Details
| Original language | English |
|---|---|
| Title of host publication | Medical Imaging 2018 |
| Subtitle of host publication | Imaging Informatics for Healthcare, Research, and Applications |
| Editors | Po-Hao Chen, Jianguo Zhang |
| Publisher | SPIE |
| Number of pages | 6 |
| ISBN (electronic) | 9781510616479 |
| Publication status | Published - 6 Mar 2018 |
| Event | Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications - Houston, United States Duration: 13 Feb 2018 → 15 Feb 2018 |
Publication series
| Name | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
|---|---|
| Volume | 10579 |
| ISSN (Print) | 1605-7422 |
Abstract
The number of health-care associated infections is increasing worldwide. Hand hygiene has been identified as one of the most crucial measures to prevent bacteria from spreading. However, compliance with recommended procedures for hand hygiene is generally poor, even in modern, industrialized regions. We present an optical assistance system for monitoring the hygienic hand disinfection procedure which is based on machine learning. Firstly, each hand and underarm of a person is detected in a down-sampled 96 px x 96 px depth video stream by pixelwise classification using a fully convolutional network. To gather the required amount of training data, we present a novel approach in automatically labeling recorded data using colored gloves and a color video stream that is registered to the depth stream. The colored gloves are used to segment the depth data in the training phase. During inference, the colored gloves are not required. The system detects and separates detailed hand parts of interacting, self-occluded hands within the observation zone of the sensor. Based on the location of the segmented hands, a full resolution region of interest (ROI) is cropped. A second deep neural network classifies the ROI into ten separate process steps (gestures), with nine of them based on the recommended hand disinfection procedure of the World Health Organization, and an additional error class. The combined system is cross-validated with 21 subjects and predicts with an accuracy of 93.37% (± 2.67%) which gesture is currently executed. The feedback is provided with 30 frames per second.
Keywords
- Gesture recognition, Hand hygiene, Hand tracking, Machine learning, Segmentation
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Materials Science(all)
- Biomaterials
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Medicine(all)
- Radiology Nuclear Medicine and imaging
Cite this
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- Apa
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- BibTeX
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Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. ed. / Po-Hao Chen; Jianguo Zhang. SPIE, 2018. 1057914 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10579).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Hand hygiene monitoring based on segmentation of interacting hands with convolutional networks
AU - Dietz, Armin
AU - Pösch, Andreas
AU - Reithmeier, Eduard
PY - 2018/3/6
Y1 - 2018/3/6
N2 - The number of health-care associated infections is increasing worldwide. Hand hygiene has been identified as one of the most crucial measures to prevent bacteria from spreading. However, compliance with recommended procedures for hand hygiene is generally poor, even in modern, industrialized regions. We present an optical assistance system for monitoring the hygienic hand disinfection procedure which is based on machine learning. Firstly, each hand and underarm of a person is detected in a down-sampled 96 px x 96 px depth video stream by pixelwise classification using a fully convolutional network. To gather the required amount of training data, we present a novel approach in automatically labeling recorded data using colored gloves and a color video stream that is registered to the depth stream. The colored gloves are used to segment the depth data in the training phase. During inference, the colored gloves are not required. The system detects and separates detailed hand parts of interacting, self-occluded hands within the observation zone of the sensor. Based on the location of the segmented hands, a full resolution region of interest (ROI) is cropped. A second deep neural network classifies the ROI into ten separate process steps (gestures), with nine of them based on the recommended hand disinfection procedure of the World Health Organization, and an additional error class. The combined system is cross-validated with 21 subjects and predicts with an accuracy of 93.37% (± 2.67%) which gesture is currently executed. The feedback is provided with 30 frames per second.
AB - The number of health-care associated infections is increasing worldwide. Hand hygiene has been identified as one of the most crucial measures to prevent bacteria from spreading. However, compliance with recommended procedures for hand hygiene is generally poor, even in modern, industrialized regions. We present an optical assistance system for monitoring the hygienic hand disinfection procedure which is based on machine learning. Firstly, each hand and underarm of a person is detected in a down-sampled 96 px x 96 px depth video stream by pixelwise classification using a fully convolutional network. To gather the required amount of training data, we present a novel approach in automatically labeling recorded data using colored gloves and a color video stream that is registered to the depth stream. The colored gloves are used to segment the depth data in the training phase. During inference, the colored gloves are not required. The system detects and separates detailed hand parts of interacting, self-occluded hands within the observation zone of the sensor. Based on the location of the segmented hands, a full resolution region of interest (ROI) is cropped. A second deep neural network classifies the ROI into ten separate process steps (gestures), with nine of them based on the recommended hand disinfection procedure of the World Health Organization, and an additional error class. The combined system is cross-validated with 21 subjects and predicts with an accuracy of 93.37% (± 2.67%) which gesture is currently executed. The feedback is provided with 30 frames per second.
KW - Gesture recognition
KW - Hand hygiene
KW - Hand tracking
KW - Machine learning
KW - Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85047721885&partnerID=8YFLogxK
U2 - 10.1117/12.2294047
DO - 10.1117/12.2294047
M3 - Conference contribution
AN - SCOPUS:85047721885
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2018
A2 - Chen, Po-Hao
A2 - Zhang, Jianguo
PB - SPIE
T2 - Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
Y2 - 13 February 2018 through 15 February 2018
ER -