Evaluation of deep learning model for human activity recognition

Recognizing a person's physical activity with certainty makes an important aspect of intelligent computing. Modern smart devices are equipped with powerful sensors that are suitable for sensor-based human activity recognition (AR) task. Traditional approaches to human activity recognition has made significant progress but most of those methods rely upon manual feature extraction. The design and selection of relevant features is the most challenging task in sensor-based human AR problem. Using manually extracted features for this task hinders the generalization of performance and these handcrafted features are also incapable of handling similar and complex activities with certainty. In this paper, we propose a deep learning based method for human activity recognition problem. The method uses convolutional neural networks to automatically extract features from raw sensor data and classify six basic human activities. Furthermore, transfer learning is used to reduce the computational cost involved in training the model from scratch for a new user. The model uses the labelled information from supervised learning, to mutually enhance the feature extraction and classification. Experiments carried on benchmark dataset verified the strong advantage of proposed method over the traditional human AR algorithms such as Random Forest (RF) and multiclass Support Vector Machine (SVM).