Device-free Human Activity Recognition Based on Random Subspace Classifier Ensemble

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

A numerical simulation tool for predicting the impact of outdoor thermal environment on building energy performance

Providing high data rates and wide bandwidth applications are the most important challenges that mobile systems and radio networks are trying to accomplish. Radio over Fiber-RoF systems is a promoting research field since it benefits of Passive Optical Network (PON) advantages and unlicensed large bands in 60 GHz millimeter band (57-64GHz). In this study, we consider a complete RoF system from the central office to the end-user passing by the base station and we study the impact of propagation environment on the whole system performance and radio coverage by considering different scenarios for multipath propagation (Line of Sight-LOS, Non Line of Sight-NLOF). In particular, we have been interested in Rayleigh and Rice propagation models for both indoor and outdoor environments. Optisystem/ADS Co-simulation has been investigated to simulate RoF network coverage for indoor and outdoor environments while considering high data rates.

Language acquisition by children depends on the learning environment when they were in kindergarten to some extent. The goals of this study were to find out the impact of the indoor and outdoor environment on children's language development. Structural equation modeling was adopted. Children randomly selected from 270 preschool classes of five provinces in China were recruited in this study (1642 children aged between 36 and 72 months). We concluded that language achievement of children cannot be directly predicted by indoor and outdoor facilities and play materials, and an intermediate transmission pathway was needed. As an intermediary variable, the curriculum implementation partially mediated the influence of the indoor learning center on children's language achievement. The psychological atmosphere and curriculum implementation have a chain mediation effect between indoor and outdoor facilities and play material quality and childhood language achievement, and the influence effect of indoors was different from that of outdoors. In terms of the influence of indoor learning center planning and play materials, the effect on children's language achievement accounted for 61.5%, while when it came to that of outdoor learning center facilities and equipment, it was 60.7%. This indicated that the impact of the indoor and outdoor environment on children's language development could not be ignored. The indoor environment was more dependent than the outdoor environment on the chain role played by class curriculum implementation, psychological atmosphere and curriculum implementation quality. Therefore, teachers should create a positive psychological atmosphere, organically combine the curriculum implementation with environmental planning and material delivery, cultivate a prepared language environment for children and support their active learning and development.

In this work, we study the performance of optical-based wireless hotspots in indoor and outdoor environments. The performance of optical-based wireless networks depends on many factors such as in point-to-point link alignment between transmitter and receiver, field of view of the receiver, semi-angle at half power of light emitting diode transmitter, background noise such as ambient and thermal noise, velocity of the user and so on. In this paper, we use the throughput-distance relationship model which has been derived analytically by taking into account above mentioned factors for evaluating the performance of VLC hotspot networks in indoor and outdoor environments at both, day and night conditions. Simulations are also performed to evaluate the performance of such hotspot network in the context of data download on move scenario. Simulation results reveal that there is a large impact of background noise on the performance of VLC hotspots. As expected, in both indoor and outdoor environments VLC hotspot performs better at night than at day time. Performance of VLC hotspot networks is also quantified in terms of received file size at different bit error rate requirements and velocities of mobile user. For example, in a typical indoor environment at night time a mobile user at velocity 1 m/s can download four megabyte file while passing through a VLC hotspot with a BER requirement of $$10^{(-3)}$$10(-3).

Tracking objects or humans using background subtraction is commonly used both in indoor and outdoor environment. But this method may not produce quality results due to illumination change, shadows, and occlusion occurred in indoor or outdoor environments. Song and Nevatia suggested a simultaneous tracking of face and body for human tracking in an indoor environment using Adaboost face detection and Camshift color tracking methods (2004). Changes in illumination are not considered in their work. In this research, an intelligent method for color tracking scheme using skin reflectance values is proposed to track the human in real-time video for varying illuminations both in indoor and outdoor environments. This research finds application in the live transmission of an indoor video conference or a live news cast from an outdoor as well as in the places where the illumination change occurs frequently

Human activity recognition plays a significant role in smart building applications, healthcare services, and security monitoring. In particular, WiFi‐based indoor wireless sensing system becomes increasingly popular due to its noninvasiveness. This work presents the design and implementation of DARMS, a Device‐free human Activity Recognition and Monitoring System that can be deployed with low‐cost commodity WiFi devices. DARMS is a passive wireless sensing system, and it can accurately distinguish various daily activities without the user wearing any sensor. DARMS makes two key technical contributions. First, an effective signal processing methodology is designed to extract the CSI features both in the time domain and frequency domain. Second, a dual‐channel neural network that combines temporal and frequency information is proposed to achieve fine‐grained activity recognition. In our experiments, DARMS shows outstanding performance in different indoor environments, with an average accuracy of 96.9% for fall detection and 93.3% for human activity recognition.

To address the limitations of traditional indoor localization techniques in indoor environments, the ARtag indoor localization technique is proposed to determine the optimal recognition distance to optimize the robot’s localization performance in indoor environments. The performance of the robot at different distances is evaluated by setting up an ARtag at different distances in an indoor environment, testing and comparing the robot’s recognition accuracy and response time at these distances. The experimental results show that at a distance of 25 cm, the robot has the fastest response speed and the highest stability, with a recognition accuracy of 100% and a response time of only 0.39 ms. The ARtag indoor localization technique can significantly improve the robot’s localization performance at specific distances, which can provide a basis for optimizing the performance of indoor robotic applications.

