Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion
Guide dogs play a crucial role in enhancing independence and mobility for people with visual impairment, offering invaluable assistance in navigating daily tasks and environments. However, the extensive training required for these dogs is costly, resulting in a limited availability that does not mee...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2076-2615/14/23/3403 |
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| author | Devon Martin David L. Roberts Alper Bozkurt |
| author_facet | Devon Martin David L. Roberts Alper Bozkurt |
| author_sort | Devon Martin |
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| description | Guide dogs play a crucial role in enhancing independence and mobility for people with visual impairment, offering invaluable assistance in navigating daily tasks and environments. However, the extensive training required for these dogs is costly, resulting in a limited availability that does not meet the high demand for such skilled working animals. Towards optimizing the training process and to better understand the challenges these guide dogs may be experiencing in the field, we have created a multi-sensor smart collar system. In this study, we developed and compared two supervised machine learning methods to analyze the data acquired from these sensors. We found that the Convolutional Long Short-Term Memory (Conv-LSTM) network worked much more efficiently on subsampled data and Kernel Principal Component Analysis (KPCA) on interpolated data. Each attained approximately 40% accuracy on a 10-state system. Not needing training, KPCA is a much faster method, but not as efficient with larger datasets. Among various sensors on the collar system, we observed that the inertial measurement units account for the vast majority of predictability, and that the addition of environmental acoustic sensing data slightly improved performance in most datasets. We also created a lexicon of data patterns using an unsupervised autoencoder. We present several regions of relatively higher density in the latent variable space that correspond to more common patterns and our attempt to visualize these patterns. In this preliminary effort, we found that several test states could be combined into larger superstates to simplify the testing procedures. Additionally, environmental sensor data did not carry much weight, as air conditioning units maintained the testing room at standard conditions. |
| format | Article |
| id | doaj-art-5ec8799d7ec94f668e6fa14a0783d577 |
| institution | Kabale University |
| issn | 2076-2615 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Animals |
| spelling | doaj-art-5ec8799d7ec94f668e6fa14a0783d5772024-12-13T16:21:04ZengMDPI AGAnimals2076-26152024-11-011423340310.3390/ani14233403Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data FusionDevon Martin0David L. Roberts1Alper Bozkurt2Department of Electrical Engineering, North Carolina State University, 890 Oval Dr., Raleigh, NC 27695, USADepartment of Computer Science, North Carolina State University, Campus Box 8206, 890 Oval Drive, Raleigh, NC 27695, USADepartment of Electrical Engineering, North Carolina State University, 890 Oval Dr., Raleigh, NC 27695, USAGuide dogs play a crucial role in enhancing independence and mobility for people with visual impairment, offering invaluable assistance in navigating daily tasks and environments. However, the extensive training required for these dogs is costly, resulting in a limited availability that does not meet the high demand for such skilled working animals. Towards optimizing the training process and to better understand the challenges these guide dogs may be experiencing in the field, we have created a multi-sensor smart collar system. In this study, we developed and compared two supervised machine learning methods to analyze the data acquired from these sensors. We found that the Convolutional Long Short-Term Memory (Conv-LSTM) network worked much more efficiently on subsampled data and Kernel Principal Component Analysis (KPCA) on interpolated data. Each attained approximately 40% accuracy on a 10-state system. Not needing training, KPCA is a much faster method, but not as efficient with larger datasets. Among various sensors on the collar system, we observed that the inertial measurement units account for the vast majority of predictability, and that the addition of environmental acoustic sensing data slightly improved performance in most datasets. We also created a lexicon of data patterns using an unsupervised autoencoder. We present several regions of relatively higher density in the latent variable space that correspond to more common patterns and our attempt to visualize these patterns. In this preliminary effort, we found that several test states could be combined into larger superstates to simplify the testing procedures. Additionally, environmental sensor data did not carry much weight, as air conditioning units maintained the testing room at standard conditions.https://www.mdpi.com/2076-2615/14/23/3403Conv-LSTMKPCAautoencodermanifold learningpattern recognitionguide dogs |
| spellingShingle | Devon Martin David L. Roberts Alper Bozkurt Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion Animals Conv-LSTM KPCA autoencoder manifold learning pattern recognition guide dogs |
| title | Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion |
| title_full | Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion |
| title_fullStr | Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion |
| title_full_unstemmed | Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion |
| title_short | Preliminary Analysis of Collar Sensors for Guide Dog Training Using Convolutional Long Short-Term Memory, Kernel Principal Component Analysis and Multi-Sensor Data Fusion |
| title_sort | preliminary analysis of collar sensors for guide dog training using convolutional long short term memory kernel principal component analysis and multi sensor data fusion |
| topic | Conv-LSTM KPCA autoencoder manifold learning pattern recognition guide dogs |
| url | https://www.mdpi.com/2076-2615/14/23/3403 |
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