What is the largest public wearable accelerometer dataset?
You can check these additional ones:

HAR: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
It is collected by attaching a smart-phone and it has accelerometer and gyroscope data.
It has 30 participants conducting 6 different activities. The acceleration is at 50 Hz and they are using 3D accelerometers.

USCHAD: https://dl.acm.org/citation.cfm?doid=2370216.2370438 Have a look at this paper. Here you will find the data as well.

It has 14 subjects (7 male, 7 female) and there are 12 different activities. Additionally, it has accelerometers and gyroscopes, and it has a ground truth labeling by an observer standing nearby. They are sampled on 100Hz. Again they are using 3D accelerometers.

I would strongly suggest feature-engineering by defining time-windows where you segment the time-series data. After defining the small windows of a few seconds, you can have one activity per window. Then you can easily extract some time-dependent features such as mean, correlation, acceleration, orientation, etc.. Also, Fast Fourier transform on these small windows can be applied so you can extract many frequency domain features like energy, coherence, etc.