A Stacked Human Activity Recognition Model Based on Parallel Recurrent Network and Time Series Evidence Theory

Abstract

As the foundation of Posture Analysis, recognizing human activity accurately in real time assists in using machines to intellectualize living condition and monitor health status. In this paper, we focus on recognition based on raw time series data, which are continuously sampled by wearable sensors, and a fine-grained evidence reasoning approach has been proposed to produce a timely and reliable result. First, the basic time unit of input data is selected by finding a tradeoff between accuracy and time cost. Then, the approach uses Long Short Term Memory to extract features and project raw multidimensional data into probability assignments, followed by trainable evidence combination and inference network that reduce uncertainly to improve the classification accuracy. Experiments validate the effectiveness of fine granularity and evidence reasoning while the final results indicate that the recognition accuracy of this approach can reach 96.4% with no additional complexity in training.

LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes

“…These researchers found that recognition efficiency could be enhanced using the Stacked LSTM network to repeatedly extract temporal features. Zhang et al [ 30 ] proposed a Stacked HAR model based on an LSTM network. The findings revealed that with no extra difficulty in training, the Stacked LSTM network could enhance recognition accuracy.…”