Non-intrusive Sensors Research Articles

0312 Dynamic Lighting in Memory Care Facilities to Improve Sleep and Mood in Older Adults with Dementia

Abstract Introduction Of the estimated 5.8 million people in the United States diagnosed with dementia, approximately one-third experience a chronic sleep disorder. Poor sleep quality in older adults is associated with higher levels of depression, anxiety, agitation, and falls as well as lower levels of cognitive and memory performance. Light is the most important environmental element that is not only necessary to fulfill visual tasks but also is the main stimulus that regulates circadian rhythms. This study aims to investigate the effect of a dynamic lighting intervention on sleep quality and mood in older adults with dementia living in long-term care facilities. Methods The study is conducted as a within-subject study design over 7 days of conventional lighting (Baseline) followed by 21 days of room lighting (Placebo) and then 21 days of dynamic lighting (Treatment). The dynamic lighting intervention is designed to create an ambient illumination that provides a high level of circadian stimulation in the morning and circadian-neutral lighting in the evenings through delivering varying light intensity and spectrum. The placebo condition exposes participants to a static lighting condition that is equivalent to the average room lighting from morning to evening. The human-lighting interaction occurs in the common areas of the selected memory care facility in Phoenix Greater Area, AZ. Participants of this study are individuals over the age of 65 diagnosed with mild to moderate dementia. Actigraphy is conducted on the last 7 days of each condition to monitor sleep quality. Daily light exposure is tracked continuously throughout the waking day using a non-intrusive wearable sensor. Moreover, caregivers complete two questionnaires (Cornell Scale for Depression in Dementia (CSDD) and Cohen-Mansfield Agitation Inventory (CMAI)) every seven days to assess the short and long-term effects of each condition on mood and agitation. Results Findings show notable improvements in mood and nighttime sleep duration and efficiency in participants after exposure to the dynamic condition compared to the baseline and placebo. Conclusion The outcomes of this study provide strong evidence that ambient lighting in memory care facilities is a significant contributor to improving sleep and quality of life of older adults with dementia. Support (if any) None

Supporting dementia care by monitoring indoor environmental quality in a nursing home

The indoor environment quality in care facilities impacts residents’ daily lives. Sensor technology has been proven useful in evaluating the indoor environment of buildings, but the process of using this information to improve residents’ comfort, specifically in the context of nursing homes, is still underdeveloped. Thus, this study focuses on monitoring indoor environmental quality in a nursing home in the Netherlands, supported by professional caregivers’ insights. A campaign was conducted for one year to monitor eight rooms, and a mixed-method approach was used to collect and analyse the data on local weather, building facility operation and proxies’ responses to residents’ comfort. Seventeen care professionals and twenty-two guardians (visitors) answered questions based on their opinions and observation. The results show that the low indoor humidity during heating seasons and high CO2 concentration at night were the main factors leading to environment-related symptoms of residents and care professionals, such as dry eyes. Furthermore, we found that data outliers can help to reveal residents’ occupancy, ventilation habits and activity time, thus showing the application value of environmental sensors in dementia care. We also recommend using non-intrusive sensors for nursing home residents and taking into account room layouts and occupants’ conditions before installing sensors.

Research on Non-Invasive Floating Ground Voltage Measurement and Calibration Method

Voltage measurement is an important part of power system operation, and non-intrusive voltage sensors have the advantages of low insulation difficulty, simple structure, easy loading and unloading, and high construction safety, which have become a new direction for voltage measurement. Based on the principle of electric field coupling, this paper constructs a non-intrusive floating ground three-capacitance voltage measurement model, which can complete the accurate measurement of voltage without connecting with the line to be measured and the earth in the measurement process. In non-intrusive voltage measurement, the change of the object to be measured or the measurement environment will cause the change of the coupling capacitance, which leads to the uncertainty of the transmission relationship of the sensor and the large error of measurement results. In order to solve this problem, a new method of sensor calibration is proposed in this paper. By sampling capacitance in parallel between two electrodes of the sensor, changing the capacitance value, and establishing an input output equation, the coupling capacitance value and the voltage value to be measured under different operating conditions are solved. In addition, the sampling capacitance is often several orders of magnitude larger than the sensor’s own capacitance, making the sensor’s voltage division ratio significantly higher and more conducive to the measurement of high voltages. The experimental results show that the measurement error is less than 2%, which verifies the feasibility of the method and the accuracy of the voltage measurement.

