Explainable IMU Anomaly Detection: Impact of Window Size on Machine Learning Performance under Noise Injection Açiklanabilir IMU Anomali Tespiti: Gürültü Enjeksiyonu Altinda Makine Ögrenmesi Performansina Pencere Boyutunun Etkisi
34th Signal Processing and Communications Applications Conference, SIU 2026, İstanbul, Türkiye, 7 - 10 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu71813.2026.11636468
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: anomaly detection, explainable artificial intelligence, IMU sensors, machine learning, window size analysis
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
In this study, anomaly detection is investigated under controlled noise injection using IMU sensor data collected from a mobile robot. The collected time-series data is processed using a windowing approach, and machine learning models are developed based on extracted features. Logistic regression and random forest algorithms are employed for anomaly detection, and SHAP is used to enhance model interpretability. In addition, the impact of different window sizes on model performance is analyzed. Experimental results indicate that window size has a significant impact on model performance.