Crowd Counting Through Walls Using WiFi

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.Comment: 10 pages, 14 figure

Preparation of NiO catalyst on FeCrAI substrate using various techniques at higher oxidation process

The cheap nickel oxide (NiO) is a potential catalyst candidate to replace the expensive available platinum group metals (PGM). However, the current methods to adhere the NiO powder on the metallic substrates are complicated. Therefore, this work explored the development of nickel oxide using nickel (Ni) on FeCrAl substrate through the combination of nickel electroplating and oxidation process for catalytic converter application. The approach was started with assessment of various nickel electroplating process based on the weight gain during oxidation. Then, the next experiment used the best process in which the pre-treatment using the solution of SiC and/or Al2O3 in methanol. The specimens then were carried out to short term oxidation process using thermo gravimetric analysis (TGA) at 1000 o C. Meanwhile, the long term oxidation process was conducted using an automatic furnace at 900, 1000 and 1100 o C. The atomic force microscopy (AFM) was used for surface analysis in nanometer range scale. Meanwhile, roughness test was used for roughness measurement analysis in micrometer range scale. The scanning electron microscope (SEM) attached with energy dispersive X-ray (EDX) were used for surface and cross section morphology analysis. The specimen of FeCrAl treated using ultrasonic prior to nickel electroplating showed the lowest weight gain during oxidation. The surface area of specimens increased after ultrasonic treatment. The electroplating process improved the high temperature oxidation resistance. In short term oxidation process indicated that the ultrasonic with SiC provided the lower parabolic rate constant (kp) and the Al2O3 and NiO layers were also occurred. The Ni layer was totally disappeared and converted to NiO layer on FeCrAl surface after long term oxidation process. From this work, the ultrasonic treatment prior to nickel electroplating was the best method to adhere NiO on FeCrAl substrate

Low-Cost Sensors and Biological Signals

Many sensors are currently available at prices lower than USD 100 and cover a wide range of biological signals: motion, muscle activity, heart rate, etc. Such low-cost sensors have metrological features allowing them to be used in everyday life and clinical applications, where gold-standard material is both too expensive and time-consuming to be used. The selected papers present current applications of low-cost sensors in domains such as physiotherapy, rehabilitation, and affective technologies. The results cover various aspects of low-cost sensor technology from hardware design to software optimization

Body sensor network for in-home personal healthcare

A body sensor network solution for personal healthcare under an indoor environment is developed. The system is capable of logging the physiological signals of human beings, tracking the orientations of human body, and monitoring the environmental attributes, which covers all necessary information for the personal healthcare in an indoor environment. The major three chapters of this dissertation contain three subsystems in this work, each corresponding to one subsystem: BioLogger, PAMS and CosNet. Each chapter covers the background and motivation of the subsystem, the related theory, the hardware/software design, and the evaluation of the prototype’s performance

Detecting abnormal events on binary sensors in smart home environments

With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies.PostprintPeer reviewe

Optimal Inertial Sensor Placement and Motion Detection for Epileptic Seizure Patient Monitoring

Use of inertial sensory systems to monitor and detect seizure episodes in patients suffering from epilepsy is investigated via numerical simulations and experiments. Numerical simulations employ a mathematical model that is able to predict human body dynamic responses during a typical epileptic seizure. An optimized inertial sensor placement procedure is developed to address achievement of highest possible sensing resolution in determining angular accelerations with minimal errors. In addition, a joint torque estimation procedure is formulated to assist in the future development of a possible detection scheme. Experimental motion data obtained from an epileptic seizure patient as well as a healthy subject via a cluster of inertial measurement sensors formed a basis for proposing a suitable detection scheme based on non-linear response analysis. In particular, preliminary experimental data analysis has shown that the proposed modified Poincaré Map based scheme can become an effective tool in detecting of seizure via inertial measurements

Body Motion Capture Using Multiple Inertial Sensors

Near-fall detection is important for medical research since it can help doctors diagnose fall-related diseases and also help alert both doctors and patients of possible falls. However, in people’s daily life, there are lots of similarities between near-falls and other Activities of Daily Living (ADLs), which makes near-falls particularly difficult to detect. In order to find the subtle difference between ADLs and near-fall and accurately identify the latter, the movement of whole human body needs to be captured and displayed by a computer generated avatar. In this thesis, a wireless inertial motion capture system consisting of a central control host and ten sensor nodes is used to capture human body movements. Each of the ten sensor nodes in the system has a tri-axis accelerometer and a tri-axis gyroscope. They are attached to separate locations of a human body to record both angular and acceleration data with which body movements can be captured by applying Euler angle based algorithms, specifically, single rotation order algorithm and the optimal rotation order algorithm. According to the experiment results of capturing ten ADLs, both the single rotation order algorithm and the optimal rotation order algorithm can track normal human body movements without significantly distortion and the latter shows higher accuracy and lower data shifting. Compared to previous inertial systems with magnetometers, this system reduces hardware complexity and software computation while ensures a reasonable accuracy in capturing human body movements

A telerehabilitation system based on wireless motion capture sensors

The constant growth of the elderly population in the world creates new challenges and opportunities in health care systems. New technological solutions have to be found in order to meet the needs and demands of our aging society. The welfare and quality of life of the elderly population must be a priority. Continuous physical activity will play an important role, due to the increase of the retirement age. However, physiotherapy can be expensive, even when the desire movements are autonomous and simple, also requires people to move to rehabilitation centres. Within this context, this paper describes the development and preliminary tests of a wireless sensor network, based on wearable inertial and magnetic sensors, applied to the capture of human motion. This will enable a personalized home-based rehabilitation system for the elderly or people in remote physical locations.Project “AAL4ALL”, co-financed by the European Community Fund FEDER through COMPETE – Programa Operacional Factores de Competitividade (POFC).FCT – Foundation for Science and Technology – Lisbon, Portugal, through project PEst-C/CTM/LA0025/2013

Truck-based mobile wireless sensor networks for the experimental observation of vehicle–bridge interaction

Heavy vehicles driving over a bridge create a complex dynamic phenomenon known as vehicle–bridge interaction. In recent years, interest in vehicle–bridge interaction has grown because a deeper understanding of the phenomena can lead to improvements in bridge design methods while enhancing the accuracy of structural health monitoring techniques. The mobility of wireless sensors can be leveraged to directly monitor the dynamic coupling between the moving vehicle and the bridge. In this study, a mobile wireless sensor network is proposed for installation on a heavy truck to capture the vertical acceleration, horizontal acceleration and gyroscopic pitching of the truck as it crosses a bridge. The vehicle-based wireless monitoring system is designed to interact with a static, permanent wireless monitoring system installed on the bridge. Specifically, the mobile wireless sensors time-synchronize with the bridge's wireless sensors before transferring the vehicle response data. Vertical acceleration and gyroscopic pitching measurements of the vehicle are combined with bridge accelerations to create a time-synchronized vehicle–bridge response dataset. In addition to observing the vehicle vibrations, Kalman filtering is adopted to accurately track the vehicle position using the measured horizontal acceleration of the vehicle and positioning information derived from piezoelectric strip sensors installed on the bridge deck as part of the bridge monitoring system. Using the Geumdang Bridge (Korea), extensive field testing of the proposed vehicle–bridge wireless monitoring system is conducted. Experimental results verify the reliability of the wireless system and the accuracy of the vehicle positioning algorithm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90810/1/0964-1726_20_6_065009.pd