Excretion Detection System with Gas Sensor – Proposal and Verification of Algorithm Based on Time-Series Clustering
[abstFig src='/00290002/09.jpg' width='300' text='Standard Lifilm configuration' ] In this study, we propose an excretion detection system, Lifi, which does not require sensors inside diapers, and we verify its capabilities. It consists of a sheet with strategically pla...
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| Vydané v: | Journal of robotics and mechatronics Ročník 29; číslo 2; s. 353 - 363 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Tokyo
Fuji Technology Press Co. Ltd
01.04.2017
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| Predmet: | |
| ISSN: | 0915-3942, 1883-8049 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | [abstFig src='/00290002/09.jpg' width='300' text='Standard Lifilm configuration' ] In this study, we propose an excretion detection system, Lifi, which does not require sensors inside diapers, and we verify its capabilities. It consists of a sheet with strategically placed air intakes, a set of gas sensors, and a processing unit with a newly developed excretion detection algorithm. The gas sensor detects chemicals with odor in the excrement, such as hydrogen sulfide and urea. The time-series data from the gas sensor was used for the detection of not only excretion, but also of the presence/absence of the cared person on the bed. We examined two algorithms, one with a simple threshold and another based on the clustering of sensor data, obtained using the
k
-means method. The results from both algorithms were satisfactory and similar, once the algorithms were customized for each cared person. However, we adopted the clustering algorithm because it possesses a higher level of flexibility that can be explored and exploited. Lifi was conceived from an overwhelming and serious desire of caretakers to discover the excretion of bed-ridden cared persons, without opening their diapers. We believe that Lifi, along with the clustering algorithm, can help caretakers in this regard. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0915-3942 1883-8049 |
| DOI: | 10.20965/jrm.2017.p0353 |