scholarly journals CSL-SHARE: A Multimodal Wearable Sensor-Based Human Activity Dataset

2021 ◽  
Vol 3 ◽  
Author(s):  
Hui Liu ◽  
Yale Hartmann ◽  
Tanja Schultz
Keyword(s):  
Human Activity ◽  
Wearable Sensor

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%.

Imaging and fusing time series for wearable sensor-based human activity recognition

2020 ◽  
Vol 53 ◽  
pp. 80-87 ◽  
Author(s):  
Zhen Qin ◽  
Yibo Zhang ◽  
Shuyu Meng ◽  
Zhiguang Qin ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Time Series ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Wearable Sensor

scholarly journals Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Huaijun Wang ◽  
Jing Zhao ◽  
Junhuai Li ◽  
Ling Tian ◽  
Pengjia Tu ◽  
...  
Keyword(s):  
Health Care ◽  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Specific Activity ◽  
Recognition Rate ◽  
Human Activity Recognition ◽  
Wearable Sensor ◽  
Identification Rate

Human activity recognition (HAR) can be exploited to great benefits in many applications, including elder care, health care, rehabilitation, entertainment, and monitoring. Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.

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