KinRes
| Autor: | Jürgen Ziegler, Kaveh Bakhtiyari, Hafizah Husain |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Signal processing
Computer science business.industry Noise (signal processing) Noise reduction 020207 software engineering 02 engineering and technology Respiratory monitoring Filter (signal processing) Signal 030218 nuclear medicine & medical imaging Informatik 03 medical and health sciences 0302 clinical medicine Position (vector) 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence business Greedy algorithm |
| Zdroj: | PervasiveHealth |
| DOI: | 10.1145/3154862.3154896 |
| Popis: | This paper proposes a novel reliable solution, named KinRes, to extract contactless respiratory signal via an IR-3D Depth sensor (Microsoft Kinect 2) on human subjects interacting with a computer. The depth sensor is very sensitive to the minor changes so that the body movements impose noise in the depth values. Previous studies on contactless respiratory concentrated solely on the still laid subjects on a surface to minimize the possible artifacts. To overcome these limitations, we low-pass filter the extracted signal. Then, a greedy self-correction algorithm is developed to correct the false detected peaks & troughs. The processed signal is validated with a simultaneous signal from a respiratory belt. This framework improved the accuracy of the signal by 24% for the subjects in a normal sitting position. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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