e-Gibalec: Mobile application to monitor and encourage physical activity in schoolchildren

Janko, Vito; Cvetković, Božidara; Gradišek, Anton; Luštrek, Mitja; Štrumbelj, Boro; Kajtna, Tanja

doi:10.3233/ais-170453

Cited by 16 publications

(17 citation statements)

References 21 publications

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“…The source of error or the shortcoming of this approach is that the regression model is trained to fit all activities, however, the shape of the regression model changes per activity, which results in inaccurate EEE. (2) Methods using AR -To overcome the problem of previous methods, Janko et al [64] used AR to recognize the performed activity and include it as a feature for training the EEE regression model. Activity-specific methods These methods are composed of two levels.…”

Section: Eee Methodsmentioning

confidence: 99%

A Survey on Energy Expenditure Estimation Using Wearable Devices

2020

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Human Energy Expenditure (EE) is a valuable tool for measuring physical activity and its impact on our body in an objective way. To accurately measure the EE, there are methods such as doubly labeled water and direct and indirect calorimetry, but their cost and practical limitations make them suitable only for research and professional sports. This situation, combined with the proliferation of commercial activity monitors, has stimulated the research of EE estimation (EEE) using machine learning on multimodal data from wearable devices. The article provides an overview of existing work in this evolving field, categorizes it, and makes publicly available an EEE dataset. Such a dataset is one of the most valuable resources for the development of the field but is generally not provided by researchers due to the high cost of collection. Finally, the article highlights best practices and promising future direction for designing EEE methods.

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Section: Eee Methodsmentioning

confidence: 99%

A Survey on Energy Expenditure Estimation Using Wearable Devices

2020

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“…It allowed the child to set its daily movement goals, compete with classmates, and keep upgrading its virtual avatar using the won virtual awards. The whole system is thoroughly described in our two previous works on the topic [ 52 , 53 ].…”

Section: Datasetsmentioning

confidence: 99%

A General Framework for Making Context-Recognition Systems More Energy Efficient

Janko

Luštrek

2021

Sensors

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Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions—in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.

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“…This dataset [ 16 ] was recorded on 10 children carrying a smartphone, which was recording acceleration data at a frequency of 50-Hz. The goal was to create a classifier that could recognize four core activities: walking, running, cycling and resting.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

Janko

Luštrek

2017

Sensors

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The recognition of the user’s context with wearable sensing systems is a common problem in ubiquitous computing. However, the typically small battery of such systems often makes continuous recognition impractical. The strain on the battery can be reduced if the sensor setting is adapted to each context. We propose a method that efficiently finds near-optimal sensor settings for each context. It uses Markov chains to simulate the behavior of the system in different configurations and the multi-objective genetic algorithm to find a set of good non-dominated configurations. The method was evaluated on three real-life datasets and found good trade-offs between the system’s energy expenditure and the system’s accuracy. One of the solutions, for example, consumed five-times less energy than the default one, while sacrificing only two percentage points of accuracy.

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e-Gibalec: Mobile application to monitor and encourage physical activity in schoolchildren

Cited by 16 publications

References 21 publications

A Survey on Energy Expenditure Estimation Using Wearable Devices

A Survey on Energy Expenditure Estimation Using Wearable Devices

A General Framework for Making Context-Recognition Systems More Energy Efficient

Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition

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