Real Indoor Environment Research Articles

Enabling Noninvasive Physical Assault Monitoring in Smart School with Commercial Wi-Fi Devices

Monitoring physical assault is critical for the prevention of juvenile delinquency and promotion of school harmony. A large portion of assault events, particularly school violence among teenagers, usually happen at indoor secluded places. Pioneering approaches employ always-on-body sensors or cameras in the limited surveillance area, which are privacy-invasive and cannot provide ubiquitous assault monitoring. In this paper, we present Wi-Dog, a noninvasive physical assault monitoring scheme that enables privacy-preserving monitoring in ubiquitous circumstances. Wi-Dog is based on widely deployed commodity Wi-Fi infrastructures. The key intuition is that Wi-Fi signals are easily distorted by human motions, and motion-induced signals could convey informative characteristics, such as intensity, regularity, and continuity. Specifically, to explicitly reveal the substantive properties of physical assault, we innovatively propose a set of signal processing methods for informative components extraction by selecting sensitive antenna pairs and subcarriers. Then a novel signal-complexity-based segmentation method is developed as a location-independent indicator to monitor targeted movement transitions. Finally, holistic analysis is employed based on domain knowledge, and we distinguish the violence process from both local and global perspective using time-frequency features. We implement Wi-Dog on commercial Wi-Fi devices and evaluate it in real indoor environments. Experimental results demonstrate the effectiveness of Wi-Dog which consistently outperforms the advanced abnormal detection methods with a higher true detection rate of 94% and a lower false alarm rate of 8%.

Optimal Structure Synthesis for Environment Augmenting Robots

Building structures can allow a robot to surmount large obstacles, expanding the set of areas it can reach. This letter presents a planning algorithm to automatically determine what structures a construction-capable robot must build in order to traverse its entire environment. Given an environment, a set of building blocks, and a robot capable of building structures, we seek an optimal set of structures (using a minimum number of building blocks) that could be built to make the entire environment traversable with respect to the robot's movement capabilities. We show that this problem is NP-Hard, and present a complete, optimal algorithm that solves it using a branch-and-bound strategy. The algorithm runs in exponential time in the worst case, but solves typical problems with practical speed. In hardware experiments, we show that the algorithm solves three-dimensional maps of real indoor environments in about 1 min, and that the structures selected by the algorithm allow a robot to traverse the entire environment. An accompanying video is available online at https://youtu.be/B9WM557NP44 .

Investigation of the physiological differences in the immersive virtual reality environment and real indoor environment: Focused on skin temperature and thermal sensation

The goal of this research is to investigate and determine whether the effect of an IVE condition on an occupant's environmental sensations and physiological responses is different from the effect of a real environmental condition in the indoor environment. The research included a series of human subject experiments, with 16 participants in an environmental chamber. A thermal quality condition was selected as a primary environmental parameter, based on current IEQ-relevant studies. While the ambient thermal condition was gradually changed from 20 °C to 30 °C, the participants were asked to report their overall thermal sensations. Their skin temperatures were also continuously measured to collect physiological signal information in real time. The results of this experimental study revealed that the participants mostly generated higher skin temperature at the selected seven skin areas. Their reported thermal sensations were significantly higher in the IVE condition, than in the real environment, showing a difference of 12%.

The Spatial and Temporal Variability of the Indoor Environmental Quality during Three Simulated Office Studies at a Living Lab

The living lab approach to building science research provides the ability to accurately monitor occupants and their environment and use the resulting data to evaluate the impact that various components of the built environment have on human comfort, health, and well-being. A hypothesized benefit of the living lab approach is the ability to simulate the real indoor environment in an experimentally controlled setting over relatively long periods of time, overcoming a significant hurdle encountered in many chamber-type experimental designs that rarely reflect typical indoor environments. Here, we present indoor environmental quality measurements from a network of sensors as well as building system design and operational data demonstrating the ability of a living lab to realistically simulate a wide range of environmental conditions in an office setting by varying air temperature, lighting, façade control, and sound masking in a series of three human subject experiments. The temporal variability of thermal and lighting conditions was assessed on an hourly basis and demonstrated the significant impact of façade design and control on desk-level measurements of both factors. Additional factors, such as desk layout and building system design (e.g., luminaires, speaker system), also contributed significantly to spatial variability in air temperature, lighting, and sound masking exposures, and this variability was reduced in latter experiments by optimizing desk layout and building system design. While ecologically valid experimental conditions are possible with a living lab, a compromise between realism and consistency in participant experience must often be found by, for example, using an atypical desk layout to reduce spatial variability in natural light exposure. Based on the experiences from these three studies, experimental design and environmental monitoring considerations for future office-based living lab experiments are explored.

