scholarly journals Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Muhammad Hameed Siddiqi ◽  
Madallah Alruwaili ◽  
Amjad Ali ◽  
Saad Alanazi ◽  
Furkh Zeshan
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Random Fields ◽  
Classification Accuracy ◽  
Conditional Random Fields ◽  
Gaussian Mixture ◽  
Human Activity Recognition ◽  
Substantial Improvement ◽  
Learning Method ◽  
Proposed Model

In healthcare, the analysis of patients’ activities is one of the important factors that offer adequate information to provide better services for managing their illnesses well. Most of the human activity recognition (HAR) systems are completely reliant on recognition module/stage. The inspiration behind the recognition stage is the lack of enhancement in the learning method. In this study, we have proposed the usage of the hidden conditional random fields (HCRFs) for the human activity recognition problem. Moreover, we contend that the existing HCRF model is inadequate by independence assumptions, which may reduce classification accuracy. Therefore, we utilized a new algorithm to relax the assumption, allowing our model to use full-covariance distribution. Also, in this work, we proved that computation wise our method has very much lower complexity against the existing methods. For the experiments, we used four publicly available standard datasets to show the performance. We utilized a 10-fold cross-validation scheme to train, assess, and compare the proposed model with the conditional learning method, hidden Markov model (HMM), and existing HCRF model which can only use diagonal-covariance Gaussian distributions. From the experiments, it is obvious that the proposed model showed a substantial improvement with p value ≤0.2 regarding the classification accuracy.

scholarly journals Human activity recognition using conditional random fields and privileged information

2016 ◽  
Author(s):  
Μιχαήλ Βρίγκας
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Human Activity Recognition ◽  
Privileged Information

Η διατριβή ασχολείται με το πρόβλημα της αναγνώρισης της ανθρώπινης δραστηριότητας από εικονοσειρές και απλές εικόνες, το οποίο ανήκει στην ευρύτερη περιοχή της υπολογιστικής όρασης. Για την επίλυση του προβλήματος χρησιμοποιήθηκαν τα υπό συνθήκη τυχαία πεδία συνδυάζοντας δεδομένα από πολλαπλές πηγές. Επιπλέον, προτάθηκε μια καινούρια μέθοδος ταξινόμησης που βασίζεται στην προνομιακή πληροφορία, η oποία δίδεται ως επιπλέων είσοδος στο μοντέλο και είναι διαθέσιμη μόνο στην φάση της εκπαίδευσης αλλά όχι στην φάση του ελέγχου. Τα πειραματικά αποτελέσματα δείχνουν ότι αυτού του είδους η πληροφορία βοηθά στο να δημιουργήσουμε έναν ισχυρότερο ταξινομητή απ' ότι κάποιος θα μάθαινε χωρίς αυτήν, ενώ αυξάνει σημαντικά την ακρίβεια της αναγνώρισης του μοντέλου.

scholarly journals A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2307 ◽  
Author(s):  
Shoujiang Xu ◽  
Qingfeng Tang ◽  
Linpeng Jin ◽  
Zhigeng Pan
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Classification Accuracy ◽  
Human Activity Recognition ◽  
Gradient Boosting ◽  
Layer By Layer ◽  
Proposed Model ◽  
Public Datasets ◽  
Gyroscope Sensor

Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosting Trees (XGBoost), Random Forest, Extremely Randomized Trees (ExtraTrees) and Softmax Regression, and the model goes deeper layer by layer. The initial input vectors sampled from smartphone accelerometer and gyroscope sensor are trained separately by four different classifiers in the first layer, and the probability vectors representing different classes to which each sample belongs are obtained. Both the initial input data and the probability vectors are concatenated together and considered as input to the next layer’s classifiers, and eventually the final prediction is obtained according to the classifiers of the last layer. This system achieved satisfying classification accuracy on two public datasets of HAR based on smartphone accelerometer and gyroscope sensor. The experimental results show that the proposed approach has gained better classification accuracy for HAR compared to existing state-of-the-art methods, and the training process of the model is simple and efficient.

scholarly journals A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yingjie Lin ◽  
Jianning Wu
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Convolution Neural Network ◽  
Sequence Information ◽  
Practical Applications ◽  
Dilated Convolution ◽  
Proposed Model ◽  
Convolution Structure

A novel multichannel dilated convolution neural network for improving the accuracy of human activity recognition is proposed. The proposed model utilizes the multichannel convolution structure with multiple kernels of various sizes to extract multiscale features of high-dimensional data of human activity during convolution operation and not to consider the use of the pooling layers that are used in the traditional convolution with dilated convolution. Its advantage is that the dilated convolution can first capture intrinsical sequence information by expanding the field of convolution kernel without increasing the parameter amount of the model. And then, the multichannel structure can be employed to extract multiscale gait features by forming multiple convolution paths. The open human activity recognition dataset is used to evaluate the effectiveness of our proposed model. The experimental results showed that our model achieves an accuracy of 95.49%, with the time to identify a single sample being approximately 0.34 ms on a low-end machine. These results demonstrate that our model is an efficient real-time HAR model, which can gain the representative features from sensor signals at low computation and is hopeful for the effective tool in practical applications.

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 Human Activity Recognition of Exoskeleton Robot with Supervised Learning Techniques

2021 ◽  
Author(s):  
Jiacheng Mai ◽  
zhiyuan chen ◽  
Chunzhi Yi ◽  
Zhen Ding
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
The Body ◽  
Lower Limbs ◽  
Data Sets ◽  
Learning Approaches ◽  
Learning Method

Abstract Lower limbs exoskeleton robots improve the motor ability of humans and can facilitate superior rehabilitative training. By training large datasets, many of the currently available mobile and signal devices that may be worn on the body can employ machine learning approaches to forecast and classify people's movement characteristics. This approach could help exoskeleton robots improve their ability to predict human activities. Two popular data sets are PAMAP2, which was obtained by measuring people's movement through inertial sensors, and WISDM, which was collected people's activity information through mobile phones. With the focus on human activity recognition, this paper applied the traditional machine learning method and deep learning method to train and test these datasets, whereby it was found that the prediction performance of a decision tree model was highest on these two data sets, which is 99% and 72% separately, and the time consumption of decision tree is the least. In addition, a comparison of the signals collected from different parts of the human body showed that the signals deriving from the hands presented the best performance in terms of recognizing human movement types.

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