A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones

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.

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Human Activity Recognition Based on Residual Network and BiLSTM

Sensors ◽

10.3390/s22020635 ◽

2022 ◽

Vol 22 (2) ◽

pp. 635

Author(s):

Yong Li ◽

Luping Wang

Keyword(s):

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Inertial Sensors ◽

Human Activity Recognition ◽

Activity Data ◽

Multidimensional Signals ◽

Proposed Model ◽

Public Datasets ◽

Residual Block

Due to the wide application of human activity recognition (HAR) in sports and health, a large number of HAR models based on deep learning have been proposed. However, many existing models ignore the effective extraction of spatial and temporal features of human activity data. This paper proposes a deep learning model based on residual block and bi-directional LSTM (BiLSTM). The model first extracts spatial features of multidimensional signals of MEMS inertial sensors automatically using the residual block, and then obtains the forward and backward dependencies of feature sequence using BiLSTM. Finally, the obtained features are fed into the Softmax layer to complete the human activity recognition. The optimal parameters of the model are obtained by experiments. A homemade dataset containing six common human activities of sitting, standing, walking, running, going upstairs and going downstairs is developed. The proposed model is evaluated on our dataset and two public datasets, WISDM and PAMAP2. The experimental results show that the proposed model achieves the accuracy of 96.95%, 97.32% and 97.15% on our dataset, WISDM and PAMAP2, respectively. Compared with some existing models, the proposed model has better performance and fewer parameters.

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Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields

Computational Intelligence and Neuroscience ◽

10.1155/2019/8590560 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 4

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.

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Similarity Embedding Networks for Robust Human Activity Recognition

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3448021 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1-17

Author(s):

Chenglin Li ◽

Carrie Lu Tong ◽

Di Niu ◽

Bei Jiang ◽

Xiao Zuo ◽

...

Keyword(s):

Activity Recognition ◽

Human Activity ◽

Short Term Memory ◽

Real Space ◽

Human Activity Recognition ◽

Sensor Data ◽

Activity Data ◽

Extensive Evaluation ◽

Sensor Signals ◽

Public Datasets

Deep learning models for human activity recognition (HAR) based on sensor data have been heavily studied recently. However, the generalization ability of deep models on complex real-world HAR data is limited by the availability of high-quality labeled activity data, which are hard to obtain. In this article, we design a similarity embedding neural network that maps input sensor signals onto real vectors through carefully designed convolutional and Long Short-Term Memory (LSTM) layers. The embedding network is trained with a pairwise similarity loss, encouraging the clustering of samples from the same class in the embedded real space, and can be effectively trained on a small dataset and even on a noisy dataset with mislabeled samples. Based on the learned embeddings, we further propose both nonparametric and parametric approaches for activity recognition. Extensive evaluation based on two public datasets has shown that the proposed similarity embedding network significantly outperforms state-of-the-art deep models on HAR classification tasks, is robust to mislabeled samples in the training set, and can also be used to effectively denoise a noisy dataset.

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A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition

Mathematical Problems in Engineering ◽

10.1155/2020/5426532 ◽

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.

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

Journal of Advanced Transportation ◽

10.1155/2021/2026895 ◽

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.

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AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/431 ◽

2019 ◽

Cited By ~ 5

Author(s):

HaoJie Ma ◽

Wenzhong Li ◽

Xiao Zhang ◽

Songcheng Gao ◽

Sanglu Lu

Keyword(s):

Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Domain Knowledge ◽

Research Problem ◽

Human Activity Recognition ◽

Attention Mechanism ◽

The Rich ◽

Public Datasets ◽

Gated Recurrent Units

Sensor-based human activity recognition is a fundamental research problem in ubiquitous computing, which uses the rich sensing data from multimodal embedded sensors such as accelerometer and gyroscope to infer human activities. The existing activity recognition approaches either rely on domain knowledge or fail to address the spatial-temporal dependencies of the sensing signals. In this paper, we propose a novel attention-based multimodal neural network model called AttnSense for multimodal human activity recognition. AttnSense introduce the framework of combining attention mechanism with a convolutional neural network (CNN) and a Gated Recurrent Units (GRU) network to capture the dependencies of sensing signals in both spatial and temporal domains, which shows advantages in prioritized sensor selection and improves the comprehensibility. Extensive experiments based on three public datasets show that AttnSense achieves a competitive performance in activity recognition compared with several state-of-the-art methods.

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