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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]
Issued Date
Article Type
AccelerometersActivity RecognitionAdaptive Boundary Correction (ABC)Boundary CorrectionClassification BoundaryDigital StorageHigh Sampling RatesHuman Activity RecognitionLife LogLifelogParticle Swarm Optimization (PSO)Pattern RecognitionSampling RateSampling RatesSignal EncodingSmartphonesTriaxial 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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