Lightweight human activity recognition learning model

In the past few decades, smartphone-based human activity recognition research has played an important role in many fields, including smart buildings, healthcare, and the military. However, the CPU and storage space of smartphones are very limited, so developing a lightweight human activity recognition learning model has become a research focus and hot spot in this field. To address the abovementioned problems, this paper proposed a lightweight human activity recognition learning model based on the nearest neighbor component analysis (NCA), L₂ regularization, and stochastic configuration networks (SCNs). In the proposed model, aiming first at the problem of high dimension and poor separability exhibited by the human activity data, NCA was used to select a subset of highly relevant data from the dataset to improve the lightness of calculation using the learning algorithm in the modeling process and recognition accuracy of the established model. Second, to prevent the occurrence of overfitting when there are too many hidden layer nodes in SCNs, the L₂ regularization method was adopted to enhance the generalization ability of SCNs. At the same time, the method of using the supervision mechanism to restrict the generation of hidden layer parameters greatly improved the lightness of the SCNs model. Finally, the proposed learning model and other learning models were verified experimentally on the UCI human activity recognition dataset. Experimental results show that compared with SCNs, the proposed L₂−SCNs model reduces the lightness of the number of parameters by 20% and helps improve the accuracy of the model. The introduction of the NCA method has greatly facilitated the recognition accuracy and lightness (modeling time) of the L₂−SCNs model, increasing by 3.41% and 70.24%, respectively. Moreover, compared with other state-of-the-art models, such as the support vector machine and long short-term memory network, the proposed model achieves the best recognition accuracy of 97.48% in the shortest time. To sum up, the model proposed herein is a lightweight human activity recognition model with exceptional recognition accuracy and a fast modeling speed.