The Effect of Activity Granularity on A Kitchen Activity Recognition System

Kitchen activity recognition enables tailored advertising based on users' cooking habits and preferences, enhancing marketing relevance. Like any other human activity, the granularity of the kitchen activity that a sensor aims to recognize may affect the sensor's performance. This study aims to investigate the effect of activity granularity on the performance of a Wi-Fi-based human activity recognition (HAR) method in a kitchen context. For this purpose, first, we utilized an authentic kitchen with an ESP32 microcontroller as a WiFi transceiver and an iPhone 12 mini as a WiFi receiver. Then, we asked one user to perform three coarse-grained kitchen activities: filling an electric kettle with water and turning it on, stir-frying cubed potatoes, and taking several cans from the fridge and putting them on a cabinet. We gathered the Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) data from WiFi packets. Then, we designed and implemented a device-free HAR system by a combination of CSI and RSSI at the feature level and utilizing a 5-fold cross-validation to assess the performance of a voting-based hybrid classification method including support vector machine (SVM), linear discriminant analysis (LDA), and Gaussian Naïve Bayes (GNB). The proposed system achieved an average accuracy of 92.27% in the recognition of coarse-grained activities. We then repeated the same experiment for gathering the same data for three fine-grained activities: chopping, slicing, and French-fries cutting. The same HAR system achieved an average accuracy of 51.53%. The results indicate that a device-free HAR method that achieves a high recognition accuracy for coarse-grained activities cannot achieve a similarly high accuracy for fine-grained activities.