Micro-level energy sources from the ambient environment can be a viable alternative to increase the life of the sensor nodes from a WSN network. Harvesting energy from the environment - particularly solar energy, is a technique that can support the operation of the sensor nodes. The power extracted from the luminous radiation is extremely variable, depending on day/night alternation, atmospheric conditions, temperature, geographical location, indoor or outdoor environment - which makes the energy harvested insufficient for use as an exclusive power source to supply sensor nodes. A special case is the indoor environment, in which the solar radiation is strongly reduced, compared to the outside environment, and artificial lighting levels are usually below 1000 Lux. We made experiments on an indoor solar cell using illumination from three different sources: fluorescent, incandescent, LED. Detailed measurements, performance comparisons, and conclusions are presented.

Human activity recognition is becoming the vital underpinning for a myriad of emerging applications in the field of human-computer interaction, mobile computing, and smart grid. Besides the utilization of up-to-date sensing techniques, modern activity recognition systems also require a machine learning (ML) algorithm that leverages the sensory data for identification purposes. In view of the unique characteristics of the measurement data and the ML challenges thereof, we propose a non-intrusive human activity recognition system that only uses existing commodity WiFi routers. The core of our system is a novel multiple kernel representation learning (MKRL) framework that automatically extracts and combines informative patterns from the Channel State Information (CSI) measurements. The MKRL firstly learns a kernel string representation from time, frequency, wavelet, and shape domains with an efficient greedy algorithm. Then it performs information fusion from diverse perspectives based on multi-view kernel learning. Moreover, different stages of MKRL can be seamlessly integrated into a multiple kernel learning framework to build up a robust and comprehensive activity classifier. Extensive experiments are conducted in typical indoor environments and the experimental results demonstrate that the proposed system outperforms existing methods and achieves a 98\% activity recognition accuracy.

The objective of this study is to characterize indoor and outdoor levels of volatile organic compounds (VOCs) and formaldehyde (HCHO) and identify indoor emission sources in thirty elderly care centers (ECCs) located in the Seoul metropolitan city and Gyeonggi province in Korea. Air monitoring samples from indoor and outdoor environments were collected from January to December in 2007. Statistical analyses of indoor and outdoor VOCs and HCHO levels in three rooms (a bedroom, living, and dining rooms) of each ECC were performed, and these were compared to identify environmental factors associated with an increase of indoor pollution levels. Total volatile organic compounds (TVOC) levels were significantly (p<0.05) different between indoor (230.7±1.7 μg/m3) and outdoor (137.8±1.9 μg/m3) environments, with an I/O ratio of 1.67. The indoor HCHO level (20.1±1.6 μg/m3) was significantly (p<0.05) higher than the outdoor level (8.1±1.9 μg/m3), with an I/O ratio of 2.48. Indoor VOCs and HCHO levels in the bedrooms were significantly (p<0.05) higher than those in the living and dining rooms. Furthermore, indoor levels of VOCs and HCHO at ECCs were significantly (p<0.05) different depending on environmental factors such as the use of carpet, paint, and wooden furniture. In multiple regression analysis, indoor VOCs and HCHO levels at ECCs were significantly (p<0.05) correlated with two micro-environmental factors: the use of carpet and paint. This study confirmed that indoor VOCs and HCHO levels were significantly higher than those in outdoor environments. These air pollutants were mainly emitted from indoor sources, such as carpet, paint, and construction materials at the ECCs in Korea.

Crizzling is an alteration pathology related to non-balanced glasses (high content of flux oxides and low content of stabilizer ones), which can produce an intense damage on glasses such as transparency losses, appearance of drops on the surface or cracking. In this work, two case studies (outdoor and indoor environment) were presented. It was observed that the main alteration agent for these glasses was the water (rain, condensation and environmental humidity). In the outdoor environment, the rain washed away the [OH−] ions formed during the alteration; however, the exposure to cyclic conditions accelerated its alteration rate. In the indoor environment, the hygroscopicity of the glasses attracts the environmental humidity and produces the degradation.

The increasing human population and the increasing number of vehicles in Tirana, Albania, emerges the need for extensive measurements of noise levels. The 831 measurements of noise levels were taken in Tirana in November 2015 for education purposes. Measurements were collected in eight locations in indoor like a classroom in a school building environment, a library and a healthcare facility and in outdoor environments like five crossroads. Noise levels were then compared with domestic and World Health Organization (WHO) standards. The logarithmic average of noise levels and the maximum value of noise level measured for the period of measurement (LAFmax) were interpolated using Interpolated Noise Levels for Observer Points in ArcGIS producing noise level maps for crossroads. The logarithmic average of noise levels and LAFmax measurements were respectively above domestic and WHO standards in 96% and 100% of outdoor environment and indoor environment locations. Interpolated values of logarithmic average of noise levels and LAFmax for five crossroads were above domestic and WHO standards indicating noise levels in road traffic could remain high. A yearly acoustic measurement for vehicles should be implemented. Participatory measurements of noise levels in quite indoor environments can be used to increase the awareness of inhabitants in Tirana.

In this paper, a thorough research to the small unmanned aerial vehicle localization technology based on the fusion of optical flow and inertial navigation information has been conducted. An experiment platform based on Pixhawk and PX4FLOW is built to test the performance of optical flowAnertial localization system Experiment results shows that this system can work at both indoor and outdoor environment. In the indoor environment, the optical flowAnertial positioning system has better accuracy compared with the system using pure optical flow information. In the outdoor environment, the results proves that this system is a potential solution for UAV positioning in GPS-denied environment-with an error of 5 meters.

The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE) method for a Pedestrian Dead Reckoning (PDR) system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL) algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment.