Improving Human Activity Recognition and its Application in Early Stroke Diagnosis

2015 ◽  
Vol 25 (04) ◽  
pp. 1450036 ◽  
Author(s):  
José R. Villar ◽  
Silvia González ◽  
Javier Sedano ◽  
Camelia Chira ◽  
Jose M. Trejo-Gabriel-Galan
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Finite State Machine ◽  
Cross Validation ◽  
Principal Component ◽  
Human Activity Recognition ◽  
Detection Methods ◽  
Stroke Diagnosis ◽  
Finite State ◽  
Early Stroke

The development of efficient stroke-detection methods is of significant importance in today's society due to the effects and impact of stroke on health and economy worldwide. This study focuses on Human Activity Recognition (HAR), which is a key component in developing an early stroke-diagnosis tool. An overview of the proposed global approach able to discriminate normal resting from stroke-related paralysis is detailed. The main contributions include an extension of the Genetic Fuzzy Finite State Machine (GFFSM) method and a new hybrid feature selection (FS) algorithm involving Principal Component Analysis (PCA) and a voting scheme putting the cross-validation results together. Experimental results show that the proposed approach is a well-performing HAR tool that can be successfully embedded in devices.

scholarly journals Hierarchical syntactic models for human activity recognition through mobility traces

2019 ◽  
Vol 24 (4) ◽  
pp. 451-464 ◽  
Author(s):  
Enrico Casella ◽  
Marco Ortolani ◽  
Simone Silvestri ◽  
Sajal K. Das
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Mobility ◽  
Human Activity Recognition ◽  
Coarse Grained ◽  
Grammatical Inference ◽  
Mobility Patterns ◽  
Daily Life Activities ◽  
Finite State ◽  
Broad Variety

AbstractRecognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known as grammatical inference. We also introduce a measure of similarity that accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity.

scholarly journals Depth-based human activity recognition via multi-level fused features and fast broad learning system

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090783
Author(s):  
Huang Yao ◽  
Mengting Yang ◽  
Tiantian Chen ◽  
Yantao Wei ◽  
Yu Zhang
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Principal Component ◽  
Matrix Decomposition ◽  
Human Activity Recognition ◽  
Learning System ◽  
Training Samples ◽  
Multi Level ◽  
Low Dimensional ◽  
High Level

Human activity recognition using depth videos remains a challenging problem while in some applications the available training samples is limited. In this article, we propose a new method for human activity recognition by crafting an integrated descriptor called multi-level fused features for depth sequences and devising a fast broad learning system based on matrix decomposition for classification. First, the surface normals are computed from original depth maps; the histogram of the surface normal orientations is obtained as a low-level feature by accumulating the contributions from normals, then a high-level feature is acquired by sparse coding and pooling on the aggregation of polynormals. After that, the principal component analysis is applied to the conjunction of the two-level features in order to obtain a low-dimensional and discriminative fused feature. At last, fast broad learning system based on matrix decomposition is proposed to accelerate the training process and enhance the classification results. The recognition results on three benchmark data sets show that our method outperforms the state-of-the-art methods in term of accuracy, especially when the number of training samples is small.

scholarly journals A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

2021 ◽  
Vol 10 (6) ◽  
pp. 3191-3201
Author(s):  
Vijaya Kumar Kambala ◽  
Harikiran Jonnadula
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activities ◽  
Privacy Preserving ◽  
Human Activity Recognition ◽  
Human Motion ◽  
Detection Methods ◽  
Prediction Algorithm ◽  
Hybrid Prediction ◽  
Task Learning

There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.

scholarly journals Dimensionality Reduction for Human Activity Recognition Using Google Colab

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 6
Author(s):  
Sujan Ray ◽  
Khaldoon Alshouiliy ◽  
Dharma P. Agrawal
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Hybrid Approach ◽  
Principal Component ◽  
Human Activity Recognition ◽  
Mobile Sensing ◽  
Sensor Data ◽  
Testing Time ◽  
Machine Learning Models

Human activity recognition (HAR) is a classification task that involves predicting the movement of a person based on sensor data. As we can see, there has been a huge growth and development of smartphones over the last 10–15 years—they could be used as a medium of mobile sensing to recognize human activity. Nowadays, deep learning methods are in a great demand and we could use those methods to recognize human activity. A great way is to build a convolutional neural network (CNN). HAR using Smartphone dataset has been widely used by researchers to develop machine learning models to recognize human activity. The dataset has two parts: training and testing. In this paper, we propose a hybrid approach to analyze and recognize human activity on the same dataset using deep learning method on cloud-based platform. We have applied principal component analysis on the dataset to get the most important features. Next, we have executed the experiment for all the features as well as the top 48, 92, 138, and 164 features. We have run all the experiments on Google Colab. In the experiment, for the evaluation of our proposed methodology, datasets are split into two different ratios such as 70–10–20% and 80–10–10% for training, validation, and testing, respectively. We have set the performance of CNN (70% training–10% validation–20% testing) with 48 features as a benchmark for our work. In this work, we have achieved maximum accuracy of 98.70% with CNN. On the other hand, we have obtained 96.36% accuracy with the top 92 features of the dataset. We can see from the experimental results that if we could select the features properly then not only could the accuracy be improved but also the training and testing time of the model.

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