scholarly journals mmFiT: Contactless Fitness Tracker Using mmWave Radar and Edge Computing Enabled Deep Learning

2021 ◽  
Author(s):  
Girish Tiwari ◽  
Parveen Bajaj ◽  
Shalabh Gupta
Keyword(s):  
Real Time ◽  
Privacy Preservation ◽  
Healthy Lifestyle ◽  
Skin Irritation ◽  
Edge Computing ◽  
User Privacy ◽  
Cloud Data ◽  
Fitness Tracker ◽  
Time Accuracy ◽  
Fitness Trackers

Internet of things (IoT) is transforming the way we imagine healthcare with ubiquitous connectivity, faster response and deeper personalized insights using large amounts of data. Fitness trackers provide useful insights to maintain balance of a healthy lifestyle. Nowadays, fitness trackers are available as wearable devices which creates a sense of unease in exercise and may cause skin irritation. In this paper, we present mmFiT, an edge computing enabled, contactless, real-time fitness tracker using a single mmwave radar point cloud data. It has the inherent advantage of user privacy preservation while tracking indoor fitness activities. Experimental results show that the system can classify various exercises with real-time accuracy of 95.53\% and is also capable of counting repetitions of exercises. This implementation is computationally inexpensive, and therefore, the system can be deployed in an IoT connected edge device for real-time operations. This system will be an ideal fit in a smart home or smart gymnasium setting.

scholarly journals mmFiT: Contactless Fitness Tracker Using mmWave Radar and Edge Computing Enabled Deep Learning

2021 ◽  
Author(s):  
Girish Tiwari ◽  
Parveen Bajaj ◽  
Shalabh Gupta
Keyword(s):  
Real Time ◽  
Privacy Preservation ◽  
Healthy Lifestyle ◽  
Skin Irritation ◽  
Edge Computing ◽  
User Privacy ◽  
Cloud Data ◽  
Fitness Tracker ◽  
Time Accuracy ◽  
Fitness Trackers

Internet of things (IoT) is transforming the way we imagine healthcare with ubiquitous connectivity, faster response and deeper personalized insights using large amounts of data. Fitness trackers provide useful insights to maintain balance of a healthy lifestyle. Nowadays, fitness trackers are available as wearable devices which creates a sense of unease in exercise and may cause skin irritation. In this paper, we present mmFiT, an edge computing enabled, contactless, real-time fitness tracker using a single mmwave radar point cloud data. It has the inherent advantage of user privacy preservation while tracking indoor fitness activities. Experimental results show that the system can classify various exercises with real-time accuracy of 95.53\% and is also capable of counting repetitions of exercises. This implementation is computationally inexpensive, and therefore, the system can be deployed in an IoT connected edge device for real-time operations. This system will be an ideal fit in a smart home or smart gymnasium setting.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

scholarly journals An edge based hybrid intrusion detection framework for mobile edge computing

2021 ◽  
Author(s):  
Ashish Singh ◽  
Kakali Chatterjee ◽  
Suresh Chandra Satapathy
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Network Architecture ◽  
Denial Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Hybrid Detection ◽  
Game Theoretical Approach ◽  
Improved Performance ◽  
Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

scholarly journals Gamified Wearable Fitness Tracker for Physical Activity: A Comprehensive Literature Review

2021 ◽  
Vol 13 (13) ◽  
pp. 7017
Author(s):  
Inje Cho ◽  
Kyriaki Kaplanidou ◽  
Shintaro Sato
Keyword(s):  
Physical Activity ◽  
Literature Review ◽  
Health Outcomes ◽  
Physical Inactivity ◽  
Moderate Amount ◽  
Point Of Interest ◽  
Comprehensive Literature Review ◽  
Fitness Tracker ◽  
Fitness Trackers ◽  
Sustainable Health

Recently, gamified wearable fitness trackers have received greater attention and usage among sport consumers. Although a moderate amount of aerobic physical activity can significantly reduce the risk of many serious illnesses, physical inactivity issues are still prominent. Although wearable fitness trackers have the potential to contribute to physical activity engagement and sustainable health outcomes, there are dwindling engagement and discontinuance issues. Thus, examining its gamification elements and role in physical activity becomes critical. This study examined the gamification elements in wearable fitness trackers and their role in physical activity and sports engagement. A comprehensive literature review yielded 26 articles that empirically measured a variety of gamification features and the effect of the device on physical activity and sports engagement. The study suggests three key gamification themes: goal-based, social-based, and rewards-based gamification that can be a point of interest for future scholars and practitioners. Based on the review, we propose a conceptual framework that embraces motivational affordances and engagement in physical activity and sports.

scholarly journals Prospects of the WSR-88D Radar for Cloud Studies

2011 ◽  
Vol 50 (4) ◽  
pp. 859-872 ◽  
Author(s):  
Valery M. Melnikov ◽  
Dusan S. Zrnić ◽  
Richard J. Doviak ◽  
Phillip B. Chilson ◽  
David B. Mechem ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Three Dimensional ◽  
High Sensitivity ◽  
Doppler Velocity ◽  
Cloud Data ◽  
Three Dimensional Mapping ◽  
Spectrum Width ◽  
Differential Reflectivity ◽  
Dimensional Mapping

AbstractSounding of nonprecipitating clouds with the 10-cm wavelength Weather Surveillance Radar-1988 Doppler (WSR-88D) is discussed. Readily available enhancements to signal processing and volume coverage patterns of the WSR-88D allow observations of a variety of clouds with reflectivities as low as −25 dBZ (at a range of 10 km). The high sensitivity of the WSR-88D, its wide velocity and unambiguous range intervals, and the absence of attenuation allow accurate measurements of the reflectivity factor, Doppler velocity, and spectrum width fields in clouds to ranges of about 50 km. Fields of polarimetric variables in clouds, observed with a research polarimetric WSR-88D, demonstrate an abundance of information and help to resolve Bragg and particulate scatter. The scanning, Doppler, and polarimetric capabilities of the WSR-88D allow real-time, three-dimensional mapping of cloud processes, such as transformations of hydrometeors between liquid and ice phases. The presence of ice particles is revealed by high differential reflectivities and the lack of correlation between reflectivity and differential reflectivity in clouds in contrast to that found for rain. Pockets of high differential reflectivities are frequently observed in clouds; maximal values of differential reflectivity exceed 8 dB, far above the level observed in rain. The establishment of the WSR-88D network consisting of 157 polarimetric radars can be used to collect cloud data at any radar site, making the network a potentially powerful tool for climatic studies.

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