Human Activity Recognition on Mobile Devices Using Artificial Hydrocarbon Networks

2018 ◽  
pp. 17-29
Author(s):  
Hiram Ponce ◽  
Guillermo González ◽  
Luis Miralles-Pechuán ◽  
Ma Lourdes Martínez-Villaseñor
Keyword(s):  
Mobile Devices ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition

scholarly journals Energy-Efficient Real-Time Human Activity Recognition on Smart Mobile Devices

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jin Lee ◽  
Jungsun Kim
Keyword(s):  
Real Time ◽  
Mobile Devices ◽  
Activity Recognition ◽  
Human Activity ◽  
Energy Efficient ◽  
Window Size ◽  
Human Activity Recognition ◽  
Smart Mobile ◽  
The Impact ◽  
Smart Mobile Devices

Nowadays, human activity recognition (HAR) plays an important role in wellness-care and context-aware systems. Human activities can be recognized in real-time by using sensory data collected from various sensors built in smart mobile devices. Recent studies have focused on HAR that is solely based on triaxial accelerometers, which is the most energy-efficient approach. However, such HAR approaches are still energy-inefficient because the accelerometer is required to run without stopping so that the physical activity of a user can be recognized in real-time. In this paper, we propose a novel approach for HAR process that controls the activity recognition duration for energy-efficient HAR. We investigated the impact of varying the acceleration-sampling frequency and window size for HAR by using the variable activity recognition duration (VARD) strategy. We implemented our approach by using an Android platform and evaluated its performance in terms of energy efficiency and accuracy. The experimental results showed that our approach reduced energy consumption by a minimum of about 44.23% and maximum of about 78.85% compared to conventional HAR without sacrificing accuracy.

scholarly journals Thinking Fast and Slow: An Approach to Energy-Efficient Human Activity Recognition on Mobile Devices

AI Magazine ◽  
2013 ◽  
Vol 34 (2) ◽  
pp. 48 ◽  
Author(s):  
Yifei Jiang ◽  
Du Li ◽  
Qin Lv
Keyword(s):  
Energy Efficiency ◽  
Mobile Devices ◽  
Activity Recognition ◽  
Human Activity ◽  
Energy Efficient ◽  
Real Life ◽  
Human Activity Recognition ◽  
Mobile App ◽  
Efficient System ◽  
Over Time

According to Daniel Kahneman, there are two systems that drive the human decision making process: The intuitive system that performs the fast thinking, and the deliberative system that does more logical and slower thinking. Inspired by this model, we propose a framework for implementing human activity recognition on mobile devices. In this area, the mobile app is usually always-on and the general challenge is how to balance accuracy and energy consumption. However, among existing approaches, those based on cellular IDs consume little power but are less accurate; those based on GPS/WiFi sampling are accurate often at the costs of battery drainage; moreover, previous methods in general do not improve over time. To address these challenges, our framework consists of two modes: In the deliberation mode, the system learns cell ID patterns that are trained by existing GPS/WiFi based methods; in the intuition mode, only the learned cell ID patterns are used for activity recognition, which is both accurate and energy-efficient; system parameters are learned to control the transition from deliberation to intuition, when sufficient confidence is gained, and the transition from intuition to deliberation, when more training is needed. For the scope of this paper, we first elaborate our framework in a subproblem in activity recognition, trip detection, which recognizes significant places and trips between them. For evaluation, we collected real-life traces of six participants over five months. Our experiments demonstrated consistent results across different participants in terms of accuracy and energy efficiency, and, more importantly, its fast improvement on energy efficiency over time due to regularities in human daily activities.

Depth-based Human Activity Recognition: vINCI Case Study

2020 ◽  
Author(s):  
Lidia Bajenaru ◽  
Ciprian Dobre ◽  
Radu-Ioan Ciobanu ◽  
Georgiana Dedu ◽  
Silviu-George Pantelimon ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition
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