Class binarization with self-adaptive algorithm to improve human activity recognition
Flourishing research in Human Activity Recognition (HAR) is essential in improving the quality of an individual’s health. Low cost and privacy interest, sensing technology becomes an imperative topic in activity monitoring applications. Nevertheless, the presence of high interclass similarity fro...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/76980/1/FSKTM%202018%2068%20-%20IR.pdf http://psasir.upm.edu.my/id/eprint/76980/ |
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| Summary: | Flourishing research in Human Activity Recognition (HAR) is essential in
improving the quality of an individual’s health. Low cost and privacy interest,
sensing technology becomes an imperative topic in activity monitoring applications.
Nevertheless, the presence of high interclass similarity from similar activities mainly
involving stairs activities yields to degrade the recognition accuracy. These kind of
activities highly sparsely distributed in the input space which is problematic to be
distinguish using traditional classifier model. Even though deep learning becomes a
recent imperative topic, model complexity is considered as a foremost drawback and
impractical to be conducted. Furthermore, although better recognition of stairs
activities is accomplished, recognition of stationary activities is less reported due to
less sensitivity of lesser waveform. Somehow, it might occur some of extracted
features are insignificant to describe the activity. Even if a ranking method is widely
utilized in solving numerous of dimension reduction problems such as in
bioinformatics and high spectral images, most of works are disregarding the
boundary to discard the irrelevant features.
In order to improve recognition of high interclass similarity activities, One-Versus-
All (OVA) binarization strategy is introduced by transforming original multi-class
classification problems into a series of two-class classification problems. However,
the learning complexity of classification is increased due to the expansion number
of learning model. Therefore, feature selection using Relief-f with self-adaptive
Differential Evolution (rsaDE) algorithm is proposed to select the most significant
features. To enhance the selection of most highly ranking features, irrelevant features
are ‘pruned’ based on determined boundary threshold. In order to estimate the quality
of ‘pruned’ features, self-adaptive DE algorithm is proposed. Two parameters
(population size and generation numbers) are adaptively adopted from number of remaining ranking features. Also, self-adaptive scaling factor and crossover
probability control parameters are introduced to diminish time of finding an optimal
parameter to produce the best population. In order to investigate the correlation
between features and class, generated feature subsets are rearranged according to its
mutual information. In such circumstances, frequency domain features are proposed
due to their less susceptible to signal quality variations and beneficial to recognize
stationary activity. These features are combined with statistical features to improve
the ability of classifier model in distinguishing between locomotion, stationary and
complex activities.
Two publicly activity datasets are used; Wireless Sensor Data Mining (WISDM) and
Physical Activity Monitoring for Aging People (PAMAP2). WISDM consists of six
different types physical activity, while PAMAP2 covers eighteen activities
comprising various simple and complex activities. In comparison, WISDM utilizes
an accelerometer sensor embedded in Android smartphone. Meanwhile, PAMAP2
utilizes an accelerometer sensor equipped with three Inertial Measurement Unit
(IMU) devices attached to three different placements. Performance of the proposed
method is compared with several benchmark works. Experimental results have
significantly promised an improvement of activity recognition level, mainly
involving very similar activities. |
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