A Dataset for Activity Recognition in an Unmodified Kitchen using Smart-Watch Accelerometers

Activity recognition from smart devices and wearable sensors is an active area of research due to the widespread adoption of smart devices and the benefits it provide for supporting people in their daily lives. Many of the available datasets for fine-grained primitive activity recognition focus on locomotion or sports activities with less emphasis on real-world day-to-day behavior. This paper presents a new dataset for activity recognition in a realistic unmodified kitchen environment. Data was collected using only smart-watches from 10 lay participants while they prepared food in an unmodified rented kitchen. The paper also providing baseline performance measures for different classifiers on this dataset. Moreover, a deep feature learning system and more traditional statistical features based approaches are compared. This analysis shows that - for all evaluation criteria - data-driven feature learning allows the classifier to achieve best performance compared with hand-crafted features.