A Sensor-Fusion Solution for Mobile Health-Care Applications

Lee, Chen‐Yi; Lai, Kelvin Yi Tse; Hsu, Shu Yu

doi:10.1007/978-3-319-14711-6_15

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Thanks to such a simple feature, the complexity of these systems is low, the coupling between their components is reduced and, since each component is only activated upon message reception, these systems are power-aware by design, theoretically reducing energy consumption when compared to typical synchronous systems [28,49,50]. The top-level architecture of an event-driven system consists of three logical layers: (i) event source ; (ii) event processing ; and (iii) event handling [48], as shown in Figure 2.…”

Section: Fundamentals Of Event-driven Solutionmentioning

confidence: 99%

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Pérez-Torres¹,

Torres-Huitzil²,

Galeana-Zapién³

2016

Sensors

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The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

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