Machine Learning for the Quantified Self

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Presentation transcript:

Machine Learning for the Quantified Self Lecture 2 Basic of Sensory Data

Dataset (1) During the course we will use a running example provided by CrowdSignals.io People share their mobile sensors data (smart phone and smart watch) and get paid for annotating their data with activities

Dataset (2)

Mobile phone measurements (examples) Accelerometer Measures the changes is forces upon the phone in the x-y-z plane Gyroscope Orientation of the phone compared to the earth’s surface Magnetometer Measures x-y-z orientation compared to the earth’s magnetic field

The raw data What does the raw CrowdSignals data look like? Separate tables per measurement Specific time point measurements: Interval measurements

Transforming the raw data (1) Need to combine these table, but how? Select a step size Δt you want to consider in the data this will represent one discrete time step start at the earliest time point in the data find all measurements for each single attribute associated with each interval we consider categorical features (e.g. label) as a number of binary features combine their values (e.g. average for heart rate or accelerometer or sum for the label)

Transforming the raw data (2)

Exploring the data (1) Let us consider a dataset from CrowdSignals which covers around 2 hours of data Imagine we take a step size of Δt = 1 minute and Δt = 250 milliseconds What difference would you expect in the spread of the data? What are the pros and cons of a higher value for Δt (less fine grained)?

Exploring the data (2)

Computational Intelligence, Unsupervised Learning Exploring the data (2) Computational Intelligence, Unsupervised Learning

Computational Intelligence, Unsupervised Learning Exploring the data (2) Computational Intelligence, Unsupervised Learning

Exploring the data (5)

Machine Learning Tasks What kind of tasks could we identify in this dataset? a classification problem, namely predicting the label (i.e. activity) based on the sensors a regression problem, namely predicting the heart rate based on the other sensory values and the activity