Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition

• Autor(a) principal: Bento, Nuno
• Data de Publicação: 2022
• Outros Autores: Rebelo, Joana, Barandas, Marília, Carreiro, André V., Campagner, Andrea, Cabitza, Federico, Gamboa, Hugo
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/145396
• topic: accelerometer; deep learning; domain generalization; human activity recognition; Analytical Chemistry; Information Systems; Biochemistry; Atomic and Molecular Physics, and Optics; Instrumentation; Electrical and Electronic Engineering

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are not capable of generalizing across different domains (i.e., subjects, devices, or datasets) with acceptable performance. This lack of generalization hinders the applicability of these models in real-world environments. As deep neural networks are becoming increasingly popular in recent work, there is a need for an explicit comparison between handcrafted and deep representations in Out-of-Distribution (OOD) settings. This paper compares both approaches in multiple domains using homogenized public datasets. First, we compare several metrics to validate three different OOD settings. In our main experiments, we then verify that even though deep learning initially outperforms models with handcrafted features, the situation is reversed as the distance from the training distribution increases. These findings support the hypothesis that handcrafted features may generalize better across specific domains.