three-stages of data sharing:
●encrypted sharing vessels for data analysis systems
●lateral hierarchy: environmental collections, wearable sensing devices data, invasive brain activities data
●longitudinal hierarchy: passive data for infrastructure functioning, passive data for social-economical functioning, active data for citizen’s personalized needs
From interpersonal communication to intuitive knowing: While we live a peaceful and liberal life until our society went into chaos because of biotechnological applications for mankind and the built environment. The more we blurred the boundary of human, machine, and the environment, the more we strike on our social norm and order, thus our world was concorde more and more by dictators for controlling efficiency.
As passive data sensing become widely accepted for wellness and mental health purpose, many wearable technology products gain rapid market shares. Recently the enthusiasm of wearable technology is clearly booming as we see Apple and Google’s Fitbit deals stay at the top of finance media. The vision of this study is to discuss possibilities of a shifting purpose from individual based product to a further community for intuitive knowing
The Holo Flower is a real-time data visualization interface displayed in holography on digital devices. The input data of the flower is based on biometric data and EEG signals from Emotiv Epoc+ headband. The concept behind the Holo Flower is to unrolling and visualizing vulnerable details of daily brain events.
Reveal a social activity or relationship phenomenon at a reformed quality after technology caused social destruction
Convey how data’s as an information medium could step into daily activities and fulfill social desire
Illustrate the trend of unrolling and visualizing all the vulnerabilities of human natures
Brain Data Collecting: Emotiv Epoc+ (14(+CMS/DRL, references) electrodes, sequential sampling 128 sps, 0.2-42 hz, Lipo battery(12 hrs), embedded gyroscope)
Digital Interface: Unity3D
Server: Cortex Cloud Server supported by Emotiv Company
The real-time EEG data is mapped from 14 individual stream(Fig.2) to the central line of each flower petals (Fig. 1). The initial flower petal mesh(Fig. 3) is designed in Autodesk 3ds Max.
Proposed Flower Behaviors:
The designed deforming behavior based on mesh vertices velocity is tested with real-time user testing.
A method simulating pressure is developed in C# to generate [ray cast] 14 mapped locations onto flower meshes. Each ray is moving from petal end to flower center based on EEG data: ApplyPressureToPoint(Vector3 _point, float _pressure)
Based on several testing, a decent range of pressure force that applied on the mesh vertices was noted. (Fig 4-5)
To discuss possibilities of AI created visualization mixed with human data engineered visualization, this chapter examined generative machine learning tools for creating responsive material finish on the the flower rendering. Conditional GAN is applied to a responsive tile sample in order to create an tile pattern while maintaining its attribution to a particular class.
Based on: Implementation of Creative Adversarial Networks https://arxiv.org/pdf/1706.07068.pdf. Phillip Kravtsov, Phillip Kuznetsov
Tile Testing: CGAN
Original tiles were generated from 27 classes of painting. Later Gaussian noise was added. The tile is then sampled into a consistent style.
Tile Testing: Imaging toning, hue, and pixel glitch
In order to reach a more deemed color but active visual quality, an adapting dynamic hue is mapped to tile image.(Fig 11) Hue gradient map used: dark red - orange - blue - white
A glitch style(Fig 12) is applied to the tile by taking every pixel of the tile image and shifted by a delta vector
4.2 Prefab of Tile Filter
Tone of the tile and Glitch Speed are applied to the prefab of flower render material as Unity Shader.
var index : int = (Time.time * framesPerSecond) % frames.Length;
renderer.material.mainTexture = frames[index];