Name
Abstract | ||
|---|---|---|
Water is the most vital nutrient in the human body accounting for about 60% of the body weight. To maintain optimal health, it is important for humans to consume a sufficient amount of fluids daily. Therefore, tracking the amount of human daily fluid intake has a myriad of health applications like dehydration prevention.
In this paper, we present FluidMeter: a ubiquitous and unobtrusive system to track the amount of fluid intake leveraging the inertial sensors embedded in smartwatches. To achieve this, FluidMeter first separates the drinking activities from other human activities (playing, running, eating, etc.). Thereafter, it analyzes the sampled sensors data during the extracted drinking episodes to recognize the sequence of micro-activities (lift the bottle, sip, release the bottle) that constitute the drinking activity. Finally, it applies some machine learning algorithms on some features extracted from sampled sensor data during the sipping period to gauge the amount of fluid intake in the designated drinking episode.
FluidMeter is evaluated by collecting more than 260 hours of different human activities by 70 different participants using different smartwatch models. The results show that FluidMeter can recognize the drinking activity and its micro-activities accurately which is comparable to that achieved by the state-of-the-art techniques. Finally, FluidMeter can estimate the overall amount of fluid intake in grams accurately with a estimation error limited to 15%, highlighting its promise as a ubiquitous health service.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3264923 | Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies |
Keywords | DocType | Volume |
Wearable computing,activity recognition,automatic fluid intake monitoring,gesture recognition,in-the-wild evaluation | Journal | 2 |
Issue | ISSN | Citations |
3 | 2474-9567 | 3 |
PageRank | References | Authors |
0.56 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takashi Hamatani | 1 | 12 | 5.27 |
| Moustafa Elhamshary | 2 | 79 | 6.30 |
| Akira Uchiyama | 3 | 78 | 14.48 |
| Teruo Higashino | 4 | 1086 | 119.60 |