Broccoli-shaped nano florets-based gastrointestinal diseases detection by copper oxide chitosan

This report describes a simple and inexpensive room-temperature chemo-resistive CuO-Chitosan-based hydrogen sensor for detecting gastrointestinal diseases (GIDs). A clinical analysis shows that GID patients exhale more than 10–20 ppm of hydrogen compared to healthy people, which is considered a biomarker for the non-invasive diagnosis of GIDs. This allows for a portable, non-intrusive, effective, and fast-detecting sensor in treating GIDs. To our knowledge, no GIDs study has used chitosan for H2 sensing. The current study prepares CuO-Chi nanocomposites and tests their hydrogen sensitivity for GIDs detection. Different ratios of CuO: chitosan [CuO: Chi (A(1:2), B(1:1), C(1:0.5), D(1:0.25), E(1:0.125), and F(1:0.0625)]were synthesized using novel co-precipitation cum probe sonication technique. Compared to other CuO composites in the literature, our synthesized CuO: Chi-B (1:1) can detect as low as 10 ppm of hydrogen gas at room temperature, with a lower limit of detection (0.07 ppm) and a regression coefficient (R2) of 0.9566. The Contact angle explains the anti-wettability property with the increase in the hydrophilicity due to the presence of electronegative atoms like N in NH2 and O in the OH group. Field emission scanning electron microscopy reveals several single broccoli nano florets. Most importantly, the sensor's calculated Brunauer–Emmett–Teller surface area is 5.1565 m2/g with a pore volume of 0.02173cm2/gm and pore size of 16.897 nm. It has excellent sensitivity, response, recovery, reproducibility, stability, and selectivity. The reaction mechanism for sensor performance is elaborately discussed by the Grotthuss mechanism. The above studies suggest and show that CuO: Chi-B (1:1) is a new material for hydrogen sensors that can be used for the breath analysis to detect GIDs at 10 ppm.

An embedded non-intrusive graphene/epoxy broadband nanocomposite sensor co-cured with GFRP for in situ structural health monitoring

Glass fiber-reinforced polymer (GFRP) composites degrade continuously through service, entailing effective health monitoring to ensure structural integrity. We develop a new type of GFRP structure, with the embedded non-intrusive sensors for in situ structural health monitoring (SHM). Piezoresistive nanocomposite sensors – a pre-cured hybrid of graphene nanoplatelet (GNP) and epoxy, are molded in the second lamina of GFRP prepreg with epoxy matrix during lay-up to laminate process and then co-cured with GFRP composite, keeping the continuity at the interface between the composite. The GNP/epoxy sensor successfully captures the structural response from static strain to a high frequency ultrasonic wave up to 600 kHz with a gauge factor up to 26, which is the first ever nonintrusive GNP/epoxy piezoresistive sensor with an ultra-wide frequency response to mechanical deformation. Furthermore, the mechanical properties of host composite show negligible signs of intrusion testified with tensile and bending tests. GFRP with embedded sensor features merits of high strength from GFRP as well as high-sensitivity sensing capability from the nanocomposite sensors, opening up a new avenue to in situ health monitoring of composites.

