. Integrating aftercare programs like Stay With It into routine care is vital for maintaining physical activity postrehabilitation. Although activity trackers show promise in promoting motivation through monitoring, they may lead to frustration during health declines. Their acceptability may also be influenced by an individual's health status, habits, and technical skills. Our study highlights the importance of considering health care professionals' perspectives when integrating new interventions into routine care.

Phospholipid Molecular Layer that Enhances Distinction of Odors Based on Artificial Sniffing.

We propose a novel odor-sensing system based on the dynamic response of phospholipid molecular layers for artificial olfaction. Organisms obtain information about their surroundings based on multidimensional information obtained from sniffing, i.e., periodic perturbations. Semiconductor- and receptor-based odor sensors have been developed previously. However, these sensors predominantly identify odors based on one-dimensional information, which limits the type of odor molecule they can identify. Therefore, the development of odor sensors that mimic the olfactory systems of living organisms is useful to overcome this limitation. In this study, we developed a novel odor-sensing system based on the dynamics of phospholipids that responds delicately to chemical substances at room temperature using multidimensional information obtained from periodic perturbations. Odor molecules are periodically supplied to the phospholipid molecular layer as an input sample. The waveform of the surface tension of the phospholipid molecular layer changes depending on the odor molecules and serves as an output. Such characteristic responses originating from the dynamics of odor molecules on the phospholipid molecular layer can be reproduced numerically. The phospholipid molecular layer amplified the information originating from the odor molecule, and the mechanism was evaluated by using surface pressure-area isotherms. This paper offers a platform for an interface-chemistry-based artificial sniffing system as an active sensor and a novel olfactory mechanism via physicochemical responses of the receptor-independent membranes of the organism.

DNA‐Barcoded Plasmonic Nanostructures for Activity‐Based Protease Sensing

AbstractWe report the development of a new class of protease activity sensors called DNA‐barcoded plasmonic nanostructures. These probes are comprised of gold nanoparticles functionalized with peptide‐DNA conjugates (GPDs), where the peptide is a substrate of the protease of interest. The DNA acts as a barcode identifying the peptide and facilitates signal amplification. Protease‐mediated peptide cleavage frees the DNA from the nanoparticle surface, which is subsequently measured via a CRISPR/Cas12a‐based assay as a proxy for protease activity. As proof‐of‐concept, we show activity‐based, multiplexed detection of the SARS‐CoV‐2‐associated protease, 3CL, and the apoptosis marker, caspase 3, with high sensitivity and selectivity. GPDs yield &gt;25‐fold turn‐on signals, 100‐fold improved response compared to commercial probes, and detection limits as low as 58 pM at room temperature. Moreover, nanomolar concentrations of proteases can be detected visually by leveraging the aggregation‐dependent color change of the gold nanoparticles. We showcase the clinical potential of GPDs by detecting a colorectal cancer‐associated protease, cathepsin B, in three different patient‐derived cell lines. Taken together, GPDs detect physiologically relevant concentrations of active proteases in challenging biological samples, require minimal sample processing, and offer unmatched multiplexing capabilities (mediated by DNA), making them powerful chemical tools for biosensing and disease diagnostics.

DNA-Barcoded Plasmonic Nanostructures for Activity-Based Protease Sensing.

We report the development of a new class of protease activity sensors called DNA-barcoded plasmonic nanostructures. These probes are comprised of gold nanoparticles functionalized with peptide-DNA conjugates (GPDs), where the peptide is a substrate of the protease of interest. The DNA acts as a barcode identifying the peptide and facilitates signal amplification. Protease-mediated peptide cleavage frees the DNA from the nanoparticle surface, which is subsequently measured via a CRISPR/Cas12a-based assay as a proxy for protease activity. As proof-of-concept, we show activity-based, multiplexed detection of the SARS-CoV-2-associated protease, 3CL, and the apoptosis marker, caspase 3, with high sensitivity and selectivity. GPDs yield >25-fold turn-on signals, 100-fold improved response compared to commercial probes, and detection limits as low as 58 pM at room temperature. Moreover, nanomolar concentrations of proteases can be detected visually by leveraging the aggregation-dependent color change of the gold nanoparticles. We showcase the clinical potential of GPDs by detecting a colorectal cancer-associated protease, cathepsin B, in three different patient-derived cell lines. Taken together, GPDs detect physiologically relevant concentrations of active proteases in challenging biological samples, require minimal sample processing, and offer unmatched multiplexing capabilities (mediated by DNA), making them powerful chemical tools for biosensing and disease diagnostics.

