Robust unobtrusive fall detection using infrared array sensors
As the world's aging population grows, fall is becoming a major problem in public health. It is one of the most vital risk to the elderly. Many technology based fall detection systems have been developed in recent years with hardware ranging from wearable devices to ambience sensors and video c...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/89600 http://hdl.handle.net/10220/47049 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | As the world's aging population grows, fall is becoming a major problem in public health. It is one of the most vital risk to the elderly. Many technology based fall detection systems have been developed in recent years with hardware ranging from wearable devices to ambience sensors and video cameras. Several machine learning based fall detection classifiers have been developed to process sensor data with various degrees of success. In this paper, we present a fall detection system using infrared array sensors with several deep learning methods, including long-short-term-memory and gated recurrent unit models. Evaluated with fall data collected in two different sets of configurations, we show that our approach gives significant improvement over existing works using the same infrared array sensor. |
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