TY - GEN
T1 - WristSense
T2 - 20th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2024
AU - Almubairik, Norah Ahmed
AU - Khan, Fakhri Alam
AU - Mohammad, Rami Mustafa
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2024.
PY - 2024
Y1 - 2024
N2 - Datasets play a crucial role in digital forensics by providing valuable resources for in-depth analysis and insightful decision-making. This article introduces the WristSense dataset, which was developed to assist in predicting aggressive behavior during digital investigations through wrist-wear devices. The dataset comprises data collected from 40 participants who wore Huawei smartwatches for a period of 8 days each, resulting in a comprehensive collection spanning over three months. It includes recordings of smartwatch sensor data such as heart rate, biometrics (sleep patterns, blood oxygen levels, activity metrics), stress levels, and event timestamps. The data was extracted using the forensically sound tools MD-NEXT and MD-RED. The participants were engaged in a self-reported questionnaire to assess their aggression levels, which was the labeling process. Thus, digital forensics professionals can gain insights into behavioral patterns related to aggression, refine investigative techniques, and address challenges posed by emerging technologies. The WristSense dataset offers an opportunity to examine the relationship between wrist-wear device data and aggressive behavior, allowing for informed predictions. This comprehensive dataset contributes to advancing forensic practices, improving decision-making processes, and enhancing the effectiveness of digital forensic investigations.
AB - Datasets play a crucial role in digital forensics by providing valuable resources for in-depth analysis and insightful decision-making. This article introduces the WristSense dataset, which was developed to assist in predicting aggressive behavior during digital investigations through wrist-wear devices. The dataset comprises data collected from 40 participants who wore Huawei smartwatches for a period of 8 days each, resulting in a comprehensive collection spanning over three months. It includes recordings of smartwatch sensor data such as heart rate, biometrics (sleep patterns, blood oxygen levels, activity metrics), stress levels, and event timestamps. The data was extracted using the forensically sound tools MD-NEXT and MD-RED. The participants were engaged in a self-reported questionnaire to assess their aggression levels, which was the labeling process. Thus, digital forensics professionals can gain insights into behavioral patterns related to aggression, refine investigative techniques, and address challenges posed by emerging technologies. The WristSense dataset offers an opportunity to examine the relationship between wrist-wear device data and aggressive behavior, allowing for informed predictions. This comprehensive dataset contributes to advancing forensic practices, improving decision-making processes, and enhancing the effectiveness of digital forensic investigations.
KW - Aggressive behaviour prediction
KW - Digital Forensics
KW - Health data
KW - Wrist-wear Devices
KW - WristSense Dataset
UR - https://www.scopus.com/pages/publications/85197359189
U2 - 10.1007/978-3-031-63211-2_21
DO - 10.1007/978-3-031-63211-2_21
M3 - Conference contribution
AN - SCOPUS:85197359189
SN - 9783031632105
T3 - IFIP Advances in Information and Communication Technology
SP - 268
EP - 284
BT - Artificial Intelligence Applications and Innovations - 20th IFIP WG 12.5 International Conference, AIAI 2024, Proceedings
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - Papaleonidas, Antonios
A2 - Macintyre, John
A2 - Avlonitis, Markos
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 27 June 2024 through 30 June 2024
ER -