scholarly journals A Portable Smart Fitness Suite for Real-Time Exercise Monitoring and Posture Correction

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6692
Author(s):  
Abdul Hannan ◽  
Muhammad Zohaib Shafiq ◽  
Faisal Hussain ◽  
Ivan Miguel Pires
Keyword(s):  
Real Time ◽  
Wearable Sensors ◽  
Well Being ◽  
Classification Model ◽  
Android Application ◽  
Permanent Disability ◽  
Technological Advances ◽  
Knn Classification ◽  
Android App ◽  
Smart Fitness

Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature.

scholarly journals Networking Android App for Sociality

2019 ◽  
pp. 1026-1032
Author(s):  
R. Rajadurai ◽  
S. V. Bhalaji ◽  
M. Puvanesh ◽  
S. Raagul ◽  
R. Aravind Kumar
Keyword(s):  
Real Time ◽  
Android Application ◽  
The Real ◽  
Android App ◽  
Networking Protocols

This paper we described about the technologies and working of the real time android application that is completely based on the networking protocols. The application that we are constructing is based on the modules, databases and a real time server. The application that we have built must enraged in the easiest methods and strategies for the benefit in society.

scholarly journals Prototype of Smart Home and Monitoring Application Based On Internet of Things (IoT) Using Android

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Andika Fajar Isnanto ◽  
Atikah Surriani ◽  
Sri Lestari ◽  
Unan Yusmaniar Oktiawati
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Smart Home ◽  
The Internet ◽  
Android Application ◽  
Communication Device ◽  
Home Based ◽  
Technological Advances ◽  
Monitoring Application ◽  
Internet Of Thing

Smart Home is one of the popular technological advances and developed by researchers or academics because of its high potential to be implemented in various fields. Smart Home is an Internet of Things based technology, which means that by connecting to the internet everyone can connect whenever and wherever they are. With the presence of smart home, it can simplify human problems and limitations. This paper describes the design and prototype of smart Home based on Internet of Things Applications on Android. There is an android application that functions to control and monitor the smart home. By using NodeMCU, is used as a line communication device between users and smartphones. The use of databases in Firebase makes the data on the smart home always real-time active because it is connected to Google servers. We also develop an android application; thus smart home can be controlled by its owner. Keywords: Smart Home, Internet of Thing, Android, Firebase.

Android Application Based Real Time Home Automation

2011 ◽  
Vol 4 (7) ◽  
pp. 188-190 ◽  
Author(s):  
Kallakunta. Ravi Kumar ◽  
◽  
Shaik Akbar
Keyword(s):  
Real Time ◽  
Home Automation ◽  
Android Application

scholarly journals The Impact of SARS-CoV-2 (COVID-19) and its Lockdown Measures on the Mental and Functional Health of Older Individuals

2021 ◽  
Author(s):  
Maria Chiara Fastame ◽  
Ilaria Mulas ◽  
Valeria Putzu ◽  
Gesuina Asoni ◽  
Daniela Viale ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Wearable Sensors ◽  
Well Being ◽  
Functional Health ◽  
Cognitive Deterioration ◽  
Older Individuals ◽  
Physical And Mental Health ◽  
Psychological Well Being ◽  
The Impact ◽  
Healthy Participants

AbstractThe effect of the COVID-19 on the physical and mental health of Italian older individuals displaying signs of cognitive deterioration has not been deeply investigated. This longitudinal study examined the impact of COVID-19 lockdown measures on the psychological well-being and motor efficiency of a sample of Italian community-dwellers with and without cognitive decline. Forty-seven participants underwent instrumental gait analysis performed in ecological setting using wearable sensors, and completed a battery of tasks assessing cognitive functioning and psychological well-being, before and after the full lockdown due to the COVID-19 spreading. A series of Multivariate Analyses of Variance (MANOVAs) documented that the superior gait performance of the cognitively healthy participants exhibited before the COVID-19 spread, vanished when they were tested at the end of the lockdown period. Moreover, before the outbreak of the COVID-19, cognitively healthy participants and those with signs of cognitive decline reported similar levels of psychological well-being, whereas, after the lockdown, the former group reported better coping, emotional competencies, and general well-being than the participants displaying signs of cognitive decline. In conclusion, the full COVID-19 outbreak had a significant impact on the mental and motor functioning of older individuals with and without signs of cognitive deterioration living in Italy.

