droidcon Berlin 2014, 08.05.2014, Berlin, Germany
Quantified Self is about measuring, tracking, and analyzing data of our body and our daily life. The data can cover very different aspects, for example, food consumption, vital signs, mood, expenses, daily routines, environmental information, etc. Today, gathering data with wearable devices are very common, such as wristbands. The data is used to monitor or manage personal health and other environmental data. One goal of Quantified Self is to gain knowledge about oneself.
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4th International TEMOS Conference (HEALTHCARE ABROAD & MEDICAL TOURISM), Bonn, Germany (02.12.2013)
Self tracking is a trend where individual persons use sensors and mobile apps to collect and visualize personal data. The data is used to monitor or manage personal health. This talk explains motivations for self tracking, available (wearable) sensors and apps, and possible insights by analyzing the personal data.
The talk contains data from the bachelor thesis “Quantified Self – An Exploratory Study on the Profiles and Motivations of Self-Tracking” by Marcia Nißen (Karlsruhe Institute of Technology, 2013).
List of talks of the Birds-of-a-Feather session on Python for High Performance and Scientific Computing at SC13 (November 19, 2013, Denver):
Bohrium: Unmodified NumPy Code on CPU, GPU, and Cluster
Compiling Python Modules to Native Parallel Modules Using Pythran and OpenMP Annotations
ClusterShell, Python library and tools for scalable cluster administration
Doubling the Performance of Python/NumPy with less than 100 SLOC
A Problem Solving Environment for Stochastic Biological Simulations
High-Performance Python-based Simulations of Pressure and Temperature Waves in a Trace Gas Sensor
Peach-Py: A Python Framework for Developing High-Performance Assembly Kernels
Synergia: Driving Massively Parallel Particle Accelerator Simulations with Python