Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput
This work addresses mobile human activity recognition with improved speed and accuracy, though it is incremental as it applies a known DRNN approach to this domain.
The paper tackles human activity recognition from raw accelerometer data using a deep recurrent neural network (DRNN) to achieve high throughput, resulting in a maximum recognition rate of 95.42% on single-activity test data and reducing recognition time to 1.347 ms compared to 11.031 ms for traditional methods.
In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The "high throughput" refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated by using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 [ms], while the best traditional method required 11.031 [ms] which includes 11.027 [ms] for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN.