Abstract
In recent years, sensor-based human activity recognition (HAR) has become a hot topic due to the advancement of sensing technologies, wireless communication technologies and nano-technologies. Since the sensor signals are usually non-stationary and quite noisy, both selecting the discriminant feature representations and finding out the optimal parameters for recognition algorithm play an important role for the enhanced performance and robustness of an HAR system. However, most of the previous research focused on one of them ignoring their interactions. Very few studies focused on these two aspects simultaneously. Considering the two factors separately may lead to inferior HAR performance. This paper presents a novel HAR framework which can optimize the feature set and the parameters of recognition algorithm synchronously for robust and optimal system performance. A new hybrid feature selection methodology using game-theory based feature selection (GTFS) and binary firefly algorithm (BFA), called GTFS-BFA, is proposed. GTFS-BFA is a hybrid methodology combining evidence from both filter and wrapper feature selection methods. It consists of two phases, namely pre-selection phase and re-selection phase. Pre-selection phase relies on game-theory-based filter method, while the re-selection phase uses binary firefly algorithm (BFA) as a wrapper method. The popular and efficient algorithm kernel extreme learning machine (KELM) is utilized as a classifier. The experimental results indicate that the proposed method can obtain better comprehensive performance in terms of four performance measures through a comparison to other existing methods on daily activity dataset from five body positions.
Highlights
In recent years, as the development of inertial measurement unit (IMU) sensors and wireless transmission technology, human activity recognition (HAR) has become a promising research area in academic and application fields
The proposed framework is composed of a game-theory based feature selection (GTFS)-based feature pre-selection phase and an Firefly algorithm (FA)-based combinational optimization phase
Experimental results have shown that the HAR performance of the proposed GTFS-binary firefly algorithm (BFA) based approach is superior to other wellestablished counterparts
Summary
As the development of inertial measurement unit (IMU) sensors and wireless transmission technology, human activity recognition (HAR) has become a promising research area in academic and application fields. The wrapper based methods select the feature subset by utilizing the performance of a classifier. A hybrid method combining the advantages of filter and wrapper based methods could improve the performance of feature selection. The classifier in the wrapper based methods has an important influence on the selected features and the performance of HAR. A novel hybrid feature selection method named GTFS-BFA is proposed in this paper to optimize the HAR system performance. Parameters and vice versa, this paper presents a novel HAR framework that can optimize the feature set and the parameters of the recognition algorithm synchronously This helps optimize the feature selection and the parameters of the recognition algorithm effectively and achieve robust and optimal system performance.
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