Improved phase fraction measurement via non-intrusive optical sensing for vertical upward gas -liquid flow

This paper presents the design and application of a non-intrusive optical sensor for the accurate measurement of phase fractions under a vertical upward gas-liquid flow. The sensor was made of two pairs of emitter and photo receivers operated at a wavelength of 1480 nm. The measurement principle of the sensor is based on the disparity in refractive indexes of gas and liquid that provides a proportional relationship between the phase fraction and light intensity. A flow regime dependent calibration model was then developed which corrects for structural scattering derived from the calibrated sensor responses. The model was able to measure local and average phase fractions under varied flow regimes with errors of ± 1.25% and ± 2% relative to photography and swell level methods respectively with a combined uncertainty of ± 2.3%. Comparisons of the non-intrusive optical sensor with the homogenous, Drift flux and Armard phase fraction correlations showed reasonable agreements. The concept of slip as a dominant effect for flow regime in the slug, churn and annular flow regimes accounted for these disparities. The work demonstrates the efficacy of a low cost, non-intrusive method to determine phase fraction in two phase flow over a wide range of flow conditions.

Occupant privacy perception, awareness, and preferences in smart office environments

Building management systems tout numerous benefits, such as energy efficiency and occupant comfort but rely on vast amounts of data from various sensors. Advancements in machine learning algorithms make it possible to extract personal information about occupants and their activities beyond the intended design of a non-intrusive sensor. However, occupants are not informed of data collection and possess different privacy preferences and thresholds for privacy loss. While privacy perceptions and preferences are most understood in smart homes, limited studies have evaluated these factors in smart office buildings, where there are more users and different privacy risks. To better understand occupants’ perceptions and privacy preferences, we conducted twenty-four semi-structured interviews between April 2022 and May 2022 on occupants of a smart office building. We found that data modality features and personal features contribute to people’s privacy preferences. The features of the collected modality define data modality features – spatial, security, and temporal context. In contrast, personal features consist of one’s awareness of data modality features and data inferences, definitions of privacy and security, and the available rewards and utility. Our proposed model of people’s privacy preferences in smart office buildings helps design more effective measures to improve people’s privacy.

Intelligent Model for the Reliability of the Non-Intrusive Continuous Sensors Used for the Detection of Fouling-Layer in Heat Exchanger System

Faults in this sensor must be detected on time to ensure the functionality of the entire system’s architecture and to maintain system balance, which will keep false positive rates low during the system’s operational period. False positives reduce diagnostic confidence and necessitate unnecessary and costly mitigation actions, lowering system productivity. It is on this basis that this study proposes a clustering model algorithm (K-mean clustering) to investigate and manage the reliability and performance of the sensors. The results from the implementation of the K-mean clustering method show that the running of the algorithm fits the model correctly, both for the training of the dataset and for the prediction of the cluster in each of the datasets considered. A reasonable grouping was found for the two and three clusters considered, which are represented by the colors (blue, orange, and green). These colors indicate the fault state, non-fault state, normal state, and abnormal state of the non-intrusive continuous sensor. The simulated results show the fault state in the blue region and the non-fault state in the orange region for the two clusters, while the normal state is in the blue region and the abnormal state is in the orange and green regions for the three clusters considered.

Nonintrusive Sensor System Developed Using a Force-Sensing Resistor for Pipeline Integrity Monitoring

Pipelines are crucial for transporting liquid and gaseous resources at various industrial sites. As unexpected pipeline failures and improper spill responses often cause detrimental effects on the environment and public safety, real-time leakage monitoring and detection are important. This study proposes a nonintrusive sensor system to measure the pressure and integrity of a pipeline in real time by easily installing nonintrusive sensors on the exterior of the pipelines. Initially, a prototype of the nonintrusive pressure-monitoring sensor was designed and fabricated. This sensor correlated the changes in pressure inside a pipe with those in the contact force exerted between the pipe and the surrounding high-stiffness band; a force-sensing resistor was used to measure the contact force. Furthermore, parametric studies were performed on a six-inch pipe, which indicated that the developed sensor can accurately monitor the inner pressure of the pipe. We also developed a methodology to determine the optimal sensor placement in a pipeline network for detecting damages using the Euclidean distance method. This was combined with the methodology of determining the decision boundary for classifying the damage location using a support vector machine to rapidly detect the damage using a minimum number of sensors. Finally, a nonintrusive sensor system was installed on the fire main pipeline system, and the damage detection performance was experimentally validated. The developed nonintrusive sensor system can be easily installed on operating pipes to monitor the inner pressure of a pipe in real-time and accurately detect damages in a pipeline network.

