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Particle Swarm Optimization Using Adaptive Boundary Correction for Human Activity Recognition
- Particle Swarm Optimization Using Adaptive Boundary Correction for Human Activity Recognition
- Kwon, Y[Kwon, Yongjin]; Heo, S[Heo, Seonguk]; Kang, K[Kang, Kyuchang]; Bae, C[Bae, Changseok]
- DGIST Authors
- Heo, S[Heo, Seonguk]
- Issue Date
- KSII Transactions on Internet and Information Systems, 8(6), 2070-2086
- Article Type
- Accelerometers; Activity Recognition; Adaptive Boundary Correction (ABC); Boundary Correction; Classification Boundary; Digital Storage; High Sampling Rates; Human Activity Recognition; Life Log; Lifelog; Particle Swarm Optimization (PSO); Pattern Recognition; Sampling Rate; Sampling Rates; Signal Encoding; Smartphones; Triaxial Accelerometer
- As a kind of personal lifelog data, activity data have been considered as one of the most compelling information to understand the user's habits and to calibrate diagnoses. In this paper, we proposed a robust algorithm to sampling rates for human activity recognition, which identifies a user's activity using accelerations from a triaxial accelerometer in a smartphone. Although a high sampling rate is required for high accuracy, it is not desirable for actual smartphone usage, battery consumption, or storage occupancy. Activity recognitions with well-known algorithms, including MLP, C4.5, or SVM, suffer from a loss of accuracy when a sampling rate of accelerometers decreases. Thus, we start from particle swarm optimization (PSO), which has relatively better tolerance to declines in sampling rates, and we propose PSO with an adaptive boundary correction (ABC) approach. PSO with ABC is tolerant of various sampling rate in that it identifies all data by adjusting the classification boundaries of each activity. The experimental results show that PSO with ABC has better tolerance to changes of sampling rates of an accelerometer than PSO without ABC and other methods. In particular, PSO with ABC is 6%, 25%, and 35% better than PSO without ABC for sitting, standing, and walking, respectively, at a sampling period of 32 seconds. PSO with ABC is the only algo-rithm that guarantees at least 80% accuracy for every activity at a sampling period of smaller than or equal to 8 seconds. © 2014 KSII.
- Korean Society for Internet Information
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