Sequential human activity recognition based on deep convolutional network and Extreme Learning Machine using wearable sensor

• 1、University of Science and Technology Beijing School of Computer and Communication Engineering,Beijing 100083
• 2、 Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing,Beijing 100083

Abstract：Human activity recognition (HAR) problems have traditionally been solved by using engineered features obtained by heuristic methods. These methods ignore the time information of the streaming sensor data and cannot achieve sequential human activity recognition. With the use of traditional statistical learning methods, results could easily plunge into the local minimum other than the global optimal, and also face the problem of low efficiency. Therefore, we propose a hybrid deep framework based on convolution operations, LSTM recurrent units and ELM classifier, the advantages are: 1) does not require expert knowledge in extracting features; 2) models temporal dynamics of features; 3) it is more suitable to classify the extracted features and shortens the runtime. All of these unique advantages make it superior to other HAR algorithms. We evaluate our framework on OPPORTUNITY dataset which has been used in OPPORTUNITY challenge. Results show that our proposed method outperforms deep non-recurrent networks by 6%; outperforming the previous reported best result by 8%. When compared with neural network using BP algorithm, testing time reduced by 38%.

Keywords： human activity recognition；convolutional neural network (CNN)；LSTM；ELM