For each participant, the study had been commenced by collecting their demographic and physically-related information. Then, we provided them with a dedicated smartphone (iPhone 6) and asked them to store it in their trousers' front pocket during the experiment. All the participant were asked to wear flat shoes. We then asked them to perform 6 different activities around the Queen Mary University of London's Mile End campus. For each trial, the researcher set up the phone and gave it to the current participants, then the researcher stood in a corner. Then, the participant pressed the start button of Crowdsense app and put it in their trousers' front pocket and performed the specified activity. We asked them to do it as natural as possible, like their everyday life. At the end of each trial, they took the phone out of their pocket and pressed the stop button. The exact places and routes for running all the activities are shown in the illustrative map in the following Figure.

The accelerometer measures the sum of two acceleration vectors: gravity and user acceleration. User acceleration is the acceleration that the user imparts to the device. Because Core Motion is able to track a device’s attitude using both the gyroscope and the accelerometer, it can differentiate between gravity and user acceleration. Sample rate - `10 Hz`.

There are 6 different labels: `downstairs`, `upstairs`, `sitting`, `standing`, `walking`, `jogging`.

Original dataset and description: https://github.com/mmalekzadeh/motion-sense (for informational purposes only, any use of the original dataset in a solution is grounds for disqualification)

```bibtex
@inproceedings{Malekzadeh:2019:MSD:3302505.3310068,
author = {Malekzadeh, Mohammad and Clegg, Richard G. and Cavallaro, Andrea and Haddadi, Hamed},
title = {Mobile Sensor Data Anonymization},
booktitle = {Proceedings of the International Conference on Internet of Things Design and Implementation},
series = {IoTDI '19},
year = {2019},
isbn = {978-1-4503-6283-2},
location = {Montreal, Quebec, Canada},
pages = {49--58},
numpages = {10},
url = {http://doi.acm.org/10.1145/3302505.3310068},
doi = {10.1145/3302505.3310068},
acmid = {3310068},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {adversarial training, deep learning, edge computing, sensor data privacy, time series analysis}
}
```