Smart MM

2019 ◽  
pp. 225-251
Author(s):  
Meenakshi Tripathi ◽  
Saatvik Shah ◽  
Prashant Bahal ◽  
Harsh Sharma ◽  
Ritika Gupta
Keyword(s):  
Artificial Intelligence ◽  
Emotion Recognition ◽  
Real Time ◽  
Management System ◽  
Recommendation System ◽  
Information Gain ◽  
Recognition System ◽  
Media Management ◽  
User Friendly ◽  
Made In

Rapid advancements have been made in the field of artificial intelligence in recent years. This has resulted in its adoption in various technologies from medicine to search engines. Existing media management systems have however not yet fully leveraged the power of artificial intelligence (AI) to give users enhanced information apart from basic media metadata. This chapter proposes a smart movie management system which works majorly offline and uses AI to deliver optimum information to the users on four vital tasks. These tasks are multilevel phrase level review polarity, plot and review keywords, a content-based recommendation system, and an emotion recognition system. The complete system works in near-real time with a user-friendly presentation to maximize a user's information gain.

scholarly journals Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

2021 ◽  
Vol 11 (22) ◽  
pp. 10540
Author(s):  
Navjot Rathour ◽  
Zeba Khanam ◽  
Anita Gehlot ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Input Device ◽  
Facial Emotions ◽  
Deep Network

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

scholarly journals A Smartphone-based Medication Self-management System with Real-time Medication Monitoring

2013 ◽  
Vol 04 (01) ◽  
pp. 37-52 ◽  
Author(s):  
Y. Uchimura ◽  
K. Omae ◽  
K. Waki ◽  
H. Fujita ◽  
K. Ohe ◽  
...  
Keyword(s):  
Real Time ◽  
Management System ◽  
Self Management ◽  
Prescription Data ◽  
Medication Regimen ◽  
Medication History ◽  
Medication Monitoring ◽  
Innovative Solution ◽  
New Type ◽  
User Friendly

SummaryBackground: Most patients cannot remember their entire medication regimen and occasionally forget to take their medication.Objectives: The objective of the study was to design, develop, and demonstrate the feasibility of a new type of medication self-management system using smartphones with real-time medication monitoring.Methods: We designed and developed a smartphone-based medication self-management system (SMSS) based on interviews of 116 patients. The system offered patients two main functions by means of smartphones: (1) storage and provision of an accurate, portable medication history and medication-taking records of patients; and (2) provision of a reminder to take medication only when the patient has forgotten to take his/her medication. These functions were realized by two data input methods: (a) reading of prescription data represented in two-dimensional barcodes using the smartphone camera and getting the photographic images of the pills; and (b) real-time medication monitoring by novel user-friendly wireless pillboxes.Results: Interviews suggested that a pocket-sized pillbox was demanded to support patient’s medication-taking outside the home and pillboxes for home use should be adaptable to the different means of pillbox storage. In accordance with the result, we designed and developed SMSS. Ten patients participated in the feasibility study. In 17 out of 47 cases (36.2%), patients took their medication upon being presented with reminders by the system. Correct medication-taking occur-rence was improved using this system.Conclusions: The SMSS is acceptable to patients and has the advantage of supporting ubiquitous medication self-management using a smartphone. We believe that the proposed system is feasible and provides an innovative solution to encourage medication self-management.Citation: Hayakawa M, Uchimura Y, Omae K, Waki K, Fujita H, Ohe K. A smartphone-based medication selfmanagement system with real-time medication monitoring. Appl Clin Inf 2013; 4: 37–52http://dx.doi.org/10.4338/ACI-2012-10-RA-0045

Real-Time Electroencephalography-Based Emotion Recognition System

2016 ◽  
Vol 11 (5) ◽  
pp. 456 ◽  
Author(s):  
Riyanarto Sarno ◽  
Muhammad Nadzeri Munawar ◽  
Brilian T. Nugraha
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Recognition System

scholarly journals Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5015
Author(s):  
Muhammad Anas Hasnul ◽  
Nor Azlina Ab. Ab.Aziz ◽  
Salem Alelyani ◽  
Mohamed Mohana ◽  
Azlan Abd. Abd. Aziz
Keyword(s):  
Artificial Intelligence ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Review Paper ◽  
Recognition System ◽  
Healthcare Systems ◽  
Multimodal Approach ◽  
Health And Wellbeing ◽  
The Subject ◽  
Recognition Systems

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

scholarly journals AutisMitr: Emotion Recognition Assistive Tool for Autistic Children

2020 ◽  
Vol 10 (1) ◽  
pp. 259-269
Author(s):  
Akansha Singh ◽  
Surbhi Dewan
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Facial Feature ◽  
Recognition System ◽  
Face Identification ◽  
Positive Outcomes ◽  
Autistic Children ◽  
Facial Feature Extraction ◽  
Three Stages

AbstractAssistive technology has proven to be one of the most significant inventions to aid people with Autism to improve the quality of their lives. In this study, a real-time emotion recognition system for autistic children has been developed. Emotion recognition is implemented by executing three stages: Face identification, Facial Feature extraction, and feature classification. The objective is to frame a system that includes all three stages of emotion recognition activity that executes expeditiously in real time. Thus, Affectiva SDK is implemented in the application. The propound system detects at most 7 facial emotions: anger, disgust, fear, joy, sadness, contempt, and surprise. The purpose for performing this study is to teach emotions to individuals suffering from autism, as they lack the ability to respond appropriately to others emotions. The proposed application was tested with a group of typical children aged 6–14 years, and positive outcomes were achieved.

scholarly journals IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1289
Author(s):  
Navjot Rathour ◽  
Sultan S. Alshamrani ◽  
Rajesh Singh ◽  
Anita Gehlot ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Real Time ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Human Beings ◽  
Emotion Detection ◽  
Resource Constrained

Facial emotion recognition (FER) is the procedure of identifying human emotions from facial expressions. It is often difficult to identify the stress and anxiety levels of an individual through the visuals captured from computer vision. However, the technology enhancements on the Internet of Medical Things (IoMT) have yielded impressive results from gathering various forms of emotional and physical health-related data. The novel deep learning (DL) algorithms are allowing to perform application in a resource-constrained edge environment, encouraging data from IoMT devices to be processed locally at the edge. This article presents an IoMT based facial emotion detection and recognition system that has been implemented in real-time by utilizing a small, powerful, and resource-constrained device known as Raspberry-Pi with the assistance of deep convolution neural networks. For this purpose, we have conducted one empirical study on the facial emotions of human beings along with the emotional state of human beings using physiological sensors. It then proposes a model for the detection of emotions in real-time on a resource-constrained device, i.e., Raspberry-Pi, along with a co-processor, i.e., Intel Movidius NCS2. The facial emotion detection test accuracy ranged from 56% to 73% using various models, and the accuracy has become 73% performed very well with the FER 2013 dataset in comparison to the state of art results mentioned as 64% maximum. A t-test is performed for extracting the significant difference in systolic, diastolic blood pressure, and the heart rate of an individual watching three different subjects (angry, happy, and neutral).

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