Could someone advise me how activities of daily living from (3 axis) accelerometer data can be detected using deep learning approach?
I would like to know what features I should use from a 3-axis accelerometer as an input in the model?
I have four different activities -- running, jumping, sitting, walking, and a data set (acceleration profile) collected using a 3-axis accelerometer sampled at 50 Hz.
The data features that you choose to use are relatively independent of the classification technique chosen. Read what data features other researchers have used when classifying activities of daily living.
The biggest advantage of deep learning over conventional supervised methods is its ability to learn features automatically. This means you do not need to design features by yourself, and just take the raw accelerometer data or processed data (e.g., ZCA proprocessing, FFT) as input to the first layer of the deep learning method you choose.
Depends on how you are planning to model the subject. You can try going for a typical sliding window approach and use a deep learning approach for classification, or maybe go for a time-step solution using RNN LSTM. These models might be simple but it will be a good starting point and baseline for other advance methods that you will try in the future.
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