Multivariate Optimization of Tenax TA-Thermal Extraction for Determining Gaseous Phase Organophosphate Esters in Air Samples

Suitable conditions for thermal extraction of semi-volatile organic compounds have largely been arrived at by univariate optimization or based on the recommendations provided by the manufacturers of the extraction equipment. Herein, we demonstrated the multivariate optimization of Tenax TA–thermal extraction for determining organophosphate esters in the gas phase fraction of air samples. Screening and refining experiments were performed using the eighth fraction factorial and Box-Behnken designs, respectively, and satisfactory models were obtained. Subsequently, the process was optimized by Derringer’s desirability function and the global desirability was 0.7299. Following optimization, the analytes were desorbed at 290 °C for 10 minutes at a helium flow of 95 mL min−1, with the transfer line set at 290 °C. The analytes were then cryofocused at 20 °C and then cryodesorbed into the chromatographic column at 295 °C for 6 minutes. Method validation exhibited high linearity coefficients (>0.99), good precision (CV < 14%) and low detection limits (0.1–0.5 ng m−3). The method was tested by pumping 0.024 m3 of real indoor environment air through Tenax TA sorbent tubes. Furthermore, with multivariate optimization, analysis time and other resources were significantly reduced, and information about experimental factor interaction effects was investigated, as compared to the univariate optimization and other traditional methods.

Using an IoT Platform for Trustworthy D2D Communications in a Real Indoor Environment

The constantly increasing need for data exchange among various types of devices, mobile and fixed, is one of the main characteristics of technological developments in the last few years. Within this context, the possibility to deliver content to more devices into the same domestic environment is very interesting for both consumers and service providers. The main hurdle for device to device (D2D) communications is the available bandwidth and, implicitly, the used radio technology and frequency range. From this point of view, so called TV white spaces (TVWSs) are an ideal candidate for short range communications, but have the problem of interference management with the licensed services already operating there. This problem can be alleviated by using cooperative, distributed spectrum sensing techniques. This paper proposes an innovative approach for D2D communications in a real indoor environment, based on a social Internet of Things (SIoT) architecture able to involve all participating objects in a twofold procedure, gathering both spectrum sensing and quality of service data, and weighting the received information using a novel trustworthiness algorithm. The algorithm, together with the entire SIoT architecture, has been implemented and extensively tested in a real indoor environment.

Enhanced Device-Free Human Detection: Efficient Learning From Phase and Amplitude of Channel State Information

With the rapidly increasing demand for security and E-health applications, device-free human detection has attracted interest because it does not require a wearable device or camera setup. This paper proposes a deep-learning-based approach that monitors wireless signals to learn three human modes, i.e., absence, working, and sleeping, in realistic indoor environments. This paper integrates the amplitude and phase of channel state information to propose a hybrid complex feature; this facilitates robust and efficient human detection even with fewer data samples. Experiments conducted in two unmodified WiFi networks demonstrate the effectiveness of the proposed algorithms. Four machine-learning algorithms provide satisfactory performance with sufficient data, and deep neural networks perform the best. Results show that by using 6% training samples, the proposed hybrid feature still achieves 93% accuracy and can even outperform three typical machine learning algorithms that use full training samples. Moreover, the proposed feature significantly improves detection accuracy by 11.62%–27.76% than traditional amplitude feature with fewer training samples.

