Machine learning methods for fully automatic recognition of facial expressions and facial actions

2005 ◽  
Author(s):  
M.S. Bartlett ◽  
G. Littlewort ◽  
C. Lainscsek ◽  
I. Fasel ◽  
J. Movellan
Keyword(s):  
Machine Learning ◽  
Facial Expressions ◽  
Automatic Recognition ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Recognition Of Facial Expressions ◽  
Fully Automatic

scholarly journals Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 778
Author(s):  
Vasco Ponciano ◽  
Ivan Miguel Pires ◽  
Fernando Reinaldo Ribeiro ◽  
Gonçalo Marques ◽  
Maria Vanessa Villasana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Inertial Sensors ◽  
Automatic Recognition ◽  
Learning Methods ◽  
Less Invasive ◽  
Machine Learning Methods ◽  
The Future

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

scholarly journals A review on the artificial intelligence algorithms for the recognition of Activities of Daily Living using sensors in mobile devices

2018 ◽  
Author(s):  
Ivan Miguel Serrano Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Eftim Zdravevski ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Automatic Recognition ◽  
Acoustic Sensors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Different Types

The automatic recognition of Activities of Daily Living (ADL) with a multi-sensor mobile device that can acquire different types of sensors' data, and rely on the use of machine learning methods to handle the recognition of ADL with reliable accuracy. This paper focuses on the literature review of the existing methods to make the identification of ADL in order to assess the efficiency of the different methods for the identification of ADL and their environments using off-the-shelf mobile devices. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment. Therefore, the main purpose of this study is to present a review of the machine learning methods already used on this field, relating them with the accuracy and number of ADL recognized.

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