scholarly journals Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2436 ◽  
Author(s):  
Itsaso Rodríguez-Moreno ◽  
José María Martínez-Otzeta ◽  
Izaro Goienetxea ◽  
Igor Rodriguez-Rodriguez ◽  
Basilio Sierra
Keyword(s):  
Activity Recognition ◽  
Action Recognition ◽  
Spatial Patterns ◽  
Short Term Memory ◽  
Research Field ◽  
Short Term ◽  
Linear Discriminant ◽  
Common Spatial Patterns ◽  
Multidimensional Signal ◽  
Long Short Term Memory

Action recognition in robotics is a research field that has gained momentum in recent years. In this work, a video activity recognition method is presented, which has the ultimate goal of endowing a robot with action recognition capabilities for a more natural social interaction. The application of Common Spatial Patterns (CSP), a signal processing approach widely used in electroencephalography (EEG), is presented in a novel manner to be used in activity recognition in videos taken by a humanoid robot. A sequence of skeleton data is considered as a multidimensional signal and filtered according to the CSP algorithm. Then, characteristics extracted from these filtered data are used as features for a classifier. A database with 46 individuals performing six different actions has been created to test the proposed method. The CSP-based method along with a Linear Discriminant Analysis (LDA) classifier has been compared to a Long Short-Term Memory (LSTM) neural network, showing that the former obtains similar or better results than the latter, while being simpler.

scholarly journals Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 411
Author(s):  
Yunkai Zhang ◽  
Yinghong Tian ◽  
Pingyi Wu ◽  
Dongfan Chen
Keyword(s):  
Autism Spectrum Disorder ◽  
Action Recognition ◽  
Short Term Memory ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Short Term ◽  
Term Memory ◽  
Clinical Observations ◽  
The Neural Network ◽  
Long Short Term Memory

The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.

scholarly journals An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1635
Author(s):  
Leyuan Liu ◽  
Jian He ◽  
Keyan Ren ◽  
Jonathan Lungu ◽  
Yibin Hou ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Information Gain ◽  
Recognition Task ◽  
Human Activity Recognition ◽  
Short Term ◽  
Activity Model ◽  
Long Short Term Memory ◽  
Selection Of

Wearable sensor-based HAR (human activity recognition) is a popular human activity perception method. However, due to the lack of a unified human activity model, the number and positions of sensors in the existing wearable HAR systems are not the same, which affects the promotion and application. In this paper, an information gain-based human activity model is established, and an attention-based recurrent neural network (namely Attention-RNN) for human activity recognition is designed. Besides, the attention-RNN, which combines bidirectional long short-term memory (BiLSTM) with attention mechanism, was tested on the UCI opportunity challenge dataset. Experiments prove that the proposed human activity model provides guidance for the deployment location of sensors and provides a basis for the selection of the number of sensors, which can reduce the number of sensors used to achieve the same classification effect. In addition, experiments show that the proposed Attention-RNN achieves F1 scores of 0.898 and 0.911 in the ML (Modes of Locomotion) task and GR (Gesture Recognition) task, respectively.

scholarly journals Deep-BEJT: A New Human Activity Recognition System based on Beta Elliptical Joint Trajectory (BEJT) and Long Short Term Memory (LSTM)

2021 ◽  
Author(s):  
Ghabri Sawsen ◽  
Wael Ouarda ◽  
Houcine Boubaker ◽  
Mohamed Moncef Ben Khelifa ◽  
Adel Alimi
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Short Term ◽  
Term Memory ◽  
Journal Paper ◽  
Long Short Term Memory

Deep-BEJT: A New Human Activity Recognition System basedon Beta Elliptical Joint Trajectory (BEJT) and Long Short TermMemory (LSTM)<div>New journal paper</div>

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