Artificial Neural Network for LiDAL Systems

In this paper, we introduce an intelligent light detection and localization (LiDAL) system that uses artificial neural networks (ANN). The LiDAL systems of interest are MIMO LiDAL and MISO IMG LiDAL systems. A trained ANN with the LiDAL system of interest is used to distinguish a human (target) from the background obstacles (furniture) in a realistic indoor environment. In the LiDAL systems, the received reflected signals in the time domain have different patterns corresponding to the number of targets and their locations in an indoor environment. The indoor environment with background obstacles (furniture) appears as a set of patterns in the time domain when the transmitted optical signals are reflected from objects in LiDAL systems. Hence, a trained neural network that has the ability to classify and recognize the received signal patterns can distinguish the targets from the background obstacles in a realistic environment, especially given the mobility of targets (humans) which distinguishes them from static obstacles (furniture). The LiDAL systems with ANN are evaluated in a realistic indoor environment through computer simulation.

LiDAL: Light Detection and Localization

In this paper, we present the first indoor light-based detection and localization system that builds on concepts from radio detection and ranging (radar) making use of the expected growth in the use and adoption of visible light communication (VLC), which can provide the infrastructure for our Light Detection and Localization (LiDAL) system. Our system enables active detection, counting, and localization of people, in addition to being fully compatible with the existing VLC systems. In order to detect human (targets), LiDAL uses the visible light spectrum. It sends pulses using a VLC transmitter and analyses the reflected signal collected by a photodetector receiver. Although we examine the use of the visible spectrum here, LiDAL can be used in the infrared spectrum and other parts of the light spectrum. We introduce LiDAL with different transmitter-receiver configurations and optimum and sub-optimum detectors considering the fluctuation of the received reflected signal from the target in the presence of Gaussian noise. We design an efficient multiple input multiple output (MIMO) LiDAL system with a wide field of view (FOV) single photodetector receiver, and also design a multiple input single output (MISO) LiDAL system with an imaging receiver to eliminate the ambiguity in target detection and localization. We develop models for the human body and its reflections and consider the impact of the color and texture of the cloth used as well as the impact of target mobility. A number of detection and localization methods are developed for our LiDAL system, including cross correlation and a background subtraction method. These methods are considered to distinguish a mobile target from the ambient reflections due to background obstacles (furniture) in a realistic indoor environment.

A novel radio map construction method with reduced human efforts for Wi-Fi localisation system

Fingerprint based Wi-Fi localisation system often takes a lot of human efforts to measure the received signal strength (RSS) of dense grid in an indoor environment. In this paper, we propose a novel fingerprint database construction method with reduced human effort to obtain optimal length of RSS time series and grid division. We verify the chaotic characteristics of RSS time series, and use phase space reconstruction algorithm to calculate the optimal length of RSS data to be collected at each reference point. Then Gaussian process regression (GPR) for fingerprinting based indoor localisation is used to construct the database with information of limited reference points. The hyper-parameters of GPR is calculated by conjugate gradient descent algorithm. The performance of the proposed radio map construction framework is validated in real indoor environment, and with using Bayesian positioning method, the localisation error mean can be 1.5 m while the construction time of radio map is greatly reduced with ensuring accuracy.

In situ investigation of NOᵪ photocatalytic degradation: Case study in an open space office in Manchester, UK

Indoor air is contaminated by numerous pollutants, which impact human health, comfort and productivity. These pollutants have various indoor sources such as building materials, furniture, combustion appliances or tobacco smoke. However, the pollution also comes from outside. In urban area, nitrogen oxides (NOx) emitted into the atmosphere can reach alarming levels. These traffic-related pollutants, which seriously impact the global environment and human health, can infiltrate inside buildings. Therefore, limiting the amount of breathable NOx in outdoor and indoor environments is an important priority for the modern society. The photocatalytic process has attracted particular attention in the last two decades and has proved to be efficient to reduce the concentration of NOx. However, further work has to be conducted to assess its efficiency in real indoor environments. The purpose of this paper was to report on the indoor air quality in an open space office in Manchester, UK. Focus was made on nitric oxide (NO) and nitrogen dioxide (NO2). The indoor concentrations of both gases were monitored from 14 January 2019 to 7 April 2019. During this period, a photocatalytic coating was applied to a part of the indoor wall. The influence of this coating on the level of NOx was assessed by comparing the indoor concentrations before and after the application. An attention was paid to the correlation between outdoor and indoor pollution and to the effect of other parameters such as temperature, humidity, pressure and O3 concentration. The results showed that the photocatalytic process led to a decrease in the NOx concentration. The likelihood to find concentrations above 35 ppb for NO and 7.5 ppb for NO2 was clearly reduced after the coating application.

