ArticleSensors (Basel, Switzerland)2017
A Novel Energy-Efficient Approach for Human Activity Recognition.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition.Methods and protocols · 2022Article
- A novel smartphone-based activity recognition modeling method for tracked equipment in forest operations.PloS one · 2022Article
- A General Framework for Making Context-Recognition Systems More Energy Efficient.Sensors (Basel, Switzerland) · 2021Article
- Step by Step Towards Effective Human Activity Recognition: A Balance between Energy Consumption and Latency in Health and Wellbeing Applications.Sensors (Basel, Switzerland) · 2019Review
- An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning.Sensors (Basel, Switzerland) · 2019Article
- Deep Liquid State Machines With Neural Plasticity for Video Activity Recognition.Frontiers in neuroscience · 2019Article
- Using Markov Chains and Multi-Objective Optimization for Energy-Efficient Context Recognition.Sensors (Basel, Switzerland) · 2017Article
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Authors and funding
9 authors.
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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.
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