Indoor Sensor Research Articles

Fusing Transformer and diffusion for high-resolution prediction of daylight illuminance and glare based on sparse ceiling-mounted input

Accurate prediction of workplane daylight illuminance and eye-height glare is crucial for lighting control. Existing studies used machine learning to predict illuminance at predetermined locations based on indoor sensors, but they may encounter challenges in scenarios 1) with flexible seating arrangements, 2) with dynamic shading devices, and 3) requiring the prediction of glare. To address these challenges, we proposed a novel method fusing Transformer and Diffusion models, with the input being data collected from sparse ceiling-mounted illuminance sensors, and the outputs being high-resolution workplane illuminance and glare. The model works well for rooms without and with dynamic roller shades. For the former, the mean absolute errors for illuminance below 3000 lx and Daylight Glare Index (DGI) are only 20.77 lx and 0.20, and the error rates in detecting illuminance <500 lx and DGI>22 are only 0.85 % and 5.55 %. For the more complicated latter case, the aforementioned four numbers are 34.78 lx, 0.59, 2.47 % and 23.13 %. The model significantly outperforms the linear and the ANN models, particularly in glare prediction. The influence of sensor number and placement strategy on model performance was also revealed. The model can potentially enhance lighting control, especially in cases with dynamic shading, with flexible seating arrangements, and where glare is of interest.

Low-Cost Indoor Sensor Deployment for Predicting PM2.5 Exposure.

Indoor air quality is critical to human health, as individuals spend an average of 90% of their time indoors. However, indoor particulate matter (PM) sensor networks are not deployed as often as outdoor sensor networks. In this study, indoor PM2.5 exposure is investigated via 2 low-cost sensor networks in Pittsburgh. The concentrations reported by the networks were fed into a Monte Carlo simulation to predict daily PM2.5 exposure for 4 demographics (indoor workers, outdoor workers, schoolchildren, and retirees). Additionally, this study compares the effects of 4 different correction factors on reported concentrations from the PurpleAir sensors, including both empirical and physics-based corrections. The results of the Monte Carlo simulation show that mean PM2.5 exposure varied by 1.5 μg/m3 or less when indoor and outdoor concentrations were similar. When indoor PM concentrations were lower than outdoor, increasing the time spent outdoors on the simulation increased exposure by up to 3 μg/m3. These differences in exposure highlight the importance of carefully selecting sites for sensor deployment and show the value of having a robust low-cost sensor network with both indoor and outdoor sensor placement.

Climate projections of human thermal comfort for indoor workplaces

Climate models predict meteorological variables for outdoor spaces. Nevertheless, most people work indoors and are affected by heat indoors. We present an approach to transfer climate projections from outdoors to climate projections of indoor air temperature (Ti) and thermal comfort based on a combination of indoor sensors, artificial neural networks (ANNs), and 22 regional climate projections. Human thermal comfort and Ti measured by indoor sensors at 90 different workplaces in the Upper Rhine Valley were used as training data for ANN models predicting indoor conditions as a function of outdoor weather. Workplace-specific climate projections were modeled for the time period 2070–2099 and compared to the historical period 1970–1999 using the same ANNs, but ERA5-Land reanalysis data as input. It is shown that heat stress indoors will increase in intensity, frequency, and duration at almost all investigated workplaces. The rate of increase depends on building and room properties, the workplace purpose, and the representative concentration pathway (RCP2.6, RCP4.5, or RCP8.5). The projected increase of the mean air temperature in the summer (JJA) outdoors, by + 1.6 to + 5.1 K for the different RCPs, is higher than the increase in Ti at all 90 workplaces, which experience on average an increase of + 0.8 to + 2.5 K. The overall frequency of heat stress is higher at most workplaces than outdoors for the historical and the future period. The projected hours of indoor heat stress will increase on average by + 379 h, + 654 h, and + 1209 h under RCP2.6, RCP4.5, and RCP8.5, respectively.

