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.

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 Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0209909 ◽  
Author(s):  
Shirin Enshaeifar ◽  
Ahmed Zoha ◽  
Severin Skillman ◽  
Andreas Markides ◽  
Sahr Thomas Acton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Urinary Tract Infection ◽  
Urinary Tract ◽  
Tract Infection ◽  
Daily Living ◽  
Learning Methods ◽  
Daily Living Activities ◽  
Machine Learning Methods ◽  
People With Dementia

scholarly journals Reviewing artificial intelligence methods for predictive modeling applications

2021 ◽  
Author(s):  
Andreas Sepp
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Modeling ◽  
Significant Contribution ◽  
Predictive Analytics ◽  
State Of The Art ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Artificial Intelligence Methods

Artificial intelligence and machine learning methods had significant contribution to the advancement and progress of predictive analytics. This article presents a state of the art of methods and applications of artificial intelligence and machine learning.

A Review on the Importance of Machine Learning and Artificial Intelligence in Real Life Problem Solving

2020 ◽  
Vol 17 (9) ◽  
pp. 4336-4339
Author(s):  
D. S. V. Suma Priya ◽  
D. Esther Rani ◽  
A. Pavan Shankar Sai ◽  
A. Konda Babu ◽  
Durgesh Nandan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Problem Solving ◽  
Concept Learning ◽  
Real Life ◽  
Learning System ◽  
Level System ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Real Life Problem

This paper clearly explains the concept, importance and main aim of machine learning and construction of the machine learning system. There are several ideas regarding this machine learning which are formed by a number of strategies. This effort leads to introduce many machine learning methods such as learning by commands, concept, learning by comparison, and learning by some algorithms. This article provides information about the main purpose of machine learning and its development. Machine learning is the primary aspect that promotes any system to have intelligence. One of its main applications is artificial intelligence. Machine learning is highly suited for complex level system representation. There are a number of machine learning concepts that leads to the integration of number of networks.

Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods

2019 ◽  
Vol 212 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Guy S. Handelman ◽  
Hong Kuan Kok ◽  
Ronil V. Chandra ◽  
Amir H. Razavi ◽  
Shiwei Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Evaluation Metrics ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals POSSIBILITIES OF APPLICATION MACHINE LEARNING METHODS IN SOLVING ANALYTICAL PROBLEMS IN METALLURGICAL INDUSTRY

2021 ◽  
Vol 9 (3) ◽  
pp. 61-65
Author(s):  
Diana Yusupova ◽  
Sergey Muzalev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Metallurgical Industry ◽  
Learning Opportunities ◽  
High Tech ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Non Linear ◽  
A Company

Background. Machine learning is a promising field for organization in the age of development of high-tech methods of management and organization of the company. As a rule, this term is used in relation to artificial intelligence, namely, machines that could learn independently. Thus, the main goal of this work is to assess the prospects for using these methods for solving various problems in a corporation. Methods. The article introduces the main methods of machine learning, their analysis, linear and non-linear learning methods are given, their use in practice is indicated, and the key advantages of using a trained artificial intelligence in a company are identified. Result. As a result, the author proposes ways of using machine learning methods in a firm, analyzes their advantages and disadvantages, identifies the problems of implementing artificial intelligence learning opportunities in practice.

scholarly journals Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Mobile Devices’ Sensors

2018 ◽  
Author(s):  
Ivan Miguel Pires ◽  
Nuno M. Garcia ◽  
Nuno Pombo ◽  
Francisco Flórez-Revuelta ◽  
Susanna Spinsante
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Data Fusion ◽  
Data Processing ◽  
Activities Of Daily Living ◽  
Mobile Devices ◽  
Daily Living ◽  
Automatic Identification ◽  
Internet Connection ◽  
Processing Data

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.

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