Gender and Age Differences in Using Indoor Maps for Wayfinding in Real Environments

Users more easily become lost in complex indoor environments than in outdoor environments. Users with diverse backgrounds encounter different self-location, route memorization, and route following problems during wayfinding. This study intends to explore gender and age effects on the use of indoor maps for wayfinding in real environments. We used eye-tracking and retrospective verbal protocol methods to conduct a wayfinding experiment in a newly opened building. Statistical data were collected and three findings were obtained. Finding 1: Males had no significant differences with females in indoor self-location, route reading, and route following. However, males paid less visual attention to the landmark and legend than females during route reading. Finding 2: Age-related differences were significant in indoor wayfinding. Younger adults generally outperformed elderly adults in wayfinding in real indoor environments. Finding 3: Gender and age interactive effects were significant in self-location and route memorization. The mean differences of visual attention on the self-location map reading and route memorization between males and females increased with age.

Indoor Localization Using Multi-operator Public Land Mobile Networks and Support Vector Machine Learning Algorithms

Given Public Land Mobile Networks (PLMN) ubiquitous infrastructure and excellent coverage, they can be used for providing location information for various location-based services, especially in indoor environments. This paper investigates indoor positioning solutions which utilize received signal strength measurements obtained by mobile device from PLMN cells belonging to multiple mobile network operators. Two indoor positioning methods, based on Support Vector Machine learning algorithms and space-partitioning principle, are proposed. The first method utilizes Support Vector Regression (SVR) algorithm, whilst the second one introduces space-partitioning principle and the combined use of Support Vector Classification and SVR algorithms. The proposed techniques are thoroughly investigated in a real indoor environment. In addition, several criteria for choosing relevant cells for the positioning purposes have been explored. Positioning with SVR has demonstrated good results, while utilizing space-partitioning principle has further reduced the average positioning error by 27%. Moreover, the proposed solution has outperformed positioning methods based on k Nearest Neighbours and Artificial Neural Networks, when implemented in the same verification test bed.

Performance Enhancement of Multi-Antenna Relay Link in LTE Het-Net

In this paper, the relay link performance for an Amplify and Forward (AF) multi-antenna relay based heterogeneous LTE (Long Term Evolution) network is enhanced with two novel techniques, the Golden codes and Error Vector Magnitude (EVM) based Constellation Combiner (ECC). The performance evaluation of the proposed co-operative LTE Het-Net is done in a real time indoor environment using Wireless open Access Research Platform (WARP). The proposed techniques for the LTE Het-Net are compared with traditional Alamouti Scheme with Maximal Ratio Combing (MRC). From the obtained results it is seen that the LTE network with proposed techniques gives a 12% improvement in EVM than the conventional LTE network.

Incorporating wheelchair users in people detection

A wheelchair users detector is presented to extend people detection, providing a more general solution to detect people in environments such as houses adapted for independent and assisted living, hospitals, healthcare centers and senior residences. A wheelchair user model is incorporated in a detector whose detections are afterwards combined with the ones obtained using traditional people detectors (we define these as standing people detectors). We have trained a model for classical (DPM) and two for modern (Faster-RCNN and YOLOv3) detection algorithms, to compare their performance. Besides the extensibility proposed with respect to people detection, a dataset of video sequences has been recorded in a real in-door senior residence environment containing wheelchairs users and standing people and it has been released together with the associated ground-truth.

Multi-Target Intense Human Motion Analysis and Detection Using Channel State Information

Intense human motion, such as hitting, kicking, and falling, in some particular scenes indicates the occurrence of abnormal events like violence and school bullying. Camera-based human motion detection is an effective way to analyze human behavior and detect intense human motion. However, even if the camera is properly deployed, it will still generate blind spots. Moreover, camera-based methods cannot be used in places such as restrooms and dressing rooms due to privacy issues. In this paper, we propose a multi-target intense human motion detection scheme using commercial Wi-Fi infrastructures. Compared with human daily activities, intense human motion usually has the characteristics of intensity, rapid change, irregularity, large amplitude, and continuity. We studied the changing pattern of Channel State Information (CSI) influenced by intense human motion, and extracted features in the pattern by conducting a large number of experiments. Considering occlusion exists in some complex scenarios, we distinguished the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions in the case of obstacles appearing between the transmitter and the receiver, which further improves the overall performance. We implemented the intense human motion detection system using single commercial Wi-Fi devices, and evaluated it in real indoor environments. The experimental results show that our system can achieve intense human motion detection rate of 90%.

