Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort.Comment: submitted to the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON

Indoor occupant positioning system using active RFID deployment and particle filters

Abstract-This article describes a method for indoor positioning of human-carried active Radio Frequency Identification (RFID) tags based on the Sampling Importance Resampling (SIR) particle filtering algorithm. To use particle filtering methods, it is necessary to furnish statistical state transition and observation distributions. The state transition distribution is obstacle-aware and sampled from a precomputed accessibility map. The observation distribution is empirically determined by ground truth RSS measurements while moving the RFID tags along a known trajectory. From this data, we generate estimates of the sensor measurement distributions, grouped by distance, between the tag and sensor. A grid of 24 sensors is deployed in an office environment, measuring Received Signal Strength (RSS) from the tags, and a multithreaded program is written to implement the method. We discuss the accuracy of the method using a verification data set collected during a field-operational test

Applied Estimation of Mobile Environments

For many research problems, controlling and estimating the position of the mobile elements within an environment is desired. Realistic mobile environments are unstructured, but share a set of common features, such as position, speed, and constraints on mobility. To estimate within these real-world environments requires careful selection of the best-suited estimation tools and software and hardware technologies. This dissertation discusses the design and implementation of applied estimation infrastructures which overcome the challenges of real-world deployments. Estimating the mobility of water within rivers and estuaries is a significant area of study considering the need for fresh water all over the world. The Floating Sensor Network is designed to enable Lagrangian measurements, from devices called drifters, in these areas which was previously infeasible to collect. Two new types of drifters are developed: a low-cost Android smartphone based drifter and a motorized active drifter. The Android drifter is economical, allowing dense sensor deployments at low cost. Since drifter studies in rivers are often beset by drifters becoming pushed onto the banks, the active drifter is able to avoid these obstacles by using a Hamilton-Jacobi safety control algorithm. Multiple field operational tests validate that the active drifters successfully avoid becoming trapped in difficult terrain. Field tests also validate the operation of the estimation solution as a whole, measuring the water flow via drifters and producing flow fields of the river.The mobile environment of occupants within an office building is also studied extensively. This dissertation introduces the environmental sensing platform for indoor occupant studies. The platform includes a design of a battery-powered environmental sensor device and the communication architecture needed to collect data into a central repository. The sensor devices themselves communicate via WiFi technology and have a rich suite of sensors, including passive infrared, temperature, humidity, light level and acceleration. Electrical current consumption measurements from the sensors show that they can operate for over 5 years on a single battery. Discussed is how these sensors can be used for occupant tracking and occupant estimation, either via the on-board instruments, or instruments which are added to the devices via an expansion port.A unified particle filter is proposed which can both estimate occupancy and track occupants within a building. This dissertation presents several prerequisite studies to motivate this direction: Two studies are performed to understand how occupancy and occupant activity affects measurable variables: particulate matter and CO2. These variables are chosen as they are otherwise important for monitoring indoor air quality. Experimental studies show that there are indeed correlations between occupant activity and these variables. Furthermore, an estimator can be built which estimates the occupancy of a conference room, given CO2 measurements. Our third study accomplishes occupant tracking using a particle filtering framework and signal strength measurements from a radio-based indoor positioning system. The implementation forms a basis from which to build the unified particle filter

Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupantcarried multi-purpose sensors. Hypotheses with respect to each type of measurements are verified, including temperature, humidity, and light level collected during eight typical activities: sitting in lab / cubicle, indoor walking / running, resting after physical activity, climbing stairs, taking elevators, and outdoor walking. Our main contribution is the development of features for activity and location recognition based on environmental measurements, which exploit location- and activity-specific characteristics and capture the trends resulted from the underlying physiological process. The features are statistically shown to have good separability and are also information-rich. Fusing environmental sensing together with acceleration is shown to achieve classification accuracy as high as 99.13%. For building applications, this study motivates a sensor fusion paradigm for learning individualized activity, location, and environmental preferences for energy management and user comfort

Building-in-Briefcase: A Rapidly-Deployable Environmental Sensor Suite for the Smart Building.

