Abstract
This work analyses energy expenditure in outdoor sport environments with augmented reality technology. Battery efficiency is becoming a relevant topic in the context of the varied outdoor end-user services, among other realms. It is a key to the acceptance and use of mobile technology. In outdoor environments, battery efficiency can be low, especially when information based on close-to-real-time requires internet access and the use of sensors. Such requirement is today evident with the growth of internet dependence and multiple sensors, which perform both actively and passively via fitness gadgets, smartphones, pervasive systems, and other personal mobile gadgets. In this context, it is relevant to understand how energy is spent with the accelerometer, global position system, and internet access (Wi-Fi or mobile data) providing smart data for outdoor sports activities. Through a prototype, an analysis is made based on the current battery autonomy, and an algorithm model for better battery efficiency is proposed.
TopIntroduction
Battery efficiency in outdoor environments is today a strong user requirement and integrated into different aspects of the social living and social activities, becoming a key users requirement, being one of such components of outdoor activities, such as outdoor sports. Until recently, such solutions were based on dedicated hardware, such as smartphones. Smartphones are portable pervasive personal communication devices found in both developing and developed countries, they are potentially a highly valuable and accurate source of data from which mobility and activity information can be collected (Ball, R., et al., 2014). However, the integration of multiple sensors into these most varied sets of mobile personal devices, such as smart-glasses, smartwatches, and other pervasive mobile gadgets bring in the possibility to consider smart data to improve specific Augmented Reality (AR) meaningful feedback to the users, avoiding information overload (Pascoal, R. M., & Guerreiro, S. L., 2017), as well as look and feel. This aspect is particularly relevant in variable environments, such as outdoor sports as these environments embody high topological variability, intermittent connectivity, constrained devices, and a need for constant middleware readjustment, based on the user's sensed indicators (smart data). For such purpose, it is necessary to re-think the energy needs of mobile pervasive systems of AR, such as MPARS to devise more flexible systems, that can support mobility.
Battery efficiency is an issue especially in outdoor environments, when users often access the Internet and use some sensors, such as GPS and accelerometer sensors, for instance, to recognize sport activities like walking, running, and biking.
In one hand, the accelerometer sensor has been heavily used in smartphones for non-intrusive activity recognition. Based on three axes it measures the acceleration of each axis in the units of g-force (Su, X., Tong, H., & Ji, P., 2014). In other hand, GPS is also heavy used in any location-based app in smart devices, such as smartphones, e.g., almost a quarter of all Android apps available in the Google Play Store use GPS. Apps require monitoring your locations in a continuous fashion, but it consumes the highest power from the smartphones (Dutta, J., Pramanick, P., & Roy, S., 2018).
To resolve, this work introduces an battery efficient context-aware approach which utilizes user’s mobility information from the sport's context and as well smartphone’s sensing values from the in-built accelerometer to provide a very close estimation of the present location of the user without using continuous GPS. It is an solution without lost the geolocation accuracy.
This paper contributes to the first analysis of energy needs and battery efficiency in outdoor sports environments. The goal is to reduce battery expenditure. The contributions of this work are three-fold:
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To simplify Hardware needs for energy efficiency.
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To handle energy-constrained with intermittent connectivity to the Internet and Global Position System in outdoor sports contexts when MPARS provides meaningful feedback from smart data in close-real-time.
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It proposes a new model of energy efficiency for AR technology in outdoor sports environments.
The rest of the paper is organized as follows. Still, in this section, the main questions of this study are presented. Section II covers the background and discusses energy expenditure for adjusting the MPARS to receive smart data, helped by sensors, and Internet connectivity, applicable in outdoor sports contexts. Section III presents the main focus of the chapter with issues, controversies, problems, and energy needs. Section IV has the experiments with an MPARS prototype to collect battery tests, followed by the hardware and software setup. Section V has the work results, followed by Section VI, which shows a proposal to battery efficiency for MPARS and Section VII the conclusions. Finally, Section VIII the future work and directions.
This work addresses the following open questions:
Key Terms in this Chapter
Autonomy: In a battery context is the period of time (in minutes or hours) a battery will last for at a specified load level. Autonomy can also be referred to as discharge time, or runtime.
Information Overload: It is a difficulty in understanding an issue and effectively making decisions when one has too much information and associated with the excessive quantity of information.
Efficiency: It is the fundamental reduction in the amount of wasted resources that are used to produce a given number of goods or services, resulting from the optimization of resource-use to best serve.
Activity Recognition: It is the task of knowing the movement of a person based on sensor data, such as an accelerometer, GPS or other sensors, e.g., in a smartphone.
Meaningful Feedback: Information useful and adequate that brings benefit to those who receive this, especially for the current context.
Smart Data: It is data from which signals and patterns have been extracted by intelligent algorithms. Data is amassed, groomed, and then processed before being sent to a platform for further data consolidation and analytics.
Power Consumption: Refers to the electrical energy per unit time, supplied to operate something, such as a smartphone. It is usually measured in units of watts. The energy used by equipment is always more than the energy really needed.