A Novel Energy-Efficient Approach for Human Activity Recognition

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.

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REAL-TIME TRIAXIAL ACCELEROMETER-BASED ENERGY EFFICIENT HUMAN ACTIVITY RECOGNITION USING SMARTPHONE

International Journal of Advance Engineering and Research Development ◽

10.21090/ijaerd.55848 ◽

2017 ◽

Vol 4 (06) ◽

Keyword(s):

Real Time ◽

Activity Recognition ◽

Human Activity ◽

Energy Efficient ◽

Human Activity Recognition ◽

Triaxial Accelerometer

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Model-Based Design of Energy-Efficient Human Activity Recognition Systems with Wearable Sensors

Technologies ◽

10.3390/technologies6040089 ◽

2018 ◽

Vol 6 (4) ◽

pp. 89

Author(s):

Florian Grützmacher ◽

Albert Hein ◽

Thomas Kirste ◽

Christian Haubelt

Keyword(s):

Energy Consumption ◽

Activity Recognition ◽

Human Activity ◽

Energy Efficient ◽

Human Activity Recognition ◽

Sensor Data ◽

System Level ◽

Estimation Methods ◽

Level Energy ◽

Wearable Sensor

The advances in MEMS technology development allow for small and thus unobtrusive designs of wearable sensor platforms for human activity recognition. Multiple such sensors attached to the human body for gathering, processing, and transmitting sensor data connected to platforms for classification form a heterogeneous distributed cyber-physical system (CPS). Several processing steps are necessary to perform human activity recognition, which have to be mapped to the distributed computing platform. However, the software mapping is decisive for the CPS’s processing load and communication effort. Thus, the mapping influences the energy consumption of the CPS, and its energy-efficient design is crucial to prolong battery lifetimes and allow long-term usage of the system. As a consequence, there is a demand for system-level energy estimation methods in order to substantiate design decisions even in early design stages. In this article, we propose to combine well-known dataflow-based modeling and analysis techniques with energy models of wearable sensor devices, in order to estimate energy consumption of wireless sensor nodes for online activity recognition at design time. Our experiments show that a reasonable system-level average accuracy above 97% can be achieved by our proposed approach.

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

AI Magazine ◽

10.1609/aimag.v34i2.2473 ◽

2013 ◽

Vol 34 (2) ◽

pp. 48 ◽

Cited By ~ 2

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.

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