scholarly journals A Human Activity Recognition System Using Skeleton Data from RGBD Sensors

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Enea Cippitelli ◽  
Samuele Gasparrini ◽  
Ennio Gambi ◽  
Susanna Spinsante
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Cost Effective ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Recognition Algorithm ◽  
Support Vector

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

scholarly journals Human activity recognition via smart-belt in wireless body area networks

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771984935 ◽  
Author(s):  
Yuhong Zhu ◽  
Jingchao Yu ◽  
Fengye Hu ◽  
Zhijun Li ◽  
Zhuang Ling
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Recognition System ◽  
Body Area Networks ◽  
Wireless Body Area Networks ◽  
Human Activity Recognition ◽  
Support Vector ◽  
Body Area ◽  
Data Collector ◽  
Multi Classification

Human activity recognition based on wireless body area networks plays an essential role in various applications such as health monitoring, rehabilitation, and physical training. Currently, most of the human activity recognition is based on smartphone, and it provides more possibilities for this task with the rapid proliferation of wearable devices. To obtain satisfactory accuracy and adapt to various scenarios, we built a smart-belt which embedded the VG350 as posture data collector. This article proposes a hierarchical activity recognition structure, which divides the recognition process into two levels. Then a multi-classification Support Vector Machine algorithm optimized by Particle Swarm Optimization is applied to identify five kinds of conventional human postures. And we compare the effectiveness of triaxial accelerometer and gyroscope when used together and separately. Finally, we conduct systematic performance analysis. Experimental results show that our overall classification accuracy is 92.3% and the F-Measure can reach 92.63%, which indicates the human activity recognition system is accurate and effective.

scholarly journals Human Activity Recognition System

2021 ◽  
Vol 13 (0203) ◽  
pp. 91-96
Author(s):  
Pankaj Bhambri ◽  
Harpreet Kaur ◽  
Akarshit Gupta ◽  
Jaskaran Singh
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Good Accuracy ◽  
Cost Effective ◽  
Recognition System ◽  
Video Stream ◽  
Modern Human ◽  
Human Activity Recognition ◽  
End User ◽  
Driven System

Modern human activity recognAition systems are mainly trained and used upon video stream and images data that understand the features and actions variations in the data having similar or related movements. Human Activity Recognition plays a significant role in human-to-human and human-computer interaction. Manually driven system are highly time consuming and costlier. In this project, we aim at designing a cost-effective and faster Human Activity Recognition System which can process both video and image in order to recognize the activity being performed in it, thereby aiding the end user in various applications like surveillance, aiding purpose etc. This system will not only be cost effective but also as a utility-based system that can be incorporated in a large number of applications that will save time and aid in various activities that require recognition process, and save a lot of time with good accuracy Also, it will aid the blind people in availing the knowledge of their surroundings.

scholarly journals MHARS: Sistema Móvel de Reconhecimento de Atividades em Ambient Assisted Living

2015 ◽  
Author(s):  
J.D.P. Ribeiro Filho ◽  
F.J. Da Silva e Silva ◽  
L.R. Coutinho ◽  
B. Gomes
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Recognition System ◽  
Human Activity Recognition

O objetivo deste artigo é apresentar o MHARS (Mobile Human Activity Recognition System), um sistema móvel voltado para o acompanhamento de pacientes no contexto de Ambient Assisted Living (AAL), que permite o reconhecimento das atividades realizadas pelo usuário bem como a detecção da sua intensidade me tempo real. O MHARS foi projetado para poder obter dados de difererentes sensores, reconecer as atividades e medir sua intensidade em diferentes níveis de mobilidade do usuário, possui mecanismos para a inferência de situações relativas ao estado de saúde do paciente, bem como suporte à execução de ações de forma a poder reagir a eventos que mereçam a atenção por parte de seus cuidadores. Experimentos realizados demonstram que o MHARS possui boa acurácia e apresenta um consumo adequado de recursos do dispositivo móvel.

scholarly journals Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 885 ◽  
Author(s):  
Zhongzheng Fu ◽  
Xinrun He ◽  
Enkai Wang ◽  
Jun Huo ◽  
Jian Huang ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Sensor Node ◽  
Learning Algorithm ◽  
Joint Probability ◽  
Human Activity Recognition ◽  
Sensor Technology ◽  
Measurement Unit ◽  
Wearable Sensor

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.

scholarly journals Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 692
Author(s):  
Jingcheng Chen ◽  
Yining Sun ◽  
Shaoming Sun
Keyword(s):  
Feature Selection ◽  
Activity Recognition ◽  
Human Activity ◽  
Joint Angle ◽  
Recognition Performance ◽  
Wearable Sensors ◽  
Estimation Algorithm ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Time Frequency

Human activity recognition (HAR) is essential in many health-related fields. A variety of technologies based on different sensors have been developed for HAR. Among them, fusion from heterogeneous wearable sensors has been developed as it is portable, non-interventional and accurate for HAR. To be applied in real-time use with limited resources, the activity recognition system must be compact and reliable. This requirement can be achieved by feature selection (FS). By eliminating irrelevant and redundant features, the system burden is reduced with good classification performance (CP). This manuscript proposes a two-stage genetic algorithm-based feature selection algorithm with a fixed activation number (GFSFAN), which is implemented on the datasets with a variety of time, frequency and time-frequency domain features extracted from the collected raw time series of nine activities of daily living (ADL). Six classifiers are used to evaluate the effects of selected feature subsets from different FS algorithms on HAR performance. The results indicate that GFSFAN can achieve good CP with a small size. A sensor-to-segment coordinate calibration algorithm and lower-limb joint angle estimation algorithm are introduced. Experiments on the effect of the calibration and the introduction of joint angle on HAR shows that both of them can improve the CP.

scholarly journals Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

2021 ◽  
Author(s):  
Muhammad Muaaz ◽  
Ali Chelli ◽  
Martin Wulf Gerdes ◽  
Matthias Pätzold
Keyword(s):  
Neural Network ◽  
Information Systems ◽  
Convolutional Neural Network ◽  
Health Information ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Information Systems ◽  
Recognition System ◽  
Human Activity Recognition ◽  
Ratio Method

AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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