Vehicular Flow Characterization: An Internet of Video Things-Based Solution

Intelligent transportation systems (ITS) have emerged as the optimal solution to address urban mobility challenges. However, to effectively implement ITS, detailed traffic flow statistics are imperative. Various solutions have been proposed, including intrusive/non-intrusive sensors and compute vision-based solutions. However, these solutions have limitations in the number of measured traffic flow parameters, cost or performance under different traffic conditions. To overcome these limitations, we propose an Internet-of-Video-Things (IoVT) based solution. The sensor node (fabricated using Raspberry Pi Zero W, Pi camera, power bank, and Wi-Fi device) can live-streaming roadside traffic video to a remote Dell server located at our lab with Camlytics (commercially available traffic analysis software) installed. The proposed solution was field tested with a 45-minute live-streamed video of 720p at 25 frames per second. Results show that the proposed solution can measure more traffic flow parameters than intrusive and non-intrusive sensors, with an accuracy of 84.3% for vehicle count and speed estimation. Other parameters were also calculated, such as time/distance headway, spatial/temporal densities, heat maps, and trajectories. Additionally, the proposed solution can count pedestrians with an accuracy of 76.3%.

Acoustic monitoring of an aircraft auxiliary power unit

In this paper, the development and implementation of a novel approach for fault detection of an aircraft auxiliary power unit (APU) has been demonstrated. The developed approach aims to target the proactive identification of faults, in order to streamline the required maintenance and maximize the aircraft’s operational availability. The existing techniques rely heavily on the installation of multiple types of intrusive sensors throughout the APU and therefore present a limited potential for deployment on an actual aircraft due to space constraints, accessibility issues as well as associated development and certification requirements. To overcome these challenges, an innovative approach based on non-intrusive sensors i.e., microphones in conjunction with appropriate feature extraction, classification, and regression techniques, has been successfully demonstrated for online fault detection of an APU. The overall approach has been implemented and validated based on the experimental test data acquired from Cranfield University’s Boeing 737-400 aircraft, including the quantification of sensor location sensitivities on the efficacy of the acquired models. The findings of the overall analysis suggest that the acoustic-based models can accurately enable near real-time detection of faulty conditions i.e., Inlet Guide Vane malfunction, reduced mass flows through the Load Compressor and Bleed Valve malfunction, using only two microphones installed in the periphery of the APU. This study constitutes an enabling technology for robust, cost-effective, and efficient in-situ monitoring of an aircraft APU and potentially other associated thermal systems i.e., environmental control system, fuel system, and engines.

An Intelligent Sensing Framework for Post-Manufacturing Performance Measurement and Healing of CSVCO

With CMOS technology scaling for miniaturization and performance improvement, analog and radio frequency integrated circuits (RFICs) are very much affected by process variations leading to performance degradation. A post-manufacturing healing mechanism with low overhead is desirable to compensate for these variations. A machine learning (ML)-assisted sensing mechanism has been proposed in this work for design of nonintrusive process variation sensor (PVS). Low-cost test signatures (TSs) extracted from these PVS are placed close to the circuit under test (CUT) and this does not load or couple to any nodes of CUT. As the TSs remain invariant with the shifts in control knobs (CKs), performance healing against process variations can be achieved with a single set of extracted TSs. A novel one-step healing approach is proposed in this work for selection of appropriate CK with the help of a neural regression model during the production test in automated test equipment (ATE). The proposed healing approach is illustrated on a current-starved voltage-controlled oscillator (CSVCO) circuit, to recover yield loss due to fabrication process variations.

