Forensic Analysis of Biometric Wearable Devices in Sports: Ensuring Security and Data Integrity in Consumer Electronics

The proliferation of biometric wearable devices in sports has revolutionized the way athletes monitor their performance, health, and overall well-being. These devices generate vast volumes of behavioral and physiological data, providing valuable insights into performance during game day, training, and recovery. However, the increasing reliance on these devices raises significant concerns regarding data security, privacy, and integrity, especially within distributed consumer electronics environments. To address these challenges, this research proposes a forensic analysis approach that leverages Deep Learning (DL) techniques to enhance the security and integrity of biometric data. The approach integrates a Scalable Locality-Sensitive Hashing (SLSH) mechanism with an Adaptive Dandelion Optimizer-driven Self-Attention-based Recurrent Neural Network (ADO-SARNN) model to detect anomalies, identify potential security breaches, and verify data authenticity in real-time. Biometric wearable devices collect extensive physiological and behavioral data from athletes and sports enthusiasts. The data undergoes Gaussian filtering to remove signal distortions and normalize the information. SLSH is employed for efficient Anomaly Detection (AD) and data integrity verification in biometric signals, while the ADO-SARNN model ensures the verification of data authenticity and performs forensic analysis by detecting manipulated inputs. The proposed model effectively distinguishes between legitimate and tampered biometric data, ensuring robust security and forensic traceability. Performance evaluation achieved a higher accuracy of 98.2%, precision of 97.4%, ROC curve of 0.98, and lower error rate of the FAR (0.00145), FRR (0.00135), and ERR (0.00117). Extensive experiments conducted on sports data platforms demonstrate that the proposed method significantly improves data integrity verification and privacy protection compared to traditional approaches. These findings underscore the effectiveness of DL-driven forensic analysis in securing wearable sports technology, marking a crucial step toward safeguarding consumer electronics within the sports industry.