### INTRODUCTION

### MATERIALS AND METHODS

### RNN Structure

*W*.

*x*is the input value in the current time step

_{t}*t*.

*s*is the hidden layer that functions as memory and is calculated by

_{t}*s*

_{t-1}(previous time step

*t*) and

*x*as shown in equation (1). In this case, the activation function

_{t}*f*is a nonlinear function, for which

*tanh*or

*ReLU*is used.

*s*

_{t-1}is initialized to 0 to calculate the first hidden state. Since the past output is used again for weight calculation, the RNN can remember past information. In addition, for RNNs, unlike the back-propagation algorithm of the general artificial neural network, the shared weight is updated at each time step through a back-propagation algorithm.

*z*is the output value at time step

_{t}*t*and is calculated by equation (2).

*U*,

*V*, and

*W*are shared at each time step.

### Application of LSTM

### Application of Clinical Biomarkers for Urination

### RESULTS

### Experimental Environment

*x-*,

*y-*, and

*z-*axes were stored in the smartphone as time series data. The data were formatted again in XML (extensible markup language), and divided into learning data and test data at a ratio of 3:7.

### Features of the Urination Data

### Results of Urination Recognition

*x*,

*y*, and

*z*represent the structures to which the accelerometer and gyro sensor signal data are input. This is the process of calculating the node continuity and the status of the current time point (

*t*) and the next time point (

*t*+1). The following Fig. 7 shows the entire network structure over time. Processing the target network involved 2 major steps. The urination stage recognized in the network through the input of accelerometer data from step 1 and the urination step recognized from the gyro data in step 2 were compared and a weight of 0.5 was assigned to determine the final urination step. As shown in Fig. 7, the presence of urinary activity and recognized urinary activity in relation to time are processed by the proposed network structure.

#### Step 1: recognition of urinary activity

TP: a match for urination is determined to correctly reflect a real urination event.

FP: a match for urination is determined not to reflect a real urination event.

TN: a nonmatch for urination is determined to correctly reflect the absence of urination.

FN: a nonmatch for urination is determined to correspond to a real urination event.