Developing Climate Change and Health Impact Monitoring with eHealth at the South East Asia Community Observatory and Health and Demographic Surveillance Site, Malaysia (CHIMES).

Malaysia is projected to experience an increase in heat, rainfall, rainfall variability, dry spells, thunderstorms, and high winds due to climate change. This may lead to a rise in heat-related mortality, reduced nutritional security, and potential migration due to uninhabitable land. Currently, there is limited data regarding the health implications of climate change on the Malaysian populace, which hinders informed decision-making and interventions. This study aims to assess the feasibility and reliability of using sensor-based devices to enhance climate change and health research within the SEACO health and demographic surveillance site (HDSS) in Malaysia. We will particularly focus on the effects of climate-sensitive diseases, emphasizing lung conditions like chronic obstructive pulmonary disease (COPD) and asthma. In our mixed-methods approach, 120 participants (>18 years) from the SEACO HDSS in Segamat, Malaysia, will be engaged over three cycles, each lasting 3 weeks. Participants will use wearables to monitor heart rate, activity, and sleep. Indoor sensors will measure temperature in indoor living spaces, while 3D-printed weather stations will track indoor temperature and humidity. In each cycle, a minimum of 10 participants at high risk for COPD or asthma will be identified. Through interviews and questionnaires, we will evaluate the devices' reliability, the prevalence of climate-sensitive lung diseases, and their correlation with environmental factors, like heat and humidity. We anticipate that the sensor-based measurements will offer a comprehensive understanding of the interplay between climate-sensitive diseases and weather variables. The data is expected to reveal correlations between health impacts and weather exposures like heat. Participant feedback will offer perspectives on the usability and feasibility of these digital tools. Our study within the SEACO HDSS in Malaysia will evaluate the potential of sensor-based digital technologies in monitoring the interplay between climate change and health, particularly for climate-sensitive diseases like COPD and asthma. The data generated will likely provide details on health profiles in relation to weather exposures. Feedback will indicate the acceptability of these tools for broader health surveillance. As climate change continues to impact global health, evaluating the potential of such digital technologies is crucial to understand its potential to inform policy and intervention strategies in vulnerable regions.

Temporal Variation of Indoor (a residential space) and Outdoor PM2.5 in Male’, Maldives

This study aims to assess the Indoor Air Quality (IAQ) inside a one room apartment of about 240 square feet in the Henveiru district in Male’, Maldives. Concentrations of Particulate Matter (PM2.5), one of the most detrimental air pollutants, were measured every 15 minutes from 10-09-2021 to 18-12-2021 using low-cost PM BlueSky sensors inside the building (indoor) and simultaneously outdoor, about 300m away from the indoor sensor. The BlueSky monitors were co-located and harmonized before starting the monitoring. The mean concentrations observed indoor and outdoor for 93 days were 10.1±13.1 µg/m3 and 6.5±4.9 µg/m3, respectively. Both results surpass the annual average of 5 µg/m3 guideline of World Health Organization (WHO). The number of days that surpassed the 24-hour WHO limit was 9 for indoor and 7 for outdoors. The diurnal pattern of PM2.5 shows a drastic accumulation of the pollutant inside the building when the only available ventilation (the window) was closed, which was identified at night and also during the daytime. The recorded PM2.5 levels also aligned with household activities like cooking, cleaning, and dusting. There was a significant moderate correlation (r= 0.48) between outdoor and indoor PM2.5. The I/O ratio was 1.95±3.01 indicating the presence of more indoor sources. This study serves as a preliminary assessment of indoor air quality in Male’, Maldives. Based on its findings, efforts should be directed towards ensuring proper ventilation in residential spaces and continuously monitoring air quality in more location for longer periods to ensure healthy IAQ.

