Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments
Abstract
:1. Introduction
- The work proposes an audio assistant app, to be developed and deployed on a smartphone, that helps VI people move independently in a complex building.
- To the best of the author’s knowledge, this work is the first to propose and recommend regression-based neural network training for the estimation of the position of a VI person moving in an indoor environment with a smartphone.
2. Background and Motivations for This Work
3. Characteristics of the Used Dataset
- X, Y, and Z-axis values of the accelerometer sensor;
- X, Y, and Z-axis values of the magnetometer;
- X, Y, and Z-axis values of the gyroscope;
- Roll, pitch, and azimuth values of the inertial sensor.
4. Deep Learning-Based Positioning
5. Setup of the Experiments and Analysis of the Results
5.1. Experimental Platform
5.2. Performance Metrics and Evaluation
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameter | Hyperparameter Values in Proposed Deep MLP |
---|---|
Software | Python, Keras, TensorFlow |
Training data | 29,436 |
Validation data | 7359 |
Epochs | 60 to 140 |
Batch size | 20, 40, 60, 80 |
Layers with Hidden neurons (with batch normalization) | 3 layers—128, 64 and 128 neurons 5 layers—256, 128, 64 and 128 and 256 neurons 7 layers—512, 256, 64, 128, 256 neurons |
Drop out rate | 0.2 to 0.8 |
Activation | Selu, elu, softplus, relu |
Optimizer | Adam, adamax, rmsprop, adagrad |
Loss function | MAE, MSE, RMSE |
Optimizer | MAE (m) | RMSE (m) | MSE (m) |
Adam | 0.71 | 1.30 | 1.70 |
Adamax | 0.84 | 1.35 | 1.81 |
Rmsprop | 1.04 | 1.84 | 3.39 |
Adagrad | 5.59 | 3.25 | 3.62 |
Activation | MAE (m) | RMSE (m) | MSE (m) |
relu | 1.24 | 2.61 | 6.84 |
softplus | 1.35 | 2.45 | 6.01 |
elu | 0.92 | 1.85 | 3.45 |
selu | 0.65 | 1.29 | 1.67 |
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Mahida, P.; Shahrestani, S.; Cheung, H. Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments. Sensors 2020, 20, 6238. https://doi.org/10.3390/s20216238
Mahida P, Shahrestani S, Cheung H. Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments. Sensors. 2020; 20(21):6238. https://doi.org/10.3390/s20216238
Chicago/Turabian StyleMahida, Payal, Seyed Shahrestani, and Hon Cheung. 2020. "Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments" Sensors 20, no. 21: 6238. https://doi.org/10.3390/s20216238
APA StyleMahida, P., Shahrestani, S., & Cheung, H. (2020). Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments. Sensors, 20(21), 6238. https://doi.org/10.3390/s20216238