The Effect of Activity Granularity on A Kitchen Activity Recognition System
Majid Ghosian Moghaddam, Ali Asghar Nazari Shirehjini, Shervin Shirmohammadi · 2024
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.