DETECT‐AD (Digital Evaluations and Technologies Enabling Clinical Translation for Alzheimer’s Disease): A simulated anti‐amyloid clinical trial using digital biomarkers as primary outcome measures

AbstractBackgroundEarly detection and precise monitoring of cognitive and functional decline in persons with Alzheimer’s Disease (AD) in clinical trials is a high priority. Unfortunately, early detection and monitoring of the subtle and slow changes of disease with conventional cognitive testing and self‐report questionnaires has been fraught with poor sensitivity and lack ecological validity. Digital biomarkers using ambient sensing technologies may afford a more effective approach to conducting trials. Remaining to be established is the ability of this approach to fundamentally improve early AD treatment response readout in a formal clinical trial setting. A simulated clinical trial (DETECT‐AD: ClinicalTrials.gov, NCT05385913) is being conducted to determine how well continuously assessed, home‐based digital biomarkers may detect clinically meaningful changes in individuals with changing amyloid (Aß) burdens in pre‐symptomatic AD.MethodClinically prodromal AD patients with estimable rates of AD progression based on Aß Florbetapir‐PET status are enrolled divided n = 50:n = 50 into Aß “positive” (higher amyloid burden) patients who predictably progress (as if they were receiving placebo) and Aß “negative” patients progressing more slowly (representing the treatment group). A study‐provided multivitamin mimics RCT conditions and study drug adherence behavior. Baseline standard clinical and biomarker measures are obtained along with digital biomarkers (DB) captured with passive and active sensors (actigraphy, bed mats, IR sensing, e‐pillbox, computing/phone device use, weekly online reporting/cognitive assessment). Primary outcome is the change in a composite DB composed of measures in 4 domains: mobility, cognition, sleep, and socialization. Secondary measures are the relationship of DB to fluid biomarkers (plasma ptau181/217, amyloid, NfL) and MRI/PET measures.ResultTo date (01/16/2023), 46/100 have been enrolled with mean(SD): follow‐up = 27.0(7.9) weeks (no dropout); age = 78(6.6); 63% women; MoCA = 25.5(2.3); PET SUVR = 1.09(0.20), range: 0.850‐2.07. Exemplary baseline digital biomarker metrics are presented in the Table. Preliminary data shows an inverse Aß‐SUVR correlation with mean steps/day (r = ‐.41, p = .012) and a positive correlation of MoCA with computer use (r = .61; p = .036).ConclusionHome‐based continuous capture of multi‐domain DBs is feasible in a clinical trial setting and may yield early meaningful indicators of clinically relevant changes in older adults with pre‐AD dementia. Further evidence will evolve as sample sizes and longitudinal follow‐up increases.

DETECT‐AD (Digital Evaluations and Technologies Enabling Clinical Translation for Alzheimer’s Disease): A simulated anti‐amyloid clinical trial using digital biomarkers as primary outcome measures

