Comparison of Deep Learing Algorithms for Indoor Monitoring using Bioelectric Potential of Living Plants

This study aims to develop a monitoring system for an indoor space. We are investigating to use the bioelectric potential of living plants as a human sensor system in an indoor environment. The system utilizes a change of the bioelectric potential to estimate a resident’s location in a room. To build an estimation model, a lot of the bioelectric potential data are collected and processed by a machine learning method. We have studied to build the estimation model using a convolutional neural network. However, recently, there are many applications that utilize Long-Short Term Memory method for a time sequential data, and they obtained a good result successfully. Therefore, in this study we applied LSTM for the bioelectric potential data and investigate the availability of CNN and LSTM to estimate the location with the bioelectric potential. As the result of classification experiments with the model trained with collected bioelectric data, we obtained that CNN is better than LSTM for this problem. However, we need to improve the accuracy by adjusting parameters in future.