GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces
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| Format: | Preprint |
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2023
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| _version_ | 1866916597613985792 |
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| author | Yuan, Sun Navid, Salami Pargoo Jorge, Ortiz |
| author_facet | Yuan, Sun Navid, Salami Pargoo Jorge, Ortiz |
| contents | We introduce GeXSe (Generative Explanatory Sensor System), a novel framework designed to extract interpretable sensor-based and vision domain features from non-invasive smart space sensors. We combine these to provide a comprehensive explanation of sensor-activation patterns in activity recognition tasks. This system leverages advanced machine learning architectures, including transformer blocks, Fast Fourier Convolution (FFC), and diffusion models, to provide a more detailed understanding of sensor-based human activity data. A standout feature of GeXSe is our unique Multi-Layer Perceptron (MLP) with linear, ReLU, and normalization layers, specially devised for optimal performance on small datasets. It also yields meaningful activation maps to explain sensor-based activation patterns. The standard approach is based on a CNN model, which our MLP model outperforms.GeXSe offers two types of explanations: sensor-based activation maps and visual domain explanations using short videos. These methods offer a comprehensive interpretation of the output from non-interpretable sensor data, thereby augmenting the interpretability of our model. Utilizing the Frechet Inception Distance (FID) for evaluation, it outperforms established methods, improving baseline performance by about 6\%. GeXSe also achieves a high F1 score of up to 0.85, demonstrating precision, recall, and noise resistance, marking significant progress in reliable and explainable smart space sensing systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_15857 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces Yuan, Sun Navid, Salami Pargoo Jorge, Ortiz Signal Processing I.5.4 We introduce GeXSe (Generative Explanatory Sensor System), a novel framework designed to extract interpretable sensor-based and vision domain features from non-invasive smart space sensors. We combine these to provide a comprehensive explanation of sensor-activation patterns in activity recognition tasks. This system leverages advanced machine learning architectures, including transformer blocks, Fast Fourier Convolution (FFC), and diffusion models, to provide a more detailed understanding of sensor-based human activity data. A standout feature of GeXSe is our unique Multi-Layer Perceptron (MLP) with linear, ReLU, and normalization layers, specially devised for optimal performance on small datasets. It also yields meaningful activation maps to explain sensor-based activation patterns. The standard approach is based on a CNN model, which our MLP model outperforms.GeXSe offers two types of explanations: sensor-based activation maps and visual domain explanations using short videos. These methods offer a comprehensive interpretation of the output from non-interpretable sensor data, thereby augmenting the interpretability of our model. Utilizing the Frechet Inception Distance (FID) for evaluation, it outperforms established methods, improving baseline performance by about 6\%. GeXSe also achieves a high F1 score of up to 0.85, demonstrating precision, recall, and noise resistance, marking significant progress in reliable and explainable smart space sensing systems. |
| title | GeXSe (Generative Explanatory Sensor System): An Interpretable Deep Generative Model for Human Activity Recognition in Smart Spaces |
| topic | Signal Processing I.5.4 |
| url | https://arxiv.org/abs/2306.15857 |