Granular Region-oriented Fuzzy-Rough based kNN Improvization for Activity Recognition Modeling

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Vijay Borges ◽  
Wilson Jeberson
Keyword(s):  
Pattern Recognition ◽  
Activity Recognition ◽  
Human Activity ◽  
Test Object ◽  
Feature Space ◽  
Nearest Neighbors ◽  
Human Activity Recognition ◽  
Similarity Relations ◽  
Fuzzy Similarity ◽  
Knn Classifier

Activity recognition is a complex task of the Human Computer Interaction (HCI) domain with ever-increasing research interest. Human activity recognition has been specially addressed by the advances in pattern recognition. k-Nearest Neighbors(kNN) is a non-parametric classifier from pattern recognition theory, that mimics human decision making by taking previous experiences into consideration for segregating unknown objects. A novel fuzzy-rough model, based on granular computing for improvisation of the kNN classifier is proposed herewith. In this model, feature-wise fuzzy memberships are generated to fuzzify the feature space of the nearest neighbors of the test object. These neighbors fuzzified feature space are then aggregated into granules, based on their class-belongingness. From these, lower and upper approximation granules are generated using rough set theory to classify the test object. It is shown experimentally that this model outperforms the traditional kNN by 16.43% and Fuzzy-kNN by 10.25%, in the human activity recognition domain. Another novelty is in the efficient use of the fuzzy similarity relations in class-dependent granulated feature space, and, fuzzy-rough lower/upper approximations in the hybridization of the kNN classifier.

scholarly journals An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zongying Liu ◽  
Shaoxi Li ◽  
Jiangling Hao ◽  
Jingfeng Hu ◽  
Mingyang Pan
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Processing Time ◽  
Kernel Method ◽  
Nearest Neighbors ◽  
Human Activity Recognition ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Proposed Model

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds.

scholarly journals Performance Boosting of Scale and Rotation Invariant Human Activity Recognition (HAR) with LSTM Networks Using Low Dimensional 3D Posture Data in Egocentric Coordinates

2020 ◽  
Vol 10 (23) ◽  
pp. 8474
Author(s):  
Ibrahim Furkan Ince
Keyword(s):  
Time Series ◽  
Coordinate System ◽  
Dimension Reduction ◽  
Activity Recognition ◽  
Human Activity ◽  
Feature Space ◽  
Human Activity Recognition ◽  
Time Series Classification ◽  
Rotation Invariant ◽  
Low Dimensional

Human activity recognition (HAR) has been an active area in computer vision with a broad range of applications, such as education, security surveillance, and healthcare. HAR is a general time series classification problem. LSTMs are widely used for time series classification tasks. However, they work well with high-dimensional feature vectors, which reduce the processing speed of LSTM in real-time applications. Therefore, dimension reduction is required to create low-dimensional feature space. As it is experimented in previous study, LSTM with dimension reduction yielded the worst performance among other classifiers, which are not deep learning methods. Therefore, in this paper, a novel scale and rotation invariant human activity recognition system, which can also work in low dimensional feature space is presented. For this purpose, Kinect depth sensor is employed to obtain skeleton joints. Since angles are used, proposed system is already scale invariant. In order to provide rotation invariance, body relative direction in egocentric coordinates is calculated. The 3D vector between right hip and left hip is used to get the horizontal axis and its cross product with the vertical axis of global coordinate system assumed to be the depth axis of the proposed local coordinate system. Instead of using 3D joint angles, 8 number of limbs and their corresponding 3D angles with X, Y, and Z axes of the proposed coordinate system are compressed with several dimension reduction methods such as averaging filter, Haar wavelet transform (HWT), and discrete cosine transform (DCT) and employed as the feature vector. Finally, extracted features are trained and tested with LSTM (long short-term memory) network, which is an artificial recurrent neural network (RNN) architecture. Experimental and benchmarking results indicate that proposed framework boosts the performance of LSTM by approximately 30% accuracy in low-dimensional feature space.

Optimizing the Performance of KNN Classifier for Human Activity Recognition

2021 ◽  
pp. 373-385
Author(s):  
Ali Al-Taei ◽  
Mohammed Fadhil Ibrahim ◽  
Nada Jasim Habeeb
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Knn Classifier

scholarly journals Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6434
Author(s):  
Changjun Fan ◽  
Fei Gao
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Inertial Sensors ◽  
Wearable Sensors ◽  
Feature Selection Method ◽  
Feature Space ◽  
Human Activity Recognition ◽  
Wearable Electronics ◽  
Benchmark Datasets

The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.

scholarly journals Human Activity Recognition in AAL Environments Using Random Projections

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Robertas Damaševičius ◽  
Mindaugas Vasiljevas ◽  
Justas Šalkevičius ◽  
Marcin Woźniak
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Feature Space ◽  
Human Activity Recognition ◽  
The State ◽  
Random Projections ◽  
Healthcare Applications ◽  
Subject Identification

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject’s body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

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