Explaining One-Dimensional Convolutional Models in Human Activity Recognition and Biometric Identification Tasks

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5644
• Main Authors: Aquino, Gustavo, Costa, Marly G. F., Costa Filho, Cicero F. F.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.07.2022; MDPI
• Subjects: accelerometer data; Accelerometers; Biometric Identification; biometric user identification; Biometrics; Business metrics; convolutional neural networks; Databases, Factual; Deep learning; Exercise; grad-cam; Human Activities; human activity recognition; Humans; Machine learning; Neural networks; Neural Networks, Computer; Older people; Physical fitness; Sensors; Smartphones; Time series; Wearable computers; deep learning; biometric user identification; human activity recognition; explainable AI; convolutional neural networks; grad-cam; accelerometer data
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22155644

Due to wearables’ popularity, human activity recognition (HAR) plays a significant role in people’s routines. Many deep learning (DL) approaches have studied HAR to classify human activities. Previous studies employ two HAR validation approaches: subject-dependent (SD) and subject-independent (SI). Using accelerometer data, this paper shows how to generate visual explanations about the trained models’ decision making on both HAR and biometric user identification (BUI) tasks and the correlation between them. We adapted gradient-weighted class activation mapping (grad-CAM) to one-dimensional convolutional neural networks (CNN) architectures to produce visual explanations of HAR and BUI models. Our proposed networks achieved 0.978 and 0.755 accuracy, employing both SD and SI. The proposed BUI network achieved 0.937 average accuracy. We demonstrate that HAR’s high performance with SD comes not only from physical activity learning but also from learning an individual’s signature, as in BUI models. Our experiments show that CNN focuses on larger signal sections in BUI, while HAR focuses on smaller signal segments. We also use the grad-CAM technique to identify database bias problems, such as signal discontinuities. Combining explainable techniques with deep learning can help models design, avoid results overestimation, find bias problems, and improve generalization capability.