Edge Computing Framework for Wearable Sensor-Based Human Activity Recognition

Salkić, Semir; Ustundag, Baris Can; Uzunović, Tarik; Golubovic, Edin

doi:10.1007/978-3-030-24986-1_30

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…), and devices that can be attached to sports tools (e.g., shoes, tennis rackets, motorbikes, cars, etc.) (e.g., [ 46 , 47 , 48 , 49 , 50 ]). This sub-section described two sports applications developed by applying the Edgine–Measurify platform.…”

Section: Resultsmentioning

confidence: 99%

Assessing Versatility of a Generic End-to-End Platform for IoT Ecosystem Applications

Berta

Bellotti

Gloria

et al. 2022

Sensors

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Availability of efficient development tools for data-rich IoT applications is becoming ever more important. Such tools should support cross-platform deployment and seamless and effective applicability in a variety of domains. In this view, we assessed the versatility of an edge-to-cloud system featuring Measurify, a framework for managing smart things. The framework exposes to developers a set of measurement-oriented resources that can be used in different contexts. The tool has been assessed in the development of end-to-end IoT applications in six Electronic and Information Technologies Engineering BSc theses that have highlighted the potential of such a system, both from a didactic and a professional point of view. The main design abstractions of the system (i.e., generic sensor configuration, simple language with chainable operations for processing data on the edge, seamless WiFi/GSM communication) allowed developers to be productive and focus on the application requirements and the high-level design choices needed to define the edge system (microcontroller and its sensors), avoiding the large set-up times necessary to start a solution from scratch. The experience also highlighted some usability issues that will be addressed in an upcoming release of the system.

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Section: Resultsmentioning

confidence: 99%

Assessing Versatility of a Generic End-to-End Platform for IoT Ecosystem Applications

Berta

Bellotti

Gloria

et al. 2022

Sensors

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“…As we can see, both architectures follow the same general structure with diferent implementations. This common idea is the processing pipeline which is shared among multiple architectures for Edge Computing [40,162]. Thus, the processing pipeline consists of the following elements: data gathering, data preprocessing (homogenization, iltering, masking private information, etc.…”

Section: Edge Computing Architectures and Sotwarementioning

confidence: 99%

Edge Intelligence: Concepts, Architectures, Applications, and Future Directions

Mèndez

Bierzynski

Cuéllar

et al. 2022

ACM Trans. Embed. Comput. Syst.

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The name Edge Intelligence , also known as Edge AI , is a recent term used in the last few years to refer to the confluence of Machine Learning, or broadly speaking Artificial Intelligence, with Edge Computing. In this manuscript, we revise the concepts regarding Edge Intelligence, such as Cloud, Edge and Fog Computing, the motivation to use Edge Intelligence, and compare current approaches and analyze application scenarios. To provide a complete review of this technology, previous frameworks and platforms for Edge Computing have been discusses in this manuscript in order to provide the general view of the basis for Edge AI. Similarly, the emerging techniques to deploy Deep Learning (DL) models at the network edge, as well as specialized platforms and frameworks to do so, are review in this manuscript. These devices, techniques and frameworks are analyzed based on relevant criteria at the network edge such as latency, energy consumption and accuracy of the models to determine the current state of the art as well as current limitations of the proposed technologies. Because of this, it is possible to understand what are the current possibilities to efficiently deploy state-of-the-art DL models at the network edge based on technologies such as AI accelerators, Tensor Processing Units and techniques that include Federated Learning and Gossip Training. Finally, the challenges of Edge AI are discusses in the manuscript as well as the Future directions that can be extracted from the evolution of the Edge Computing and Internet of Things (IoT) approaches.

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“…The development of numerous AI based services in the field of IoT opened doors for cloudbased platforms [32]. However, power budget application, data collected through devices with limited bandwidth are the main challenges for cloud-based approaches [33]. Due to these application level limitations, the concept of Edge Computing is prospering [34].…”

Section: Edge Intelligencementioning

confidence: 99%

Towards supervised real-time human activity recognition on embedded equipment

Najeh

Lohr

Leduc³

2022

2022 IEEE International Workshop on Metrology for Living Environment (MetroLivEn)

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In recent years, real-time human activity recognition (HAR) has reached importance due to its applications in various domains such as assistive services for the elderly in smart buildings, monitoring, well-being, comfort and security. Various techniques, researched within the image processing and computer vision communities, have been established to recognize human activities in real-time, but all of them are based on wearable sensors and there is no much attention for ambient sensor based approaches. In the literature, deep learning (DL) is one of effective and cost-efficient supervised learning model and different architectures haves been investigated for real-time HAR. However, it still struggles with the quality of data as well as hardware implementation issues.This paper presents two contributions. Firstly, an intensive analysis of DL architectures and its characteristics along with their limitations in the framework of real time HAR are investigated. Secondly, existing hardware architectures and related challenges in this field are highlighted (adaptation of DL architectures towards microcontrollers, difficulty to provide a smart home with numerous sensors and trends regarding cloud-bases approaches). Then, new research directions and solutions around the real-time data quality assessment, the study of main performance factors for DL on microcontrollers, the concept of minimal sensors set up for the employment of IoT devices and the distributed intelligence are suggested to solve them respectively and to improve this field.

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Edge Computing Framework for Wearable Sensor-Based Human Activity Recognition

Cited by 7 publications

References 13 publications

Assessing Versatility of a Generic End-to-End Platform for IoT Ecosystem Applications

Assessing Versatility of a Generic End-to-End Platform for IoT Ecosystem Applications

Edge Intelligence: Concepts, Architectures, Applications, and Future Directions

Towards supervised real-time human activity recognition on embedded equipment

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