scholarly journals Joint Selection using Deep Reinforcement Learning for Skeleton-based Activity Recognition

2021 ◽  
Author(s):  
Bahareh Nikpour ◽  
Narges Armanfard
Keyword(s):  
Reinforcement Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Three Dimensional ◽  
Human Activity Recognition ◽  
Selection Method ◽  
The Body ◽  
Wide Range ◽  
Markov Decision ◽  
Body Joints

<div>Skeleton based human activity recognition has attracted lots of attention due to its wide range of applications. Skeleton data includes two or three dimensional coordinates of body joints. All of the body joints are not effective in recognizing different activities, so finding key joints within a video and across different activities has a significant role in improving the performance. In this paper we propose a novel framework that performs joint selection in skeleton video frames for the purpose of human activity recognition. To this end, we formulate the joint selection problem as a Markov Decision Process (MDP) where we employ deep reinforcement learning to find the most informative joints per frame. The proposed joint selection method is a general framework that can be employed to improve human activity classification methods. Experimental results on two benchmark activity recognition data sets using three different classifiers demonstrate effectiveness of the proposed joint selection method.</div>

scholarly journals Joint Selection using Deep Reinforcement Learning for Skeleton-based Activity Recognition

2021 ◽  
Author(s):  
Bahareh Nikpour ◽  
Narges Armanfard
Keyword(s):  
Reinforcement Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Three Dimensional ◽  
Human Activity Recognition ◽  
Selection Method ◽  
The Body ◽  
Wide Range ◽  
Markov Decision ◽  
Body Joints

<div>Skeleton based human activity recognition has attracted lots of attention due to its wide range of applications. Skeleton data includes two or three dimensional coordinates of body joints. All of the body joints are not effective in recognizing different activities, so finding key joints within a video and across different activities has a significant role in improving the performance. In this paper we propose a novel framework that performs joint selection in skeleton video frames for the purpose of human activity recognition. To this end, we formulate the joint selection problem as a Markov Decision Process (MDP) where we employ deep reinforcement learning to find the most informative joints per frame. The proposed joint selection method is a general framework that can be employed to improve human activity classification methods. Experimental results on two benchmark activity recognition data sets using three different classifiers demonstrate effectiveness of the proposed joint selection method.</div>

Unsupervised Human Activity Representation Learning with Multi-task Deep Clustering

2021 ◽  
Vol 5 (1) ◽  
pp. 1-25
Author(s):  
Haojie Ma ◽  
Zhijie Zhang ◽  
Wenzhong Li ◽  
Sanglu Lu
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Clustering Algorithm ◽  
Well Being ◽  
Human Activity Recognition ◽  
Feature Representation ◽  
Fine Tuning ◽  
Learning Approaches ◽  
Sensing Data ◽  
Wide Range

Human activity recognition (HAR) based on sensing data from wearable and mobile devices has become an active research area in ubiquitous computing, and it envisions a wide range of application scenarios in mobile social networking, environmental context sensing, health and well-being monitoring, etc. However, activity recognition based on manually annotated sensing data is manpower-expensive, time-consuming, and privacy-sensitive, which prevents HAR systems from being really deployed in scale. In this paper, we address the problem of unsupervised human activity recognition, which infers activities from unlabeled datasets without the need of domain knowledge. We propose an end-to-end multi-task deep clustering framework to solve the problem. Taking the unlabeled multi-dimensional sensing signals as input, we firstly apply a CNN-BiLSTM autoencoder to form a compressed latent feature representation. Then we apply a K-means clustering algorithm based on the extracted features to partition the dataset into different groups, which produces pseudo labels for the instances. We further train a deep neural network (DNN) with the latent features and pseudo labels for activity recognition. The tasks of feature representation, clustering, and classification are integrated into a uniform multi-task learning framework and optimized jointly to achieve unsupervised activity classification. We conduct extensive experiments based on three public datasets. It is shown that the proposed approach outperforms shallow unsupervised learning approaches, and it performs close to the state-of-the-art supervised approaches by fine-tuning with a small number of labeled data. The proposed approach significantly reduces the cost of human-based data annotation and narrows down the gap between unsupervised and supervised human activity recognition.

scholarly journals Human Activity Recognition

2020 ◽  
Vol 9 (7) ◽  
pp. 903-905
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Three Dimensional ◽  
Recognition System ◽  
Research Area ◽  
Human Activity Recognition ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
3D Cnn ◽  
Medical Healthcare

The topic of Human activity recognition (HAR) is a prominent research area topic in the field of computer vision and image processing area. It has empowered state-of-art application in multiple sectors, surveillance, digital entertainment and medical healthcare. It is interesting to observe and intriguing to predict such kind of movements. Several sensor-based approaches have also been introduced to study and predict human activities such accelerometer, gyroscope, etc., it has its own advantages and disadvantages.[10] In this paper, an intelligent human activity recognition system is developed. Convolutional neural network (CNN) with spatiotemporal three dimensional (3D) kernels are trained using Kinetics data set which has 400 classes that depicts activities of humans in their everyday life and work and consist of 400 and more videos for each class. The 3D CNN model used in this model is RESNET-34. The videos were temporally cut down and last around tenth of a second. The trained model show satisfactory performance in all stages of training, testing. Finally the results show promising activity recognition of over 400 human actions.

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.

Sensitivity of Sensor Locations in a Wearable IMU-based Human Activity Recognition System

2021 ◽  
Author(s):  
Mehdi Ejtehadi ◽  
Amin M. Nasrabadi ◽  
Saeed Behzadipour
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Subjects ◽  
Kinematic Model ◽  
Recognition System ◽  
Human Activity Recognition ◽  
The Body ◽  
Measurement Unit ◽  
Body Segments ◽  
Body Kinematic

Abstract Background: The advent of Inertial measurement unit (IMU) sensors has significantly extended the application domain of Human Activity Recognition (HAR) systems to healthcare, tele-rehabilitation & daily life monitoring. IMU’s are categorized as body-worn sensors and therefore their output signals and the HAR performance naturally depends on their exact location on the body segments. Objectives: This research aims to introduce a methodology to investigate the effects of misplacing the sensors on the performance of the HAR systems. Methods: The properly placed sensors and their misplaced variations were modeled on a human body kinematic model. The model was then actuated using measured motions from human subjects. The model was then used to run a sensitivity analysis. Results: The results indicated that the transverse misplacement of the sensors on the left arm and right thigh and the rotation of the left thigh sensor significantly decrease the rate of activity recognition. It was also shown that the longitudinal displacements of the sensors (along the body segments) have minor impacts on the HAR performance. A Monte Carlo simulation indicated that if the sensitive sensors are mounted with extra care, the performance can be maintained at a higher than 95% level.Conclusions: Accurate mounting of the IMU’s on the body impacts the performance of the HAR. Particularly, the transverse position and rotation of the IMU’s are more sensitive. The users of such systems need to be informed about the more sensitive sensors and directions to maintain an acceptable performance for the HAR.

scholarly journals Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Min-Cheol Kwon ◽  
Sunwoong Choi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Body Movement ◽  
Human Activity Recognition ◽  
Daily Activities ◽  
Whole Body ◽  
Wide Range ◽  
Artificial Neural

Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

scholarly journals Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3468 ◽  
Author(s):  
Tian ◽  
Zhang ◽  
Chen ◽  
Geng ◽  
Wang
Keyword(s):  
Ensemble Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
The Body ◽  
Swarm Optimization ◽  
Diversity Measure ◽  
Base Classifier ◽  
Glowworm Swarm Optimization ◽  
Selective Ensemble

Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.

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