An Intelligent Human Activity Recognition Method with Incremental Learning Capability for Bedridden Patients

Human activity recognition (HAR) is now valuable for bedridden patients to prevent falling, bedsore or other dangerous situation. This work proposes an intelligent broad learning system (BLS) recognition method based on the random vector functional-link neural network (RVFLNN) to identify the actions of bedridden patients. And the actions cover six types, including turning over to left, turning over to right, sitting up, lying down, stretching out for something and exiting from the bed. With the data collected from four pressure sensors that installed at four corners of an intelligent nursing bed, first, some pivotal preprocessing such as median filtering and down sampling are adopted to make a good performance. Then sparse auto encoder (SAE) is adopted for feature extraction. Finally, the RVFLNN is used for classification. Besides, for both new samples and new categories, the proposed method offers an incremental learning ability that can easily update the model with no need of model retaining. Compared with the convolutional neural network (CNN), the proposed method has superiority in training time while the accuracy is guaranteed.