scholarly journals Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4916
Author(s):  
Ali Usman Gondal ◽  
Muhammad Imran Sadiq ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Smart Cities ◽  
Classification Model ◽  
Rapid Urbanization ◽  
Metal Waste ◽  
Waste Classification ◽  
The City

Urbanization is a big concern for both developed and developing countries in recent years. People shift themselves and their families to urban areas for the sake of better education and a modern lifestyle. Due to rapid urbanization, cities are facing huge challenges, one of which is waste management, as the volume of waste is directly proportional to the people living in the city. The municipalities and the city administrations use the traditional wastage classification techniques which are manual, very slow, inefficient and costly. Therefore, automatic waste classification and management is essential for the cities that are being urbanized for the better recycling of waste. Better recycling of waste gives the opportunity to reduce the amount of waste sent to landfills by reducing the need to collect new raw material. In this paper, the idea of a real-time smart waste classification model is presented that uses a hybrid approach to classify waste into various classes. Two machine learning models, a multilayer perceptron and multilayer convolutional neural network (ML-CNN), are implemented. The multilayer perceptron is used to provide binary classification, i.e., metal or non-metal waste, and the CNN identifies the class of non-metal waste. A camera is placed in front of the waste conveyor belt, which takes a picture of the waste and classifies it. Upon successful classification, an automatic hand hammer is used to push the waste into the assigned labeled bucket. Experiments were carried out in a real-time environment with image segmentation. The training, testing, and validation accuracy of the purposed model was 0.99% under different training batches with different input features.

scholarly journals A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1104
Author(s):  
Shin-Yan Chiou ◽  
Kun-Ju Lin ◽  
Ya-Xin Dong
Keyword(s):  
Control System ◽  
Real Time ◽  
Human Error ◽  
Dynamic Scheduling ◽  
Wearable Sensors ◽  
Time Data ◽  
Scan Time ◽  
Number Of Patients ◽  
And Control ◽  
Automatic Scheduling

Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families.

scholarly journals Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 20
Author(s):  
Reynaldo Villarreal-González ◽  
Antonio J. Acosta-Hoyos ◽  
Jaime A. Garzon-Ochoa ◽  
Nataly J. Galán-Freyle ◽  
Paola Amar-Sepúlveda ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Real Time ◽  
Binary Classification ◽  
Simulated Data ◽  
Classification Model ◽  
Rt Pcr ◽  
Simon Bolivar ◽  
Polymerase Chain ◽  
Available Information

Real-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.

Joy in the Workplace: A Scoping Review of Positive Emotion and Electroencephalogram

2020 ◽  
Vol 64 (1) ◽  
pp. 1493-1497
Author(s):  
Yaqoub Yusuf ◽  
Jodi Boutte’ ◽  
Asante’ Lloyd ◽  
Emma Fortune ◽  
Renaldo C. Blocker
Keyword(s):  
Real Time ◽  
Scoping Review ◽  
Positive Emotion ◽  
Positive Emotions ◽  
Wearable Sensors ◽  
Recent Advances ◽  
Electrode Systems ◽  
Electroencephalogram Eeg ◽  
Occupational Settings

A workplace that is a conduit for positive emotions can be important to employees retention and can contribute optimal levels of productivity. Validated tools for examining emotions are primarily subjective and retrospective in nature. Recent advances in technology have led to more novel and passive ways of measuring emotions. Wearable sensors, such as electroencephalogram (EEG), are being explored to assess cognitive and physical burdens objectively and in real-time. Therefore, there exists a need to investigate and validate the use of EEG to examine emotions objectively and in real-time. In this paper, we conducted a scoping review of EEG to measure positive emotions and/or indicators of joy in the workplace. Our review results in 22 articles that employ EEG to study joy in occupational settings. Three major themes identified in the analysis include (1) EEG for symptoms detection and outcomes, (2) Populations studied using EEG, and (3) EEG electrode systems.

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