WristSense: A Wrist-Wear Dataset for Identifying Aggressive Tendencies

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.