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

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%.

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An Improved Ensemble Machine Learning Algorithm for Wearable Sensor Data Based Human Activity Recognition

Springer Series in Reliability Engineering - Reliability and Statistical Computing ◽

10.1007/978-3-030-43412-0_13 ◽

2020 ◽

pp. 207-228

Author(s):

Huu Du Nguyen ◽

Kim Phuc Tran ◽

Xianyi Zeng ◽

Ludovic Koehl ◽

Guillaume Tartare

Keyword(s):

Machine Learning ◽

Activity Recognition ◽

Human Activity ◽

Learning Algorithm ◽

Human Activity Recognition ◽

Sensor Data ◽

Machine Learning Algorithm ◽

Wearable Sensor ◽

Ensemble Machine Learning

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A survey on wearable sensor modality centred human activity recognition in health care

Expert Systems with Applications ◽

10.1016/j.eswa.2019.04.057 ◽

2019 ◽

Vol 137 ◽

pp. 167-190 ◽

Cited By ~ 15

Author(s):

Yan Wang ◽

Shuang Cang ◽

Hongnian Yu

Keyword(s):

Health Care ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Wearable Sensor

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Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning

Sensors ◽

10.3390/s21082760 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2760

Author(s):

Seungmin Oh ◽

Akm Ashiquzzaman ◽

Dongsu Lee ◽

Yeonggwang Kim ◽

Jinsul Kim

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Activity Recognition ◽

Human Activity ◽

Random Sampling ◽

Data Gathering ◽

Human Activity Recognition ◽

Learning Models ◽

Active Transfer ◽

Existing Data

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.

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Human Activity Recognition of Children with Wearable Devices Using LightGBM Machine Learning

10.21203/rs.3.rs-1076792/v1 ◽

2021 ◽

Author(s):

Gábor Csizmadia ◽

Krisztina Liszkai-Peres ◽

Bence Ferdinandy ◽

Ádám Miklósi ◽

Veronika Konok

Keyword(s):

Machine Learning ◽

Activity Recognition ◽

Human Activity ◽

Learning Algorithm ◽

Binary Classification ◽

Wearable Sensors ◽

Window Size ◽

Human Activity Recognition ◽

Daily Routine ◽

Light Gradient

Abstract Human activity recognition (HAR) using machine learning (ML) methods is a relatively new method for collecting and analyzing large amounts of human behavioral data using special wearable sensors. Our main goal was to find a reliable method which could automatically detect various playful and daily routine activities in children. We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. We analyzed the data of 34 children (19 girls, 15 boys; age range: 6.59 – 8.38; median age = 7.47). All children were typically developing first graders from three elementary schools. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM)learning algorithm, a decision based method with a 3-fold cross validation. We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. The window size had no significant effect. The overall accuracy was 0.95, which is at the top segment of the previously published similar HAR data. In summary, the LGBM is a very promising solution for HAR. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective.

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