A Mobile Laboratory Robot for Various and Precise Measurements of Crops and Soil in Agricultural Fields: Development and Pilot Study

Localized management based on multipoint measurements of crops and soil is expected to improve agricultural productivity. The difficulties with this process are as follows: it is time-consuming due to the many measurement points; it requires various sensors for various measurements; it can lead to unstable measurements due to sunlight and wind. To solve the above issues, the system we propose has the advantages of efficient measurements performed by a robot, various measurements performed through exchangeable sensor units, and stable measurements through protecting the crop from sunlight and wind. As a pilot study for the system, we conducted an experiment to simultaneously measure the crops and soil in a cabbage field. The robot achieved mobility in the field, &gt;4 h of operation time, and the ability to obtain soil electrical conductivity measurements and crop imaging at 100 points. Furthermore, the stability of the measurement conditions within the sensor unit during the experiment was evaluated. Compared to the case without the covering, the illuminance became 280-fold stabler (standard deviation = 0.4 lx), and the wind-induced crop shaking became 20-fold lower (root mean square error of the image pixels = 0.5%). The potential impacts of this research include high reproducibility because of the shareable sensor unit and the expectation of new discoveries using precise indoor sensors.

Consensus-based clustering for indoor sensor deployment and indoor condition monitoring

Accurate indoor climate monitoring is important for identifying energy-efficiency strategies in buildings. However, existing indoor monitoring approaches typically use networks of fixed/rigid sensor nodes that are often deployed in an ad-hoc manner, which could be limited in covering the areas/zones of interest or in accounting for the profile variations of the physical parameters over time. To address this gap, this paper proposes a consensus-based clustering method for identifying more robust sensor deployment strategies. The proposed method clusters a co-association matrix of input partitions that capture the periodic variations of the physical parameters from multiple building locations to increase the robustness of the sensing strategy to the daily and hourly changes in the indoor climate conditions. To test the proposed approach, thirty sensing units that sense four physical parameters were deployed in three rooms. Two consensus-clustering-based approaches (hourly- and daily-consensus) and four different frequencies for profile generation were tested, resulting in 48 scenarios for evaluation. The experimental results showed that the proposed approach could be more robust to changes in indoor climate conditions compared to the baseline, achieving a higher strategy performance index by up to 59%, and that the daily-consensus approach is often more robust than the hourly approach. The results also showed that the time frequency affects the strategy robustness and that parameters whose profiles have seasonal components are more likely to outperform. The proposed method could be used to determine reliable indoor sensor locations for capturing the dynamic environmental state of buildings, towards efficient building operation and management.

The usefulness of sensor fusion for unmanned aerial vehicle indoor positioning

PurposeIn this study, an indoor sensor information fusion positioning system of the quadrotor unmanned aerial vehicle (UAV) was investigated to solve the problem of unstable indoor flight positioning.Design/methodology/approachThe presented system was built on Light Detection and Ranging (LiDAR), Inertial Measurement Unit (IMU) and LiDAR-Lite devices. Based on this, one can obtain the aircraft's current attitude and the position vector relative to the target and control the attitudes and positions of the UAV to reach the specified target positions. While building a UAV positioning model relative to the target for indoor positioning scenarios under limited Global Navigation Satellite Systems (GNSS), the system detects the environment through the NVIDIA Jetson TX2 (Transmit Data) peripheral sensor, obtains the current attitude and the position vector of the UAV, packs the data in the format and delivers it to the flight controller. Then the flight controller controls the UAV by calculating the posture to reach the specified target position.FindingsThe authors used two systems in the experiment. The first is the proposed UAV, and the other is the Vicon system, our reference system for comparison purposes. Vicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.Originality/valueVicon positioning error can be considered lower than 2 mm from low to high-speed experiments. After comparison, experimental results demonstrated that the system could fully meet the requirements (less than 50 mm) in real-time positioning of the indoor quadrotor UAV flight. It verifies the accuracy and robustness of the proposed method compared with that of Vicon and achieves the aim of a stable indoor flight preliminarily.

