Enhancing Kitchen Activity Recognition: A Benchmark Study of the Rostock KTA Dataset

With the global population aging, the demand for technologies facilitating independent living, especially for those with cognitive impairments, is increasing. This paper addresses this need by conducting a comprehensive evaluation of the Rostock Kitchen Task Assessment dataset, a pivotal resource in kitchen task activity recognition. Our study begins with an in-depth introduction, emphasizing the increasing prevalence of neurodegenerative disorders and the crucial role of assistive technologies. Our contributions encompass a systematic literature review, design and implementation of a working prototype of our envisioned system, refinement of the Rostock Kitchen Task Assessment dataset, creation of a semantically annotated dataset, extraction of statistical features, comparative analysis, and rigorous model performance assessment. The core of our work is the thorough evaluation and benchmarking of different activity recognition approaches using the aforementioned Rostock Kitchen Task Assessment dataset. Our experimental results demonstrate that despite encountering an imbalance problem in the dataset, the fusion of the Hidden Markov Model and Random Forest leads to superior results, achieving a weighted-averaged $F_{1}$ -score of 74.10% for all available activities and 81.40% for the most common actions in the Rostock Kitchen Task Assessment dataset. Moreover, through systematic analysis, we identify strengths and suggest potential refinements, thereby advancing the field of kitchen activity recognition. This offers valuable insights for researchers and practitioners in assistive and remote care technologies.