Physics Sensor Based Deep Learning Fall Detection System
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909134606041088 |
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| author | Qu, Zeyuan Huang, Tiange Ji, Yuxin Li, Yongjun |
| author_facet | Qu, Zeyuan Huang, Tiange Ji, Yuxin Li, Yongjun |
| contents | Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshold based classification methods. In this paper, we build a complete system named TSFallDetect including data receiving device based on embedded sensor, mobile deep-learning model deploying platform, and a simple server, which will be used to gather models and data for future expansion. On the other hand, we exploit the sequential deep-learning methods to address this falling motion prediction problem based on data collected by inertial and film pressure sensors. We make a empirical study based on existing datasets and our datasets collected from our system separately, which shows that the deep-learning model has more potential advantage than other traditional methods, and we proposed a new deep-learning model based on the time series data to predict the fall, and it may be superior to other sequential models in this particular field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06994 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics Sensor Based Deep Learning Fall Detection System Qu, Zeyuan Huang, Tiange Ji, Yuxin Li, Yongjun Signal Processing Artificial Intelligence Machine Learning Fall detection based on embedded sensor is a practical and popular research direction in recent years. In terms of a specific application: fall detection methods based upon physics sensors such as [gyroscope and accelerator] have been exploited using traditional hand crafted features and feed them in machine learning models like Markov chain or just threshold based classification methods. In this paper, we build a complete system named TSFallDetect including data receiving device based on embedded sensor, mobile deep-learning model deploying platform, and a simple server, which will be used to gather models and data for future expansion. On the other hand, we exploit the sequential deep-learning methods to address this falling motion prediction problem based on data collected by inertial and film pressure sensors. We make a empirical study based on existing datasets and our datasets collected from our system separately, which shows that the deep-learning model has more potential advantage than other traditional methods, and we proposed a new deep-learning model based on the time series data to predict the fall, and it may be superior to other sequential models in this particular field. |
| title | Physics Sensor Based Deep Learning Fall Detection System |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.06994 |