Sitting is the most common status of modern human beings. Many people are sitting with bad posture which may lead to postural pain. Especially for people with short term-disabilities, sitting in the right posture is very important. In this research, we propose a posture recognition system on an office chair that can categorize different health-related sitting postures to prevent harm from bad sitting postures. The smart chair system consists of an array of five flex sensors integrated into an FPGA board. The output of the system is the classification result of the sitting posture. In this paper, several health-related sitting postures are selected. The sitting postures are: 1-sit straight; 2-left recline; 3-right recline; 4-lounge;5-lean backward; 6-cross left leg; 7-cross right leg. The proposed reconfigurable framework will be integrated as part of smart ambient assisted living with user-centered health monitoring system.
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A Smart Chair Sitting Posture Recognition System using Flex Sensors and FPGA Implemented Artificial Neural Network
Sitting is the most common status of modern human beings. Some sitting postures may bring health issues. To prevent the harm from bad sitting postures, a local sitting posture recognition system is desired with low power consumption and low computing overhead. The system should also provide good user experience with accuracy and privacy. This paper reports a novel posture recognition system on an office chair that can categorize seven different health-related sitting postures. The system uses six flex sensors, an Analog to Digital Converter (ADC) board and a Machine Learning algorithm of a two-layer Artificial Neural Network (ANN) implemented on a Spartan-6 Field Programmable Gate Array (FPGA). The system achieves 97.78% accuracy with a floating-point evaluation and 97.43% accuracy with the 9-bit fixed-point implementation. The ADC control logic and the ANN are constructed with a maximum propagation delay of 8.714 ns. The dynamic power consumption is 7.35 mW when the sampling rate is 5 Sample/second with the clock frequency of 5 MHz.
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- Award ID(s):
- 1652944
- NSF-PAR ID:
- 10138768
- Date Published:
- Journal Name:
- IEEE sensors journal
- ISSN:
- 1530-437X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JSEN.2020.2980207
