A Samsung GearS3 Frontier Smartwatch Wearable Device Data Extraction Tool

GearGadget utilizes the Tizen Studio Software Development Kit’s Smart Development Bridge (SDB) tool in order to connect a Samsung™ Gear S3 Frontier smartwatch wearable device wirelessly to a host PC for data extraction. The tool creates a filesystem extraction of the wearable, which can be processed across most mobile forensic tools using a standard Android filesystem parsers.

The tool is packaged as an .OVF virtual machine (VM) with all of the required software already installed in order to make the extraction process as efficient as possible, and results in a filesystem data acquisition, a log file documenting the acquired data, and an MD5 hash value for the data acquisition.

Download the GearGadget Data Extraction VM:

GearGadget Virtual Machine Download

NOTE: This 9.2GB VM is an open virtualization format file (.ovf) Linux Distro (Ubuntu 18.04) compressed into a 7zip container file (.7z).  To uncompress you will need to download 7zip by visiting 

VM Username: GearGadget VM Password: forensics

Learn how to prepare the Gear S3 device for data extraction and how to work the virtual machine by downloading & reading the GearGadget Information Sheet:

GearGadget Instruction Sheet Download

Research: Forensic Inspection of Sensitive User Data & Artifacts from Smartwatch Wearable Devices

The GearGadget data extraction tool is a result of research performed by Nicole Odom, a graduate student pursuing her Master of Science with an emphasis in Digital Forensics. The project, under the mentorship of Prof. Josh Brunty, sought to provide an enhanced understanding of how smartwatch wearable devices with cellular network capability interact with companion mobile phones and where sensitive user data and forensic artifacts are stored, both through utilization as a standalone and connected device. Through this research, a methodology for the forensically sound acquisition of data from a standalone wearable device was established, in order that analysts and researchers may most efficiently utilize their time and effort during investigations. This work was presented at the 71st Annual American Academy of Forensic Sciences meeting in February 2019, the Techno Forensics & Digital Forensics Conference in June 2019, & the Open Source Digital Forensics Conference (OSDFcon) in October 2019.



This research was also a published paper in the November, 2019 edition of the Journal of Forensic Sciences (JFS). The full version of this paper can be found by clicking here

The Research Poster resulting from this research can be found by clicking here



OSDFCon 2019 slides can be found by clicking here