Calculation of Irradiance from Illuminance for Artificial Light Photovoltaics Applications

Many indoor sensors are powered by artificial-light-harvesting photovoltaic (PV) cells. The performance evaluation and application of PV devices require irradiance measurement to determine input power. Standard solar irradiance meters provide measurements calibrated to sunlight spectra and do not have the low-light-level capability needed for indoor applications. Light meters, specifically designed for human visible artificial light applications, measure illuminance. This article describes a low-cost method to calculate irradiance from illuminance measurement of artificial light sources. An application example is provided.

Predicting Ventilation Rate in a Naturally Ventilated Dairy Barn in Wind-Forced Conditions Using Machine Learning Techniques

Precise ventilation rate estimation of a naturally ventilated livestock building can benefit the control of the indoor environment. Machine learning has become a useful technique in many research fields and might be applied to ventilation rate prediction. This paper developed a machine-learning model for ventilation rate prediction from batch computational fluid dynamics (CFD) simulation results. By comparing deep neural networks (DNN), support vector regression (SVR), and random forest (RF), the best machine learning algorithm was selected. By comparing the modeling scheme of direct single-output (ventilation rate) and indirect multiple-output (predict averaged air velocities normal to the openings, then calculate the ventilation rate), the performances of the machine learning models widely applied in ventilation rate prediction were evaluated. In addition, this paper further evaluated the impact of adding indoor air velocity measurement in ventilation rate prediction. The results showed that the modeling performance of the DNN algorithm (Mean Absolute Percentage Error (MAPE) = 20.1%) was better than those of the SVR (MAPE = 23.2%) and RF algorithm (MAPE = 21.0%). The scheme of multiple-output performed better (MAPE &lt; 8%) than the single-output scheme (MAPE = 20.1%), where MAPE was the mean absolute percentage error. Additionally, the comparison of modeling schemes with different inputs showed that the predictive accuracy could be improved by adding indoor velocities to the inputs. The MAPE decreased from 7.7% in the scheme without indoor velocity to 4.4% in the scheme with one indoor velocity, and 3.1% in the scheme with two indoor velocities. The location of the additional air velocity affected the accuracy of the predictive model, with the ones at the bottom layer performing better in the prediction than those at the top layer. This study enables a real-time and accurate prediction of the ventilation rate of a barn and provides a recommendation for optimal indoor sensor placement.

Predicting indoor air temperature and thermal comfort in occupational settings using weather forecasts, indoor sensors, and artificial neural networks

We present an approach to generate location-specific forecasts of indoor temperature (Ti) and thermal comfort and issue indoor heat warnings for occupational settings. Indoor forecasts are generated using standard outdoor weather forecasting products and an artificial neural network (ANN) trained on-site using local indoor measurements from a low-cost sensor system measuring Ti and indoor physiologically equivalent temperature (PETi). The outcomes are hourly indoor Ti and PETi forecasts. Different ANN-based forecast products using different predictors were concurrently tested at 121 workplaces in agricultural, industrial, storage, and office buildings using data for an entire annual cycle. A forecast was considered skillful when the Ti and PETi forecast was <2 K from actual measurements. The best-performing model used the predictors time of year, week, and day; solar position; and outdoor weather forecast variables to train and run an ANN to predict Ti or PETi. It had an annual average mean absolute forecast error of 0.87 K for Ti and 0.99 K for PETi over the next 24 h, with Pearson correlation coefficients of 0.98 and 0.97, respectively. Overall, 91% of Ti forecasts and 88% of PETi forecasts were skillful. Indoor forecasts showed larger errors in the summer than in the winter. We conclude that combining indoor data with weather forecasts using ANNs could be implemented widely to provide location-specific indoor weather forecasts to improve and localize heat and health warning systems.

