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Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method

In this paper, we report our improvement on the prediction accuracy of pre-impact fall detection by applying a learning-based method on the real-time data from an IMU (inertial measurement unit)-sensor mounted on the waist, making it possible to achieve a high accuracy on a wearable device with the...

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Detalles Bibliográficos
Autores principales: Shi, Jingyi, Chen, Diansheng, Wang, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506847/
https://www.ncbi.nlm.nih.gov/pubmed/32842652
http://dx.doi.org/10.3390/s20174750
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author Shi, Jingyi
Chen, Diansheng
Wang, Min
author_facet Shi, Jingyi
Chen, Diansheng
Wang, Min
author_sort Shi, Jingyi
collection PubMed
description In this paper, we report our improvement on the prediction accuracy of pre-impact fall detection by applying a learning-based method on the real-time data from an IMU (inertial measurement unit)-sensor mounted on the waist, making it possible to achieve a high accuracy on a wearable device with the extracted features. Using the fixed threshold method is difficult for achieving satisfactory detection accuracy, due to various characteristics and behaviors in the movement of different individuals. In contrast, one could realize high-accuracy detection with machine learning-based methods, but it is difficult to apply them in the wearable devices due to the high hardware requirement. Our method merges the two methods above. We build a convolutional neural network (CNN) with a class activation mapping (CAM) method, which could highlight the class-specific region in the data and obtain a hot map of the fall data. After training on the MobiAct dataset, the model could achieve high-accuracy detection (95.55%) and obtain the region with high contributions to the classification. Then, we manually extract effective features and characteristics of this region and form our special threshold method, achieving pre-impact fall detection in real-world data. Consequently, our method achieves accuracy of 95.33% and a detection time of within 400 ms.
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spelling pubmed-75068472020-09-26 Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method Shi, Jingyi Chen, Diansheng Wang, Min Sensors (Basel) Letter In this paper, we report our improvement on the prediction accuracy of pre-impact fall detection by applying a learning-based method on the real-time data from an IMU (inertial measurement unit)-sensor mounted on the waist, making it possible to achieve a high accuracy on a wearable device with the extracted features. Using the fixed threshold method is difficult for achieving satisfactory detection accuracy, due to various characteristics and behaviors in the movement of different individuals. In contrast, one could realize high-accuracy detection with machine learning-based methods, but it is difficult to apply them in the wearable devices due to the high hardware requirement. Our method merges the two methods above. We build a convolutional neural network (CNN) with a class activation mapping (CAM) method, which could highlight the class-specific region in the data and obtain a hot map of the fall data. After training on the MobiAct dataset, the model could achieve high-accuracy detection (95.55%) and obtain the region with high contributions to the classification. Then, we manually extract effective features and characteristics of this region and form our special threshold method, achieving pre-impact fall detection in real-world data. Consequently, our method achieves accuracy of 95.33% and a detection time of within 400 ms. MDPI 2020-08-22 /pmc/articles/PMC7506847/ /pubmed/32842652 http://dx.doi.org/10.3390/s20174750 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Shi, Jingyi
Chen, Diansheng
Wang, Min
Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method
title Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method
title_full Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method
title_fullStr Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method
title_full_unstemmed Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method
title_short Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method
title_sort pre-impact fall detection with cnn-based class activation mapping method
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506847/
https://www.ncbi.nlm.nih.gov/pubmed/32842652
http://dx.doi.org/10.3390/s20174750
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