Transition Activity Recognition System Based on Standard Deviation Trend Analysis

Shi, Junhao; Zuo, Decheng; Zhang, Zhan

doi:10.3390/s20113117

Cited by 13 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed system significantly surpassed the authors' prior works that used HMM and SVM with similar depth data and transition awareness [9], [10], [11]. While another sensor-based approach achieved transition-aware recognition [42], its accuracy was limited to 80%. Notably, while state-of-the-art hybrid DL recognition models [27], [50], [51] obtained high accuracy, they were not considered for the application with real-time processing, privacy concerns, or transition state recognition.…”

Section: ) System Comparisonmentioning

confidence: 81%

“…However, to implement this approach, the legs of the person must be clearly detected. Furthermore, an algorithm based on Standard Deviation Trend Analysis (STD-TA) of sensor data was proposed [42] for recognizing transition states, defined as the transitional process between two different basic actions. The evaluation of the real data yielded an accuracy of over 80% with their model.…”

Section: Transition-aware Action Recognitionsmentioning

confidence: 99%

See 1 more Smart Citation

Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface

Htet,

Zin,

Tin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The global rise in the elderly population, which presents challenges to healthcare systems owing to labor shortages in caregiving facilities, necessitates innovative solutions for elderly care services. Smart aging technologies such as robotic companions and digital home gadgets, offer a solution to these challenges by improving the elderly's quality of life and assisting caregivers. However, limitations in data privacy, real-time processing, and reliability often hinder the effectiveness of the existing technologies. Among these, privacy concerns are a major barrier to ensuring user trust and ethical implementation. Therefore, this study proposes a more effective approach for smart aging through elderly activity monitoring that prioritizes data privacy. The proposed system utilizes stereo depth cameras to monitor the activities of the elderly. Data were collected from real-world environments with the participation of six elderly individuals from a care center and hospital. This system focuses on recognizing common daily actions of the elderly including sitting, standing, lying down, and seated in a wheelchair. Additionally, it recognizes transition states (in-between actions such as changing from sitting to standing) that are crucial for assessing balance issues. By integrating motion information with a deep-learning architecture, the system achieved a high accuracy of 99.42% in recognizing daily actions in real-time. This high accuracy was maintained even with minimal data from new environments through transfer learning, and the adaptability of this model ensured its potential for real-world applications. For intuitive interaction between the caregivers and the system, a user-friendly graphical interface (GUI) was also designed in the proposed approach.

show abstract

Section: ) System Comparisonmentioning

confidence: 81%

Section: Transition-aware Action Recognitionsmentioning

confidence: 99%

Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface

Htet,

Zin,

Tin

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The choice of classifier aims to identify a method that has the highest classification accuracy for the collected datasets and for the given data processing environment (e.g., online vs. offline). The reviewed literature included a broad range of classifiers, from simple decision trees 18 , k-nearest neighbors 65 , support vector machines [91][92][93] , logistic regression 21 , naïve Bayes 94 , and fuzzy logic 64 to ensemble classifiers such as random forest 76 , XGBoost 95 , AdaBoost 45,96 , bagging 24 , and deep neural networks 48,60,82,[97][98][99] . Simple classifiers were frequently compared to find the best solution in the given measurement scenario 43,53,[100][101][102] .…”

Section: Activity Classificationmentioning

confidence: 99%

A systematic review of smartphone-based human activity recognition methods for health research

2021

View full text Add to dashboard Cite

Smartphones are now nearly ubiquitous; their numerous built-in sensors enable continuous measurement of activities of daily living, making them especially well-suited for health research. Researchers have proposed various human activity recognition (HAR) systems aimed at translating measurements from smartphones into various types of physical activity. In this review, we summarized the existing approaches to smartphone-based HAR. For this purpose, we systematically searched Scopus, PubMed, and Web of Science for peer-reviewed articles published up to December 2020 on the use of smartphones for HAR. We extracted information on smartphone body location, sensors, and physical activity types studied and the data transformation techniques and classification schemes used for activity recognition. Consequently, we identified 108 articles and described the various approaches used for data acquisition, data preprocessing, feature extraction, and activity classification, identifying the most common practices, and their alternatives. We conclude that smartphones are well-suited for HAR research in the health sciences. For population-level impact, future studies should focus on improving the quality of collected data, address missing data, incorporate more diverse participants and activities, relax requirements about phone placement, provide more complete documentation on study participants, and share the source code of the implemented methods and algorithms.

show abstract

“…Although postural transitions may not have an emphasizing effect on the system due to their short duration and lower incidence, the validity of this statement is dependent on the application prospects. Shi et al [46] proposed a standard deviation trend-analysis (STD-TA)-based architecture to recognize transition activities. For the dimensional reduction of features, only statistical features were extracted, and a conventional SVM was utilized for classifier training.…”

Section: Transition Activitiesmentioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities

Irfan

Anjum

Masood

et al. 2021

Sensors

View full text Add to dashboard Cite

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.

show abstract

Transition Activity Recognition System Based on Standard Deviation Trend Analysis

Cited by 13 publications

References 35 publications

Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface

Smarter Aging: Developing a Foundational Elderly Activity Monitoring System With AI and GUI Interface

A systematic review of smartphone-based human activity recognition methods for health research

A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities

Contact Info

Product

Resources

About