3 research outputs found
Π Π°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΡΠ΅Π΄ΡΡΠ² ΡΠ±ΠΎΡΠ° ΠΈ Π»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° 3D-Π²ΠΈΠ΄Π΅ΠΎΠ΄Π°Π½Π½ΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π²ΡΠ΅ΠΌΡΠΏΡΠΎΠ»ΡΡΠ½ΠΎΠΉ ΠΊΠ°ΠΌΠ΅ΡΡ ΠΈ ΠΠΊΡΠΎΡΠ½ΠΎΠ³ΠΎ ΠΡΠΎΠ»ΠΎΠ³Π°
ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΌΡ 3D-Π²ΠΈΠ΄Π΅ΠΎΠ½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΎΠ±ΡΠ΅ΠΊΡΠ½ΠΎ-ΠΎΡΠΈΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ Π»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π ΠΎΡΠ»ΠΈΡΠΈΠ΅ ΠΎΡ ΠΎΠ±ΡΡΠ½ΠΎΠ³ΠΎ 2D-Π²ΠΈΠ΄Π΅ΠΎΠ½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ, ΠΌΠ΅ΡΠΎΠ΄Ρ ΡΡΡΡ
ΠΌΠ΅ΡΠ½ΠΎΠ³ΠΎ Π·ΡΠ΅Π½ΠΈΡ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡ Π½Π°Π΄ΡΠΆΠ½ΠΎΠ΅ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΠ΅ ΡΠ°ΡΡΠ΅ΠΉ ΡΠ΅Π»Π°, ΡΡΠΎ Π΄Π΅Π»Π°Π΅Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠΌ Π½ΠΎΠ²ΡΠ΅ ΠΏΠΎΡΡΠ°Π½ΠΎΠ²ΠΊΠΈ Π·Π°Π΄Π°ΡΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π½Π°Π»ΠΈΠ·Π° ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ Π»ΡΠ΄Π΅ΠΉ Π² ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Π²ΠΈΠ΄Π΅ΠΎΠ½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ. ΠΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΌΡ Π²ΠΈΠ΄Π΅ΠΎΠ½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΎΠΏΠΈΡΡΠ²Π°ΡΡ ΡΠ»ΠΎΠΆΠ½ΠΎΠ΅ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ Π»ΡΠ΄Π΅ΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΉ ΠΏΡΠΎΡΡΡΡ
Π΄Π΅ΠΉΡΡΠ²ΠΈΠΉ ΠΈ ΠΏΠΎΠ·. Π¦Π΅Π»Ρ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠΈΡ
ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ² Π»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Π° Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ 3D-Π²ΠΈΠ΄Π΅ΠΎΠ½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΡ.Π Π°Π±ΠΎΡΠ° Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° ΠΏΡΠΈ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ Π Π€Π€Π, Π³ΡΠ°Π½Ρ β 16-29-09626-ΠΎΡΠΈ_ΠΌ
Automatic visual detection of human behavior: a review from 2000 to 2014
Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012
Extending the Design Space of E-textile Assistive Smart Environment Applications
The thriving field of Smart Environments has allowed computing devices to gain new capabilities and develop new interfaces, thus becoming more and more part of our lives. In many of these areas it is unthinkable to renounce to the assisting functionality such as e.g. comfort and safety functions during driving, safety functionality while working in an industrial plant, or self-optimization of daily activities with a Smartwatch.
Adults spend a lot of time on flexible surfaces such as in the office chair, in bed or in the car seat. These are crucial parts of our environments. Even though environments have become smarter with integrated computing gaining new capabilities and new interfaces, mostly rigid surfaces and objects have become smarter. In this thesis, I build on the advantages flexible and bendable surfaces have to offer and look into the creation process of assistive Smart Environment applications leveraging these surfaces. I have done this with three main contributions.
First, since most Smart Environment applications are built-in into rigid surfaces, I extend the body of knowledge by designing new assistive applications integrated in flexible surfaces such as comfortable chairs, beds, or any type of soft, flexible objects. These developed applications offer assistance e.g. through preventive functionality such as decubitus ulcer prevention while lying in bed, back pain prevention while sitting on a chair or emotion detection while detecting movements on a couch.
Second, I propose a new framework for the design process of flexible surface prototypes and its challenges of creating hardware prototypes in multiple iterations, using resources such as work time and material costs. I address this research challenge by creating a simulation framework which can be used to design applications with changing surface shape. In a first step I validate the simulation framework by building a real prototype and a simulated prototype and compare the results in terms of sensor amount and sensor placement. Furthermore, I use this developed simulation framework to analyse the influence it has on an application design if the developer is experienced or not.
Finally, since sensor capabilities play a major role during the design process, and humans come often in contact with surfaces made of fabric, I combine the integration advantages of fabric and those of capacitive proximity sensing electrodes. By conducting a multitude of capacitive proximity sensing measurements, I determine the performance of electrodes made by varying properties such as material, shape, size, pattern density, stitching type, or supporting fabric. I discuss the results from this performance evaluation and condense them into e-textile capacitive sensing electrode guidelines, applied exemplary on the use case of creating a bed sheet for breathing rate detection