scholarly journals Qualitative and Quantitative Spatio-temporal Relations in Daily Living Activity Recognition

2015 ◽  
pp. 115-130 ◽  
Author(s):  
Jawad Tayyub ◽  
Aryana Tavanai ◽  
Yiannis Gatsoulis ◽  
Anthony G. Cohn ◽  
David C. Hogg
Keyword(s):  
Activity Recognition ◽  
Daily Living ◽  
Temporal Relations ◽  
Qualitative And Quantitative ◽  
Daily Living Activity ◽  
Spatio Temporal

Daily living activity recognition based on statistical feature quality group selection

2012 ◽  
Vol 39 (9) ◽  
pp. 8013-8021 ◽  
Author(s):  
Oresti Banos ◽  
Miguel Damas ◽  
Hector Pomares ◽  
Alberto Prieto ◽  
Ignacio Rojas
Keyword(s):  
Activity Recognition ◽  
Daily Living ◽  
Group Selection ◽  
Statistical Feature ◽  
Daily Living Activity

scholarly journals Realtime Online Daily Living Activity Recognition Using Head-Mounted Display

2017 ◽  
Vol 11 (3) ◽  
pp. 67 ◽  
Author(s):  
Fais Al Huda ◽  
Herman Tolle ◽  
Rosa Andrie Asmara
Keyword(s):  
Activity Recognition ◽  
Daily Living ◽  
Great Accuracy ◽  
Display Device ◽  
Google Glass ◽  
Daily Living Activity ◽  
Head Mounted Display ◽  
Everyday Activities ◽  
Research Fields ◽  
The Military

Human activity recognition is one of the popular research fields. The results of this study can be applied to many other fields such as the military, commercialism, and health. With the advent of the wearable head mounted display device mainly like google glass raises the possibility of this research. In this study tries to identify everyday activities are often called the ambient activity. Development of the system is done online using a smartphone and a head mounted display. The system produces an accuracy above 90%, which can be concluded that the system was able to recognize the activities with great accuracy.

scholarly journals Learning Dynamic Spatio-Temporal Relations for Human Activity Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 130340-130352
Author(s):  
Zhenyu Liu ◽  
Yaqiang Yao ◽  
Yan Liu ◽  
Yuening Zhu ◽  
Zhenchao Tao ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Temporal Relations ◽  
Learning Dynamic ◽  
Spatio Temporal

scholarly journals Fall Detection and Daily Living Activity Recognition using Machine Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 328-332
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Angular Velocity ◽  
Activity Recognition ◽  
Daily Living ◽  
Fall Detection ◽  
Classification Algorithm ◽  
Support Vector ◽  
Velocity Data ◽  
Daily Living Activity

Elder people are increasing all over the world as a result certain fall occur in their daily life. This fall lead to several severe problems. The fall may often causes injuries and in many cases it result in death of the individual. The problem should be addressed to reduce the fall. By using some Machine Learning(ML) algorithm the fall and daily living activities are recognized. The acceleration and angular velocity data obtained from the dataset are used to detect the fall and daily living activity. Body movement of the person are collected and stored in the dataset. Acceleration and angular velocity data are used to extract the time and frequency domain feature and provide them to classification algorithm. Here, Logistic regression algorithm is used for detecting the fall and living activity. It is very effective algorithm and does not require too many computational resources. It is easy to regularize and provide well calibrated predicted probabilities as output. The sensitivity, accuracy and specificity of fall detection and activity recognition is obtained as a result. The performance evaluation is made with three classification algorithm. The three classification algorithm are Artificial neural network (ANN), K-nearest neighbours (KNN), Quadratic support vector machine (QSVM). Logistic regression provides highest accuracy compared with other three algorithm.

Sthoulya w.s.r. to Obesity Management - A Case Study

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Dr. Navyashree KL ◽  
Dr. Manjunatha Adiga
Keyword(s):  
Daily Living ◽  
Quantitative Data ◽  
Metabolic Disorders ◽  
New World ◽  
Obesity Management ◽  
Qualitative And Quantitative ◽  
Results Management ◽  
Individual Disease

Background: New world syndrome like obesity is one of the world’s oldest metabolic disorders is heading for the rocks with various reasons like urbanization and modernization. Though obesity and over weight is not affecting much to a person for daily living, the complication of which has become burning issue in the society. Aim and Objective: To assess the efficacy of Shamanoushadi along with Pathya Ahara Vihara. Methods: Guduchi Bhadraadi Kashaya, Hareetaki Churna and Pathya Ahara Vihara. Results: Management of Sthoulya with Pathya Ahara Vihara along with Shamanoushadi was effective in all qualitative and quantitative data. Discussion: The drug possessing Rooksha Guna, Kaphahara property counteracts Sthoulya. Conclusion: Our classics with variety of treatment to individual disease suggest the stages or condition that disease can cross over

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