Discovering user mobility and activity in smart lighting environments

"Smart lighting" environments seek to improve energy efficiency, human productivity and health by combining sensors, controls, and Internet-enabled lights with emerging “Internet-of-Things” technology. Interesting and potentially impactful applications involve adaptive lighting that responds to individual occupants' location, mobility and activity. In this dissertation, we focus on the recognition of user mobility and activity using sensing modalities and analytical techniques. This dissertation encompasses prior work using body-worn inertial sensors in one study, followed by smart-lighting inspired infrastructure sensors deployed with lights. The first approach employs wearable inertial sensors and body area networks that monitor human activities with a user's smart devices. Real-time algorithms are developed to (1) estimate angles of excess forward lean to prevent risk of falls, (2) identify functional activities, including postures, locomotion, and transitions, and (3) capture gait parameters. Two human activity datasets are collected from 10 healthy young adults and 297 elder subjects, respectively, for laboratory validation and real-world evaluation. Results show that these algorithms can identify all functional activities accurately with a sensitivity of 98.96% on the 10-subject dataset, and can detect walking activities and gait parameters consistently with high test-retest reliability (p-value < 0.001) on the 297-subject dataset. The second approach leverages pervasive "smart lighting" infrastructure to track human location and predict activities. A use case oriented design methodology is considered to guide the design of sensor operation parameters for localization performance metrics from a system perspective. Integrating a network of low-resolution time-of-flight sensors in ceiling fixtures, a recursive 3D location estimation formulation is established that links a physical indoor space to an analytical simulation framework. Based on indoor location information, a label-free clustering-based method is developed to learn user behaviors and activity patterns. Location datasets are collected when users are performing unconstrained and uninstructed activities in the smart lighting testbed under different layout configurations. Results show that the activity recognition performance measured in terms of CCR ranges from approximately 90% to 100% throughout a wide range of spatio-temporal resolutions on these location datasets, insensitive to the reconfiguration of environment layout and the presence of multiple users.2017-02-17T00:00:00

3D indoor positioning of UAVs with spread spectrum ultrasound and time-of-flight cameras

Este trabajo propone el uso de un sistema híbrido de posicionamiento acústico y óptico en interiores para el posicionamiento 3D preciso de los vehículos aéreos no tripulados (UAV). El módulo acústico de este sistema se basa en un esquema de Acceso Múltiple por División de Código de Tiempo (T-CDMA), en el que la emisión secuencial de cinco códigos ultrasónicos de espectro amplio se realiza para calcular la posición horizontal del vehículo siguiendo un procedimiento de multilateración 2D. El módulo óptico se basa en una cámara de Tiempo de Vuelo (TOF) que proporciona una estimación inicial de la altura del vehículo. A continuación se propone un algoritmo recursivo programado en un ordenador externo para refinar la posición estimada. Los resultados experimentales muestran que el sistema propuesto puede aumentar la precisión de un sistema exclusivamente acústico en un 70-80% en términos de error cuadrático medio de posicionamiento.This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential emission of five spread spectrum ultrasonic codes is performed to compute the horizontal vehicle position following a 2D multilateration procedure. The optical module is based on a Time-Of-Flight (TOF) camera that provides an initial estimation for the vehicle height. A recursive algorithm programmed on an external computer is then proposed to refine the estimated position. Experimental results show that the proposed system can increase the accuracy of a solely acoustic system by 70–80% in terms of positioning mean square error.• Gobierno de España y Fondos para el Desarrollo Regional Europeo. Proyectos TARSIUS (TIN2015-71564-C4-4-R) (I+D+i), REPNIN (TEC2015-71426-REDT) y SOC-PLC (TEC2015-64835-C3-2-R) (I+D+i) • Junta de Extremadura, Fondos FEDER y Fondo Social Europeo. Proyecto GR15167 y beca predoctoral 45/2016 Exp. PD16030peerReviewe

Fast heuristic method to detect people in frontal depth images

This paper presents a new method for detecting people using only depth images captured by a camera in a frontal position. The approach is based on first detecting all the objects present in the scene and determining their average depth (distance to the camera). Next, for each object, a 3D Region of Interest (ROI) is processed around it in order to determine if the characteristics of the object correspond to the biometric characteristics of a human head. The results obtained using three public datasets captured by three depth sensors with different spatial resolutions and different operation principle (structured light, active stereo vision and Time of Flight) are presented. These results demonstrate that our method can run in realtime using a low-cost CPU platform with a high accuracy, being the processing times smaller than 1 ms per frame for a 512 × 424 image resolution with a precision of 99.26% and smaller than 4 ms per frame for a 1280 × 720 image resolution with a precision of 99.77%

CSM-428: Techniques used for Location-based Services: A Survey

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