Abstract
In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.
Highlights
Human activity recognition plays a crucial role in pervasive computing
We proposed a solution to solve the contradiction between the sampling rate and the power consumption, that is, using a low sampling rate of Inertial measurement units (IMUs) in activity recognition system (ARS) to achieve a similar and the power consumption, that is, using a low sampling rate of IMU in ARS to achieve a similar recognition accuracy, compared with using high sampling rates
This work presents a user-independent and energy-efficient ARS with high accuracy using the sampling rate lower than what is required by the Nyquist theorem
Summary
Human activity recognition plays a crucial role in pervasive computing. Many applications for healthcare, sports, security agencies and context-aware services applications have emerged [1,2].For example, life logs collected by smart mobile phone sensors (such as accelerometers) have been used to provide personalized health care [3]. Human activity recognition plays a crucial role in pervasive computing. Many applications for healthcare, sports, security agencies and context-aware services applications have emerged [1,2]. Life logs collected by smart mobile phone sensors (such as accelerometers) have been used to provide personalized health care [3]. Vermeulen et al [4] developed a smartphone-based falls detection application to help elderly people. Zhou et al [5] implemented a phone system for indoor pedestrian localization. Google Now is one of the emerging smart applications that provide context-aware services. It calculates and pushes relevant information automatically to mobile users based on their current locations [6]
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