A 3D Rotating Laser-Based Navigation Solution for Micro Aerial Vehicles in Dynamic Environments

In this paper, we present a 3D rotating laser-based navigation framework for micro aerial vehicles (MAVs) to fly autonomously in dynamic environments. It consists of a 6-degree of freedom (DoF) localization module and a 3D dynamic mapping module. A self-designed rotating laser scanner generates dense point clouds in which 3D features are extracted and aligned. The localization module is able to solve scan distortion issue while estimating the 6-DoF pose of MAVs. At the same time, the dynamic mapping module can further eliminate dynamic trails so that a clear dense 3D map is reconstructed. The dynamic targets are detected based on the spatial constraints and therefore without the need of dense point cloud clustering. Through filtering the detected dynamic obstacles, the localization approach can be robust to the dynamic environment variations. To verify the robustness and effectiveness of our proposed framework, we have tested our system in both real indoor environment with dynamic obstacles and outdoor foliage condition using a customized MAV platform.

Stochastic Dosimetry Based on Low Rank Tensor Approximations for the Assessment of Children Exposure to WLAN Source

In this study, the exposure of a female eight-year old child to a WLAN access point located in an unknown position in a realistic indoor environment was investigated. A stochastic dosimetry approach based on low rank tensor approximation was used to develop surrogate models to quickly estimate the specific absorption rate in all the possible positions of the WLAN source and the child. Results showed that, for all the possible positions in the room, the exposure values were significantly below the limits of the International Commission of Non-Ionizing Radiation Protection guidelines for general public exposure. The variation of the positions of the source and the child greatly influenced the exposure, resulting in quartile coefficient of dispersion values always higher than 65%. The WLAN source positions, among all the possible ones, that minimize the exposure of the child staying in the rectangular room were found to be on the sides of the smaller wall of the room.

Ultrasonic Communication Using Consumer Hardware

We have implemented a near-ultrasonic communication protocol in the 18.5–20 kHz band, which is inaudible to most humans, using commodity smartphone speakers and microphones to transmit and receive signals. The protocol described in this paper is a component of Google's Nearby platform, where near-ultrasound signals are used to establish copresence between nearby devices by transmitting a short token. High-frequency sound does not pass through walls (most energy is reflected), so identified devices are constrained to approximately the same room, “within earshot” of one another. Our protocol has a raw data rate of 94.5 b/s, and we find in real indoor environments that transmission between mobile devices is reliable at 2 m distance and often works at 10 m. We use direct-sequence spread spectrum modulation, which makes it highly robust to multipath, motion, and narrowband noise. We use a 127-chip pseudorandom code, repeating once per data symbol, and modulate its amplitude with orthogonal sine waveforms encoding 4-bit symbol values. We add the orthogonal sines to a constant “pedestal,” which is inefficient in an information-theoretic sense, but makes synchronization easier. We describe a robust and computationally efficient transmitter and receiver implementations and show experiments on real and simulated data.

A novel mobile robot localization approach based on classification with rejection option using computer vision

In this paper, we propose a novel approach for mobile robot localization from images. The proposal is based on supervised learning using topological representations for the environment. The whole system comprises feature extraction and classification methods. With respect to feature extraction, we consider standard methods in digital image processing, e.g. Scale-Invariant Feature Transform and Local Binary Patterns. For classification, we apply machine learning methods with rejection option. A thorough assessment of the proposal is carried out using data from virtual and real indoor environments. Additionally, we compare the proposed architectures with classic localization systems using an omnidirectional camera. Based on the results, Spatial Moments combined with Bayes classifier is the best performing model, providing high accuracy rate (99.94%) and small computational time (47.3μ s and 0.165 s for classification and extraction, respectively). Finally, we observe that localization with rejection option increases efficiency and reliability of navigation in mobile robotics.