An Advanced Artificial Fish School Algorithm to Update Decision Tree for NLOS Acoustic Localization Signal Identification with the Dual-Receiving Method

For indoor sensor systems, it is essential to implement an extra supporting area notification part. To inform the real-time coordinates, the time difference of arrival (TDOA) algorithm can be introduced. For these indoor localization systems, their main processes are often built based on the line of sight (LOS) scenario. However, obstacles make the off-the-shelf localization system unable to play its due role in the flexible non-line of sight (NLOS) scenario. So, it is necessary to adjust the signals according to the NLOS identification results. However, the NLOS identification methods before were not effective enough. To address these challenges, on the one hand, this paper proposes an adaptive strategy for a dual-receiving signal processing method. On the other hand, the system is matched with the homologous NLOS identification method based on a novel artificial fish school algorithm (AFSA) and the decision tree model. According to our experiments, our novel AFSA optimization method can obtain a better effect and take less time. The NLOS acoustic signal identification accuracy can be improved significantly in flexible scenarios compared with other methods. Based on these processes, the system will achieve more accurate localization results in flexible NLOS situations.

Using Sensor Data to Identify Factors Affecting Internal Air Quality within 279 Lower Income Households in Cornwall, South West of England.

(1) Background: Poor air quality affects health and causes premature death and disease. Outdoor air quality has received significant attention, but there has been less focus on indoor air quality and what drives levels of diverse pollutants in the home, such as particulate matter, and the impact this has on health; (2) Methods: This study conducts analysis of cross-sectional data from the Smartline project. Analyses of data from 279 social housing properties with indoor sensor data were used to assess multiple factors that could impact levels of particulate matter. T-Tests and Anova tests were used to explore associations between elevated PM2.5 and building, household and smoking and vaping characteristics. Binary logistic regression was used to test the association between elevated particulate matter and self-reported health; (3) Results: Of the multiple potential drivers of the particulate matter investigated, smoking and vaping were significantly associated with mean PM2.5. Following multivariate analysis, only smoking remained significantly associated with higher mean concentrations. Properties in which <15 cigarettes/day were smoked were predicted to have PM2.5 concentrations 9.06 µg/m3 higher (95% CI 6.4, 12.82, p ≤ 0.001) than those in which residents were non-smokers and 11.82 µg/m3 higher (95% CI 7.67, 18.19, p ≤ 0.001) where >15 cigarettes were smoked; (4) Conclusions: A total of 25% of social housing properties in this study experienced levels of indoor PM greater than WHO guideline levels for ambient air pollution. Although there are many factors that impact air quality, in this study the main driver was smoking. This highlights the importance of targeting smoking in indoor environments in future smoking cessation and control policy and practice and of understanding how pollutants interact in the home environment. There is also a need for further research into the impact on indoor air quality of vaping, particularly due to the rise in use and uncertainty of its long-term impact.

Short Term Prediction Model of Environmental Parameters in Typical Solar Greenhouse Based on Deep Learning Neural Network

The type of single-slope solar greenhouse is mainly used for vegetable production in China. The coupling of heat storage and release courses and the dynamic change in the outdoor weather parameters momentarily affect the indoor environment. Due to the high cost of small weather stations, the environmental parameters monitored by the nearest meteorological stations are usually used as outdoor environmental parameters in China. In order to accurately predict the solar greenhouse and crop water demand, this paper proposes three deep learning models, including neural network regression (DNNR), long short-term memory (LSTM), and convolutional neural network-long- short-term memory (CNN-LSTM), and the hyperparameters of three models were determined by orthogonal experimental design (OD). The temperature and relative humidity monitored by the indoor sensors and outdoor weather station were taken as the inputs of models, the temperature and relative humidity 3, 6, 12 and 24 h in advance were taken as the output, 16 combinations of input and output data of two typical solar greenhouses were trained separately by three deep learning models, those models were trained 144, 144 and 288 times, respectively. The best model of three type models at four prediction time points were selected, respectively. For the forecast time point of 12 h in advance, the errors of the best LSTM and CNN-LSTM models in two greenhouses were all smaller than the DNNR models. For the three other time points, the results show that the DNNR models have excellent prediction accuracy among the three models. The maximum and minimum temperature, relative humidity, and ETo were also accurately predicted using the corresponding optimized models. In sum, this study provided an optimized deep learning prediction model for environmental parameters of greenhouse and provides technical support for irrigation decision-making and water allocation.

