We propose a new method of recognizing daily human activities based on a Deep Neural Network (DNN), using multimodal signals such as environmental sound and subject acceleration. We conduct recognition experiments to compare the proposed method to other methods such as a Support Vector Machine (SVM), using real-world data recorded continuously over 72 hours. Our proposed method achieved a frame accuracy rate of 85.5% and a sample accuracy rate of 91.7% when identifying nine different types of daily activities. Furthermore, the proposed method outperformed the SVM-based method when an additional "Other" activity category was included. Therefore, we demonstrate that DNNs are a robust method of daily activity recognition.
['Tomoki Hayashi', 'Masafumi Nishida', 'Norihide Kitaoka', 'Kazuya Takeda']
Daily activity recognition based on DNN using environmental sound and acceleration signals