Remote Pedestrian Localization Systems for Resource-Constrained Environments: A Systematic Review

The steady increase in the number of elderly citizens represents a likelihood of an increased burden on the family, government, healthcare, and social services since many of these elderly people cannot live independently without assistance from a caregiver. As such, there is an increase in demand for services in terms of technologies to address the urgent needs of the aging population. Remote monitoring, which is based on non-invasive, non-intrusive, and wearable sensors, actuators, and communication and information technologies, offers efficient solutions that bridge the gaps between healthcare and where elderly people really want to live every day. The rate at which such platforms have been adopted is extremely low in low-developed countries and rural areas, one of the main reasons being the lack or scarcity of some resources that these systems take for granted. In other words, these systems are designed for developed countries but are very much needed in resource-constrained environments as well. This study provides an in-depth, state-of-the-art systematic review of the current outdoor remote pedestrian localization systems to identify their suitability for resource-constrained environments. After checking 35 survey papers from the last ten years to the best of our knowledge, this is the first survey that investigates the suitability of existing pedestrian localization systems for a resource-constrained environment. This study is based on PRISMA guidelines to provide a replicable work and report the studies’ main findings. A total of 37 works published between 2012, and January 2023 have been identified, analyzed, and key information that described the devices and tools used, communication technologies, position estimate technologies, methods, techniques and algorithms, and resource optimization strategies currently used by the localization systems was extracted to help us answer our question. The results indicate they are not fit for a resource-constrained environment as most assume the availability of infrastructures such as Wi-Fi, Internet, cellular networks, and digital literacy, among others, for their systems to operate properly, which are limited or not available in the resource-constrained environment described in this review.

Microgrid Protection Using Magneto-Resistive Sensors and Superimposed Reactive Energy

The concept of microgrids has emerged as an effective way to integrate distributed energy resources (DERs) into distribution networks. The presence of DERs in microgrids leads to challenges in the formulation of protection for microgrids. Protection problems arise in a microgrid due to varying fault current levels in different operating scenarios. In order to overcome the practical challenges arising from varying fault current levels leading to short-circuit faults in microgrids, this paper proposes a MagnetoResistive (MR) sensors-based protection scheme, with fault localization through SuperimposedReactiveEnergy (SRE). The process is initiated by employing highly sensitive non-intrusive magnetic sensors to detect the magnetic field at each end of the distribution line. The magnetic field is then used to calculate the total harmonic distortion and thus detect faults in microgrids. After detection of faults, the proposed scheme uses SRE to identify faulty zones in microgrids. Finally, SI components of the current are extracted for fault classification. Extensive simulations on the International Electro-technical Commission (IEC) microgrid are performed in MATLAB/Simulink to validate the efficacy of the proposed scheme. Simulation results show that the proposed scheme can effectively detect, classify and isolate different faults in microgrids, while operating under various modes with varying fault locations and resistances, with the efficiency of approximately 97–98%.

Non-intrusive microwave technique for direct detection of concrete compressive strength monitoring by multivariate modeling

This paper investigates using microwaves from 1.5 to 14 GHz for monitoring concrete compressive strength in real-time to implement a non-intrusive sensor. The approach is based on acquiring reflected microwave spectra by the concrete irradiated through a rectangular cavity antenna connected to a Vector Network Analyzer (VNA) and investigating their relationship with strength. Since the information is hidden in the spectrum, we used the multivariate statistical analysis technique to create both classification and regression models. The acquired Voltage Standing Wave Ratio (VSWR) spectra were statistically inferred with compressive strength from destructive tests. The experimentation was performed on 16 concrete samples, lasted 28 days, and the acquired data consisted in 409 spectra where the reflected spectra are correlated with the compressive strength during curing time. The inference processes show high predictability, with an F1 score from 0.91 to 0.99 for classification and a cross-validation value of the parameter R2 of 0.997.