A Joint Design of Radar Sensing, Wireless Power Transfer, and Communication Based on Reconfigurable Software Defined Radio

This paper proposes a compact three-mode base station capable of performing radar sensing, communication, and wireless power transfer (WPT) in collaboration with indoor sensor networks. With regard to the wireless sensor node, the base station transmits two-tone signals in the downlink to support its operation and provides two-way communication. The sensor node sends uplink information through backscattering using the third order intermodulation (IM3) product of the rectification. In the radar mode, a single-tone continuous wave (CW) is used to monitor if there is a moving target in the static environment. If a speed is detected, the transmit signal to the node is stopped, while the single-tone CW excitation will continue until the speed of the target is zero, and then the base station transmits a stepped frequency continuous wave (SFCW) signal to measure the distance of the target. The repeat between the two radar waveforms continues until the target is undetectable within the detection range. The software defined radio PlutoSDR is adopted as the base station. The system can wirelessly supply power and bi-directionally communicate with a CO2 sensor node 2 m away. It gives a range resolution of 2.5 cm and a minimum detectable speed of 0.25 m/s in the radar mode.

A spatial data model for accessibility with indoor temporal changes

AbstractIndoor accessibility is often evaluated with the networks extracted from buildings. For instance, circulation networks with depth cost are applied to floor design. A group of data models supports the extraction of indoor networks from buildings, and these networks are widely used to evaluate the accessibility of users. However, the existing cases are mostly devised for static environments, that is, without consideration of dynamic obstacles. This assumption may contradict the fact of daily changes such as pedestrian flow and temporal blockage. Except emergency scenarios, these daily temporal changes can interrupt the continuous movement of a user, and they shall be considered for data modeling of indoor networks. Thus, this article proposes a concise data model which can manage daily temporal changes of accessibility and support related accessibility queries. In the data model, we consider indoor accessibility on two levels of detail, that is, the room and sub‐room levels. On the room level, we revise a gravity model to measure the accessibility variation of rooms influenced by temporal obstacles. On the sub‐room level, we continuously update obstacle‐avoiding networks adapted to temporal obstacles. In this case, the accessibility of a user at any location can be assessed and time‐dependent accurate routes can be planned at different moments. We implemented this spatial data model in the DBMS PostgreSQL and its feasibility was demonstrated with the test in a campus building. The data model can be applied to daily checks of user accessibility and can be further incorporated with indoor sensor systems and simulation models to predict accessibility variation.

FusIon Data Tracking System (FITS)

The field of generating movement profiles of individuals is valuable in many real-world applications (e.g., controlling disease spread or evaluating marketing engagement). Existing solutions often rely on global positioning systems (GPS) or similar systems, primarily targeted at outdoor use cases. However, the indoor tracking capabilities of current solutions either lack precision or are available in closed buildings only. The literature proposes sensor fusion approaches, but many of those are based on specific sensors. These approaches do not reveal implementation details or data to allow for their independent evaluation. Therefore, this article presents FusIon Data Tracking System ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> ) as an approach and proof-of-concept to facilitate the correlation of data from different indoor sensors to movement profiles of different individuals. Functionally, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> does this by generating synthetic sensor measurement data based on real-world movement data and correlating objects tracked from distinct sensors by effectively solving clustering and position prediction tasks. This correlation is evaluated based on different metrics [multiple object tracker accuracy/precision (MOTA/MOTP)] in four different scenarios, for example, sparse data, high density of sensors, low density of sensors, and a base case. Finally, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FITS</monospace> ’s performance was evaluated by increasing the load test (dataset up to 100000 measurements and 1000 visitors) to assess whether near real-time processing is feasible under a high workload.

