NetherlandsForensicInstitute/NFI_FARED_Digital_Traces

This is the README file for the dataset Netherlands Forensic Institute: Forensic Activity Recognition Dataset (NFI_FARED). Two forms of data were collected: Digital Traces from iPhones worn on the subjects' bodies, and raw sensor signals from body-worn Inertial Measurement Units (IMUs). This dataset and README refers to the Digital Trace data. The IMU data is available here.

Published as a part of the paper "Forensic Activity Classification Using Digital Traces from iPhones: A Machine Learning-based Approach".

NFI_FARED contains Digital Trace data from 14 participants (8 male, 6 female), aged 26.6 ± 8.8 years old. Each subject carried four iPhones simultaneously during the data collection. The models and iOS versions are: 6+ (11.4.1), 7 (14.7.1), 11 (13.1.1), and XR (15.4.1). For further details on the data collection procedure, please refer to the paper.

Data is extracted from the databases cache_encryptedC.db and healthdb_secure.sqlite from the iPhones after the data collection experiments and processed into .pkl files using proprietary scripts at the NFI.

The datafiles df_dict_motionstate.pkl, df_dict_natalie.pkl, df_dict_stepcounthistory.pkl contain data extracted from the tables MotionStateHistory, NatalieHistory and StepCountHistory, respectively, from the file cache_encryptedC.db.

The datafiles df_dict_healthdb_floors.pkl, df_dict_healthdb_distance.pkl, df_dict_healthdb_steps.pkl contain data pertaining to steps, distances and floors extracted from the tables sample and quantity_samples from the file healthdb_secure.sqlite

In all datafiles, columns containing experimental metadata have column names with prefix META_. These are:

• META_carrying_location: Carry location of the iPhone. Value is one of 'hand', 'frontpocket', ‘backpocket, 'breastpocket', or 'rucksack'.

• META_telephone_type: Type of iPhone. Value is one of 'Iphone6+_IOS_11.4.1', 'IphoneXR_IOS_15.4.1', 'Iphone11_IOS_13.1.1', or 'Iphone7_IOS_14.7.1'

• META_test_subject: Subject ID number

• META_experiment: number of experimental session

• META_label_activity: assigned ground-truth activity for the corresponding registration.

Activity labels are as follows: standing, sitting, walking, running, train, car, tram, bus, cycling, stair_up, stair_down, escalator_down, escalator_up, elevator_down, elevator_up, dragging, throwing, punching, kicking . Rows labeled no activity are from untracked moments in the recording sessions e.g. going from the office where the iPhones were provided to the starting location. There are no assurances on the activities performed (or not performed) in these periods.

Other columns in the datafiles contain data registered by the phones. Variable names are the same as in the original databases. Timestamps startTime, start_date and end_date have been converted from Apple epoch to local time and (local time) has been appended to the column name, e.g. startTime (local time). Original epoch time has (epoch) appended to columns where it is still present, e.g. startTime (epoch).

Digital Traces are in general not logged at regular intervals, so traces from different databases are likely not to be aligned in time. For this reason we recommend aggregating traces from the different databases to larger, e.g. one minute, intervals to achieve consistency. Python scripts for processing the .pkl files into .csv files aggregated at specified intervals can be found on the project GitHub .

Citation

If you wish to use this dataset in your research please cite:

@misc{mccarthy2025forensicactivityclassificationusing,
      title={Forensic Activity Classification Using Digital Traces from iPhones: A Machine Learning-based Approach}, 
      author={Conor McCarthy and Jan Peter van Zandwijk and Marcel Worring and Zeno Geradts},
      year={2025},
      eprint={2512.03786},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2512.03786}, 
}

Contact

For questions regarding the data processing, the paper, and/or project GitHub please contact Conor McCarthy: [email protected]

For questions regarding the data collection please contact Jan Peter van Zandwijk: [email protected]