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Edge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges

Alaa Awad Abdellatif, Amr Mohamed, Carla Fabiana Chiasserini, Mounira Tlili, Aiman Erbad

arXiv:2004.07311v1eess.SPcs.CYcs.NI

TL;DR

Scalable, lower-cost smart healthcare requires real-time remote monitoring despite energy, bandwidth, and data-transfer challenges. The paper proposes a MEC-based, context-aware architecture with edge compression and event detection, reporting lower data transfer, up to 50% EEG-distortion reduction, and 50% longer MEN battery lifetime.

  • Problem

    Smart healthcare needs scalable, cost-effective remote monitoring, but multimodal transmission, energy constraints, and reliable rapid cloud-based emergency detection remain challenging.

  • Method

    The paper proposes a MEC-based architecture implementing context-aware in-network processing through multimodal stacked-autoencoder compression and edge-based feature extraction for event detection.

  • Results

    Up to 50% EEG distortion reduction and 50% MEN battery-lifetime improvement were reported for multimodal SAE and CDT, respectively.

  • Takeaways & Limitations

    Edge compression and event detection can reduce cloudward data transfer while supporting short response times, efficient processing, and lower energy and bandwidth consumption.

Abstract

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Improving efficiency of healthcare systems is a top national interest worldwide. However, the need of delivering scalable healthcare services to the patients while reducing costs is a challenging issue. Among the most promising approaches for enabling smart healthcare (s-health) are edge-computing capabilities and next-generation wireless networking technologies that can provide real-time and cost-effective patient remote monitoring. In this paper, we present our vision of exploiting multi-access edge computing (MEC) for s-health applications. We envision a MEC-based architecture and discuss the benefits that it can bring to realize in-network and context-aware processing so that the s-health requirements are met. We then present two main functionalities that can be implemented leveraging such an architecture to provide efficient data delivery, namely, multimodal data compression and edge-based feature extraction for event detection. The former allows efficient and low distortion compression, while the latter ensures high-reliability and fast response in case of emergency applications. Finally, we discuss the main challenges and opportunities that edge computing could provide and possible directions for future research.

I. INTRODUCTION

Smart-health systems use connected sensing and wireless technologies for context-aware remote monitoring, but their data volume, responsiveness, scalability, energy, and privacy requirements challenge centralized cloud processing. The paper therefore proposes MEC-based architecture and context-aware edge solutions.

  • S-health evolves mobile health through context-aware wireless services supporting remote monitoring by patients and caregivers.
  • Sensors, cameras, and controllers enable automatic identification, drug-patient association, and real-time vital-sign monitoring for early deterioration detection.
  • 5G and IoMT-enabled healthcare generates data that must be transported, processed, stored, and privacy-protected rapidly.
  • Centralized cloud computing is unsuitable because it limits scalability and responsiveness while imposing heavy network traffic.
  • The paper examines MEC motivations, expected benefits, requirements, solutions, open challenges, and context-aware edge processing.
  • Its proposed architecture is introduced as a basis for meeting s-health requirements through processing and storage near data sources.

A. MEC-based S-Health Architecture

The proposed MEC-based architecture connects patient-side hybrid sensing sources to healthcare providers through local aggregation, mobile or infrastructure edge processing, and edge-cloud services.

  • The architecture spans data sources located on or around patients through intermediate edge components to service providers.
  • Hybrid sensing sources include body-area sensors, cameras, smartphones, and external medical devices for continuous monitoring and emergency detection.
  • A Patient Data Aggregator collects vital signs from body-area sensors and transmits the aggregated medical data toward network infrastructure.
  • The Mobile/Infrastructure Edge Node fuses multimodal data, performs in-network processing and classification, extracts information, and issues emergency notifications.
  • The edge cloud supports local storage, sophisticated pattern detection, trend discovery, and population health management.
  • Healthcare providers deliver preventive, curative, emergency, or rehabilitative services based on the system’s processed information.

B. Benefits for s-health

MEC can improve remote-monitoring practicality by reducing transmission costs and enabling local context processing, while supporting applications ranging from heart monitoring to contactless sensing.

  • MEC architectures support multiple e-health systems, with the paper emphasizing practical benefits rather than in-depth technical comparison.
  • Heart monitoring applications track vital signs associated with cardiac arrhythmia, chronic heart failure, ischemia, and myocardial infarction.
  • Energy savings arise from managing device operating states and data transfer at the edge, supplemented by compression and sensor-edge proximity.
  • Edge processing can extract context and apply localization to match a patient’s position with nearby appropriate caregivers.
  • Contactless camera sensing supports physiological-signal extraction without affecting patient activities.

2) Contactless monitoring systems:

Contactless monitoring can improve patient comfort but produces large camera-data volumes that strain bandwidth, motivating processing, compression, and feature extraction at the edge. For predictive monitoring, local analysis supports rapid emergency detection.