Dense Indoor Sensor Networks: Towards passively sensing human presence with LoRaWAN

Sensors have become ubiquitous in buildings but are rarely connected to a network, and their potential to analyse the performance, use, and interaction with a building is not yet fully realised. In the coming years, we expect sensors in buildings to become part of the Internet of Things (IoT) and grow in numbers to form a Dense Indoor Sensor Network (DISN) that allows for unprecedented analysis of the performance, use, and interaction with buildings. Multiple technologies vie for leading this transformation. We explore Long Range Wide Area Network (LoRaWAN) as an alternative for creating indoor sensor networks that extends beyond its original long-distance communication purpose. For the present paper, we developed a DISN with 390 sensor nodes and four gateways and empirically evaluated its performance for two years. Our analysis of more than 86 million transmissions revealed that DISNs achieve a much lower distance coverage compared to estimations from previous research indicating that more gateways are required. In addition, the deployment of multiple gateways decreased the loss of transmissions due to environmental and network factors. Given the complexity of our system, we received few colliding concurrent messages, which demonstrates a gap between the projected requirements of LoRaWAN systems and the actual requirements of real-world applications given sufficient gateways. We also contribute to the modelling of transmissions with our comparison of attenuation models derived from multiple methodologies. Across all models, we find that robust coverage in an indoor environment can be maintained by placing a gateway every 30 m and every 5 floors. Finally, we also investigate the application of DISNs for the passive sensing and visualisation of human presence using a Digital Twin (DT) and a Fused Twins (FT) representation in Augmented Reality (AR). A passive sensing approach allows us to gather relevant data on human use of a building while still preserving privacy via the aggregation process. Immersive in situ visualisations in FT allow for new interactions and new forms of participation. We conclude that DISNs are already technologically feasible today and basing them on Low Power Wide Area Network (LPWAN) offers intriguing possibilities to reduce energy consumption, maintenance cost, and bandwidth use while also enabling new forms of human-building interaction.

CNN for Elderly Wandering Prediction in Indoor Scenarios.

This work proposes a way to detect the wandering movement of Alzheimer’s patients from path data collected from non-intrusive indoor sensors around the house. Due to the lack of adequate data, we have manually generated a dataset of 220 paths using our developed application. Wandering patterns in the literature are normally identified by visual features (such as loops or random movement), thus our dataset was transformed into images and augmented. Convolutional layers were used on the neural network model since they tend to have good results in finding patterns mainly on images. The Convolutional Neural Network model was trained with the generated data representing the hourly analysis and achieved an F1 score (relation between precision and recall) of 75%, recall of 60%, and precision of 100% on the validation slice. For comparative purposes, we have also trained the model with a 30-min interval of analysis and achieved an F1 score of 57.14%, a recall of 80% and a precision of 44.44%.

A Design and Implementation of the system framework for Monitoring Indoor Sensor Data based Augmented Reality

Abstract— Large and complex buildings are increasing, and many objects exist in these buildings. Objects generate events through interaction with each other. There is an augmented reality among the technologies for recognizing the surrounding environment, and through augmented reality, data generated in the surrounding environment can be grasped to understand the situation. Among the events that occur indoors are indoor disasters, which can cause damage to people and property. Therefore, it is necessary to collect various indoor sensor data to identify indoor disaster events. At this time, the collected indoor sensor data should confirm and understand information. In the case of an indoor sensor, there are signal data for determining a location or a measurement sensor for understanding an indoor environment. Instant feedback may be possible if such indoor sensor data can be checked through a human visual action rather than devices. Accordingly, if augmented reality technology is used, information can be obtained more easily, and data readability becomes convenient. First of all, the augmented reality-based indoor sensor data monitoring system framework was designed by applying coordinate data of inertial sensors to identify a human’s indoor location so that sensor data can be monitored, and a prototype system was implemented through this. This system can be used as a system to monitor data from firefighters or control centers in the event of an indoor disaster.