AbstractBackgroundEarly detection and precise monitoring of cognitive and functional decline in persons with Alzheimer’s Disease (AD) in clinical trials is a high priority. Unfortunately, early detection and monitoring of the subtle and slow changes of disease with conventional cognitive testing and self‐report questionnaires has been fraught with poor sensitivity and lack ecological validity. Digital biomarkers using ambient sensing technologies may afford a more effective approach to conducting trials. Remaining to be established is the ability of this approach to fundamentally improve early AD treatment response readout in a formal clinical trial setting. A simulated clinical trial (DETECT‐AD: ClinicalTrials.gov, NCT05385913) is being conducted to determine how well continuously assessed, home‐based digital biomarkers may detect clinically meaningful changes in individuals with changing amyloid (Aβ) burdens in pre‐symptomatic AD.MethodClinically prodromal AD patients with estimable rates of AD progression based on Aβ Florbetapir‐PET status are enrolled divided n = 50:n = 50 into Aβ “positive” (higher amyloid burden) patients who predictably progress (as if they were receiving placebo) and Aβ “negative” patients progressing more slowly (representing the treatment group). A study‐provided multivitamin mimics RCT conditions and study drug adherence behavior. Baseline standard clinical and biomarker measures are obtained along with digital biomarkers (DB) captured with passive and active sensors (actigraphy, bed mats, IR sensing, e‐pillbox, computing/phone device use, weekly online reporting/cognitive assessment). Primary outcome is the change in a composite DB composed of measures in 4 domains: mobility, cognition, sleep, and socialization. Secondary measures are the relationship of DB to fluid biomarkers (plasma ptau181/217, amyloid, NfL) and MRI/PET measures.ResultTo date (01/16/2023), 46/100 have been enrolled with mean(SD): follow‐up = 27.0(7.9) weeks (no dropout); age = 78(6.6); 63% women; MoCA = 25.5(2.3); PET SUVR = 1.09(0.20), range: 0.850‐2.07. Exemplary baseline digital biomarker metrics are presented in the Table. Preliminary data shows an inverse Aβ‐SUVR correlation with mean steps/day (r = ‐.41, p = .012) and a positive correlation of MoCA with computer use (r = .61; p = .036).ConclusionHome‐based continuous capture of multi‐domain DBs is feasible in a clinical trial setting and may yield early meaningful indicators of clinically relevant changes in older adults with pre‐AD dementia. Further evidence will evolve as sample sizes and longitudinal follow‐up increases.

First insights exploring Activities of Daily Living performance captured through activity sensors in a Smart Home Environment as an indicator of cognitive decline: A cross‐sectional Analysis

AbstractBackgroundSmart homes offer a unique potential not only for supporting various users’ needs but also for monitoring their activities. Herein the findings of the RADAR‐AD Smart Home study are presented. Using data from activity sensors installed in a smart home environment simulating real‐life conditions, we aim to detect deficits while performing Activities of Daily Living (ADLs) in three known groups of people in different stages of cognitive decline related to Alzheimer’s Disease (AD).MethodIn a fully equipped Smart Home (https://smarthome.iti.gr/), Fibaro smart plugs (to monitor power consumption), motion‐, door‐, flood‐sensors and panic buttons were installed. During their 24 hour visit, participants [13 Healthy Control (HC), 14 Preclinical AD (PreAD), 13 Prodromal AD (ProAD)] (Table 1) followed a protocol listing a number of ADLs (e.g., meal/beverage preparation) (Table 1). Data collection, feature extraction (i.e., activity duration, number of repetitions) and visualization of ADLs were performed through the CARL data collection and analysis platform for assisted living (https://carl.iti.gr/). (Figure 1). Two‐Way ANOVA and Mann‐Whitney were used to test the statistical significance between the groups.ResultComparing Activity Duration for different ADLs and groups (Figure 2), HC executed the ADLs in less time compared to PreAD and ProAD, while PreAD showed no difference in the majority of the tasks compared to ProAD. Three activities were completed by 50% of the HC, 36% of the PreAD and only 17% of the ProAD. Two‐way ANOVA revealed a statistically significant interaction between the effects of cognitive decline and Activity Duration F(3,106) = 3.504, p = 0.034. Mann‐Whitney analysis for the complex task “Meal Preparation” showed decreased duration for HC compared to PreAD (U = 33.00, p = 0.042) and ProAD (U = 17.00, p = 0.052).ConclusionThe RADAR‐AD Smart Home study provides a proof‐of‐concept for the use of home‐based sensors for investigating ADLs in patients with cognitive decline. This work has received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (grant No 806999). www.imi.europa.eu. This communication reflects the views of the RADAR‐AD consortium and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.