Non-intrusive closed-loop current sensor based on tunneling magnetoresistance

In this paper, a non-intrusive closed-loop current sensor based on high-sensitivity tunneling magnetoresistance (TMR) were demonstrated. Using the finite element modeling based on Maxwell’s electromagnetic theory, the distribution of magnetic field on the closed-loop magnetic flux concentrator (MFC) was calculated with a copper busbar passing through the center. Two different types with “slot” and “hole” were designed to place TMR sensors for the detection of the magnetic field generated by the working copper busbar. A secondary winding around MFC generated a reverse magnetic field to completely offset the magnetic field of the primary current, which realizes the real-time adjustment and monitoring of the current by the TMR sensor with magnetic flux balance. Moreover, the simulated results show that the performance of slot-gap MFC is better than that with hole-type design. Utilizing the experimental TMR sensor with sensitivity of 10 mV/V/Oe and linear field range of ±40 Oe, the monitoring current can reach up to 200 A. The described application in the current monitoring demonstrates the functionality and feasibility of TMR sensors.

Graph Theoretical Analysis of EEG Functional Connectivity Patterns and Fusion with Physiological Signals for Emotion Recognition.

Emotion recognition is a key attribute for realizing advances in human-computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results.

SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home

Nowadays, one of the most important objectives in health research is the improvement of the living conditions and well-being of the elderly, especially those who live alone. These people may experience undesired or dangerous situations in their daily life at home due to physical, sensorial or cognitive limitations, such as forgetting their medication or wrong eating habits. This work focuses on the development of a database in a home, through non-intrusive technology, where several users are residing by combining: a set of non-intrusive sensors which captures events that occur in the house, a positioning system through triangulation using beacons and a system for monitoring the user's state through activity wristbands. Two months of uninterrupted measurements were obtained on the daily habits of 2 people who live with a pet and receive sporadic visits, in which 18 different types of activities were labelled. In order to validate the data, a system for the real-time recognition of the activities carried out by these residents was developed using different current Deep Learning (DL) techniques based on neural networks, such as Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM) or Gated Recurrent Unit networks (GRU). A personalised prediction model was developed for each user, resulting in hit rates ranging from 88.29% to 90.91%. Finally, a data sharing algorithm has been developed to improve the generalisability of the model and to avoid overtraining the neural network.

Gas-liquid flow regime identification via a non-intrusive optical sensor combined with polynomial regression and linear discriminant analysis

The identification of gas-liquid flow regimes for co-current upward flow in a vertical pipe is investigated using Polynomial regression (PR) and Linear Discriminant analysis (LDA) on the response from a non-intrusive optical sensor. The average voltage and associated standard deviation can be extracted as features from the sensor and used as inputs to establish the coefficients of an optimal regression model. Further to this, due to the structural similarity between the slug and churn flow regimes, further discrimination was required in the form of a Linear discriminant analysis to give accurate classification between these two flow regimes. According to the offline test and real-time flow conditions considered, a classification accuracy of 100% is achieved. The results of this paper show the capability of applying a relatively simple supervised regression model and LDA to the response from a non-intrusive optical sensor to identify gas-liquid flow regimes efficiently and accurately.

Vehicular Traffic Flow Characterization: An Edge Computing Perspective

With rapid urbanization, road network inefficiencies have become a primary impediment for developing sustainable smart cities. Associated challenges range from traffic congestion, ambient pollution, accidents, and lost productivity. In this regard, intelligent transportation system (ITS) based solutions are being proposed to design and develop smart urban mobility. However, real-world traffic flow parameters (such as vehicular count, flow, classification, speed, road capacity, time/distance headway, temporal/spatial densities, heatmaps, and trajectories) are fundamental building blocks for designing and developing ITS solutions. In existing literature, varying solutions have been proposed for traffic flow characterization. These can be categorized as either intrusive or non-intrusive sensors. However, these solutions have serious limitations, especially under heterogeneous traffic flow behavior. To overcome these limitations, compute vision-based solutions have emerged as optimum traffic flow characterization solutions. In this work, all compute vision-based edge computing solutions in existing literature have been reported and analyzed. Through comparative analysis, it is concluded that edge computing solutions are the optimum choice as compared to Internet-of-Video-Things based solutions. However, edge computing solutions are seriously constrained because of the compute resources of single board computers. To overcome this limitation, mathematical traffic flow characterization equations have been reported for calculating eight additional traffic flow parameters. These mathematical equations can optimize the already proposed edge computing solution’s performance by 400%.