data to train and test classification algorithms iii) a model deployment stage where the
learnt model is transferred to the mobile device for identifying new contiguous portions
of sensor data streams that cover various activities of interest. Sensor data can be
processed in real-time or logged for offline analysis and evaluation. The model generation is usually performed offline on a server system and later deployed to the phone
to recognize the activity performed.
Recently, several authors [7, 8] have proposed many applications related to activity
recognition on multiple body positions. Most of the work, like Ahmad [9], Tran [10],
Awan [11], Shoaib [12], and Abidine [13], consider a single classifier approach to
study activity recognition using smartphones. For the classification, SVMs are popular
[8, 14]. It is also the case for HMMs [15] which they commonly used for time-series
activity recognition. However, there is very limited number of publications in the
literature that investigate the application of the WSVM classifier for smartphone data,
and no one is found about applying the latter one on smartphone data or even on HAR
system’s datasets. Building a system with high precision to accurately identify these
activities is a challenging task.
In this work, we adopted a new method for physical activity recognition using
mobile phones that uses labels outputting WSVM in HMM. WSVM investigated the
effect of overweighting the minority class on SVM modeling between the performed
activities. HMM is a natural solution to address the activity complexity by ― capturing
and smoothing information during the transition between two activities (e.g. Walking
and Standing). We also used the feature extraction approach that transforms the original
high dimensional data to a lower dimensional feature space. The transformation can be
linear or nonlinear. In this project, we employed the linear Principal Component
Analysis (PCA) [16] to extract the feature vectors.
2 The Proposed HAR System by Combining WSVM-HMM
Based PCA
2.1 Overview
Figure 1 shows the architecture of the proposed activity recognition system. Among
the available labelled data, training and test subsets are chosen using the crossvalidation mechanism. The constructed PCA space is then used for training and testing
the Weighted SVM classifier. In the second step of the process is a pre-classification by
‘WSVM’, this phase is carried out by the ‘cross-validation’ will generate an estimate of
the label vector.
The principal component features concatenated with the WSVM estimated label
vector are employed as a new training data to train HMM classifier. The final classification is performed with the ‘Viterbi’ algorithm, by the use of a HMM model.
An estimated label vector is generated by the ‘Viterbi’ algorithm and the system
will output the recognized activity (i.e., walking, running, and others).
Human Activities Recognition in Android Smartphone Using WSVM-HMM Classifier
387
learnt model is transferred to the mobile device for identifying new contiguous portions
of sensor data streams that cover various activities of interest. Sensor data can be
processed in real-time or logged for offline analysis and evaluation. The model generation is usually performed offline on a server system and later deployed to the phone
to recognize the activity performed.
Recently, several authors [7, 8] have proposed many applications related to activity
recognition on multiple body positions. Most of the work, like Ahmad [9], Tran [10],
Awan [11], Shoaib [12], and Abidine [13], consider a single classifier approach to
study activity recognition using smartphones. For the classification, SVMs are popular
[8, 14]. It is also the case for HMMs [15] which they commonly used for time-series
activity recognition. However, there is very limited number of publications in the
literature that investigate the application of the WSVM classifier for smartphone data,
and no one is found about applying the latter one on smartphone data or even on HAR
system’s datasets. Building a system with high precision to accurately identify these
activities is a challenging task.
In this work, we adopted a new method for physical activity recognition using
mobile phones that uses labels outputting WSVM in HMM. WSVM investigated the
effect of overweighting the minority class on SVM modeling between the performed
activities. HMM is a natural solution to address the activity complexity by ― capturing
and smoothing information during the transition between two activities (e.g. Walking
and Standing). We also used the feature extraction approach that transforms the original
high dimensional data to a lower dimensional feature space. The transformation can be
linear or nonlinear. In this project, we employed the linear Principal Component
Analysis (PCA) [16] to extract the feature vectors.
2 The Proposed HAR System by Combining WSVM-HMM
Based PCA
2.1 Overview
Figure 1 shows the architecture of the proposed activity recognition system. Among
the available labelled data, training and test subsets are chosen using the crossvalidation mechanism. The constructed PCA space is then used for training and testing
the Weighted SVM classifier. In the second step of the process is a pre-classification by
‘WSVM’, this phase is carried out by the ‘cross-validation’ will generate an estimate of
the label vector.
The principal component features concatenated with the WSVM estimated label
vector are employed as a new training data to train HMM classifier. The final classification is performed with the ‘Viterbi’ algorithm, by the use of a HMM model.
An estimated label vector is generated by the ‘Viterbi’ algorithm and the system
will output the recognized activity (i.e., walking, running, and others).
Human Activities Recognition in Android Smartphone Using WSVM-HMM Classifier
387