347 research outputs found
Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG
Security and safety is one the main concerns both for governments and for private
companies in the last years so raising growing interests and investments in
the area of biometric recognition and video surveillance, especially after the sad
happenings of September 2001. Outlays assessments of the U.S. government for
the years 2001-2005 estimate that the homeland security spending climbed from
100 billion of 2005. In this lapse of
time, new pattern recognition techniques have been developed and, even more
important, new biometric traits have been investigated and refined; besides
the well-known physical and behavioral characteristics, also physiological measures
have been studied, so providing more features to enhance discrimination
capabilities of individuals. This dissertation proposes the design of a multimodal
biometric platform, FAIRY, based on the following biometric traits: ear,
face, iris EEG and ECG signals. In the thesis the modular architecture of the
platform has been presented, together with the results obtained for the solution
to the recognition problems related to the different biometrics and their possible
fusion. Finally, an analysis of the pattern recognition issues concerning the
area of videosurveillance has been discussed
Body swarm interface (BOSI) : controlling robotic swarms using human bio-signals
Traditionally robots are controlled using devices like joysticks, keyboards, mice and other
similar human computer interface (HCI) devices. Although this approach is effective and
practical for some cases, it is restrictive only to healthy individuals without disabilities,
and it also requires the user to master the device before its usage. It becomes complicated and non-intuitive when multiple robots need to be controlled simultaneously with these traditional devices, as in the case of Human Swarm Interfaces (HSI).
This work presents a novel concept of using human bio-signals to control swarms of
robots. With this concept there are two major advantages: Firstly, it gives amputees and
people with certain disabilities the ability to control robotic swarms, which has previously
not been possible. Secondly, it also gives the user a more intuitive interface to control
swarms of robots by using gestures, thoughts, and eye movement.
We measure different bio-signals from the human body including Electroencephalography
(EEG), Electromyography (EMG), Electrooculography (EOG), using off the shelf
products. After minimal signal processing, we then decode the intended control action
using machine learning techniques like Hidden Markov Models (HMM) and K-Nearest
Neighbors (K-NN). We employ formation controllers based on distance and displacement
to control the shape and motion of the robotic swarm. Comparison for ground truth for
thoughts and gesture classifications are done, and the resulting pipelines are evaluated with both simulations and hardware experiments with swarms of ground robots and aerial vehicles
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