A building’s environment has profound influence on occupant comfort and health. Continuous monitoring of building occupancy and environment is essential to fault detection, intelligent control, and building commissioning. Though many solutions for environmental measuring based on wireless sensor networks exist, they are not easily accessible to households and building owners who may lack time or technical expertise needed to set up a system and get quick and detailed overview of environmental conditions. Building-in-Briefcase (BiB) is a portable sensor network platform that is trivially easy to deploy in any building environment. Once the sensors are distributed, the environmental data is collected and communicated to the BiB router via the Transmission Control Protocol/Internet Protocol (TCP/IP) and WiFi technology, which then forwards the data to the central database securely over the internet through a 3G radio. The user, with minimal effort, can access the aggregated data and visualize the trends in real time on the BiB web portal. Paramount to the adoption and continued operation of an indoor sensing platform is battery lifetime. This design has achieved a multi-year lifespan by careful selection of components, an efficient binary communications protocol and data compression. Our BiB sensor is capable of collecting a rich set of environmental parameters, and is expandable to measure others, such as CO 2 . This paper describes the power characteristics of BiB sensors and their occupancy estimation and activity recognition functionality. We have demonstrated large-scale deployment of BiB throughout Singapore. Our vision is that, by monitoring thousands of buildings through BiB, it would provide ample research opportunities and opportunities to identify ways to improve the building environment and energy efficiency

Building-in-Briefcase: A Rapidly-Deployable Environmental Sensor Suite for the Smart Building

A building’s environment has profound influence on occupant comfort and health. Continuous monitoring of building occupancy and environment is essential to fault detection, intelligent control, and building commissioning. Though many solutions for environmental measuring based on wireless sensor networks exist, they are not easily accessible to households and building owners who may lack time or technical expertise needed to set up a system and get quick and detailed overview of environmental conditions. Building-in-Briefcase (BiB) is a portable sensor network platform that is trivially easy to deploy in any building environment. Once the sensors are distributed, the environmental data is collected and communicated to the BiB router via the Transmission Control Protocol/Internet Protocol (TCP/IP) and WiFi technology, which then forwards the data to the central database securely over the internet through a 3G radio. The user, with minimal effort, can access the aggregated data and visualize the trends in real time on the BiB web portal. Paramount to the adoption and continued operation of an indoor sensing platform is battery lifetime. This design has achieved a multi-year lifespan by careful selection of components, an efficient binary communications protocol and data compression. Our BiB sensor is capable of collecting a rich set of environmental parameters, and is expandable to measure others, such as CO 2 . This paper describes the power characteristics of BiB sensors and their occupancy estimation and activity recognition functionality. We have demonstrated large-scale deployment of BiB throughout Singapore. Our vision is that, by monitoring thousands of buildings through BiB, it would provide ample research opportunities and opportunities to identify ways to improve the building environment and energy efficiency

Building-in-Briefcase: A Rapidly-Deployable Environmental Sensor Suite for the Smart Building.

A building’s environment has profound influence on occupant comfort and health. Continuous monitoring of building occupancy and environment is essential to fault detection, intelligent control, and building commissioning. Though many solutions for environmental measuring based on wireless sensor networks exist, they are not easily accessible to households and building owners who may lack time or technical expertise needed to set up a system and get quick and detailed overview of environmental conditions. Building-in-Briefcase (BiB) is a portable sensor network platform that is trivially easy to deploy in any building environment. Once the sensors are distributed, the environmental data is collected and communicated to the BiB router via the Transmission Control Protocol/Internet Protocol (TCP/IP) and WiFi technology, which then forwards the data to the central database securely over the internet through a 3G radio. The user, with minimal effort, can access the aggregated data and visualize the trends in real time on the BiB web portal. Paramount to the adoption and continued operation of an indoor sensing platform is battery lifetime. This design has achieved a multi-year lifespan by careful selection of components, an efficient binary communications protocol and data compression. Our BiB sensor is capable of collecting a rich set of environmental parameters, and is expandable to measure others, such as CO 2 . This paper describes the power characteristics of BiB sensors and their occupancy estimation and activity recognition functionality. We have demonstrated large-scale deployment of BiB throughout Singapore. Our vision is that, by monitoring thousands of buildings through BiB, it would provide ample research opportunities and opportunities to identify ways to improve the building environment and energy efficiency