Deep Recurrent Neural Network for Mobile Human Activity Recognition with High Throughput

Masaya Inoue, Sozo Inoue, Takeshi Nishida,
Journal of Artificial Life and Robotics
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 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.

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