  • 2) Contactless monitoring systems:: A single standard camera can generate up to 40 GB per day, making conventional cloud transmission impractical under limited bandwidth.
  • 2) Contactless monitoring systems:: Processing, compressing, and extracting important information at the MEN reduces the data burden from camera sensors.
  • 2) Contactless monitoring systems:: Predictive monitoring targets emergency detection in high-risk patients to support preventative strategies for reducing morbidity and mortality.
  • 3) Disorder prediction/detection systems:: Real-time prediction requires swift delivery and analysis near the patient, while wireless errors and security attacks can alter cloud-transferred data.
  • 3) Disorder prediction/detection systems:: MEN-based event detection addresses the need for quick abnormality detection in emergency applications.

III. IMPLEMENTING THE EDGE NODE FUNCTIONS

The edge node functions compress multimodal health data and extract features for classification, reducing transmission burdens while supporting fast, reliable disorder detection. The proposed compression pipeline uses a deep-learning encoder at the edge and decoder at the cloud, with multimodal SAE reducing EEG distortion relative to single-modality compression.

  • III. IMPLEMENTING THE EDGE NODE FUNCTIONS: The network edge implements data compression plus feature extraction and classification to reduce energy and bandwidth use and support fast disorder detection.These functions are presented as the main edge-node mechanisms for meeting e-health application requirements.
  • III. IMPLEMENTING THE EDGE NODE FUNCTIONS: The conventional cloud approach can generate 8-10 GB per patient per day when EEG, EMG, EOG, and activity video are transmitted for brain-disorder monitoring.This motivates local in-network processing before cloud delivery.
  • A. Multimodal data compression using deep learning: The multimodal compression case study uses EEG-EOG data from 32 people watching 40 music videos.The dataset supports evaluation of multimodal compression for s-health monitoring.
  • A. Multimodal data compression using deep learning: Stacked auto-encoders encode compressed representations at the MEN and reconstruct signals at a cloud server, exploiting intra- and inter-modality correlations.The encoder progressively reduces neurons across layers, while training determines the weights and biases used for online edge compression.
  • A. Multimodal data compression using deep learning: Training is performed offline at the server, after which the learned configuration supports low-complexity online compression and transfer at the MEN.The last encoder layer is set according to the desired compression ratio, and other layer sizes are optimized.
  • A. Multimodal data compression using deep learning: Up to 50% lower EEG distortion is achieved with M-SAE than SM, while EOG distortion increases by just 2% as compression ratio varies.Figure 2 compares multimodal SAE with separate single-modality SAE compression for EEG and EOG signals.

B. Edge-based feature extraction and classification

The paper applies edge-based feature extraction and classification to epileptic seizure detection, using a MEN to process EEG data and selectively forward data or computed features to the cloud.

  • B. Edge-based feature extraction and classification: The approach builds on prior methods that extract features from vital signs, voice, or video to distinguish potential patients from healthy people or identify emergencies.The cited related work includes Parkinson’s disease detection using voice features and cloud computing.
  • B. Edge-based feature extraction and classification: The study focuses on epileptic seizure detection and processes EEG data collected by an EEG headset at the MEN before forwarding it to the cloud.The EEG dataset contains three classes, denoted A, B, and E.
  • B. Edge-based feature extraction and classification: Feature extraction and classification occur at the MEN, which sends all data or only computed features depending on whether classification detects a seizure event.This selective transmission is summarized by Figure 3.

1) Feature extraction :

The feature-extraction stage uses frequency-domain analysis because it is insensitive to electrode-placement signal variations, selecting five features to distinguish seizure from non-seizure EEG events.

  • 1) Feature extraction :: The procedure considers frequency-domain rather than time-domain feature extraction because of its insensitivity to variations caused by electrode placement.The gathered EEG data are transformed into the frequency domain before feature selection.
  • 1) Feature extraction :: Figure 3 presents efficient class-based data transmission for s-health systems.The associated transmission concept sends data according to the detected class.
  • 1) Feature extraction :: Frequency-domain normal and abnormal EEG classes differ in mean, median, and amplitude variations across frequency bands.These differences provide the basis for distinguishing seizure-related signal patterns.
  • 1) Feature extraction :: The selected Frequency Features are mean, median, peak amplitude, Root Mean Square, and Signal Energy.RMS and SE are used as signal-strength estimators in different frequency bands.

2) Event-detection at the edge :

The paper performs feature extraction and classification at the edge, using a low-complexity threshold classifier for seizure detection. The proposed FFC achieves high accuracy in an intermediate threshold range and is compared with established classifiers.

  • Event-detection at the edge: The FFC defines an IF-THEN rule over generated frequency features to detect abnormal EEG variations caused by seizures.The patient status is determined using a classification threshold obtained during offline training.
  • Event-detection at the edge: 98.3% accuracy is achieved for seizure detection at γ = 0.7, while the classifier outperforms RandomForest, NaiveBayes, IBk, and REPTree for γ ranging from 0.5 to 0.8.Very low or high γ values produce mostly Seizure or Normal statuses, reducing class discrimination.
  • Event-detection at the edge: Figure 4 compares FFC with RandomForest, NaiveBayes, IBk, and REPTree using classification accuracy across varying γ.The comparison concerns the threshold-dependent behavior of the classifiers.

IV. CHALLENGES AND OPPORTUNITIES

The paper identifies privacy and security challenges for MEC-based s-health systems, including data ownership and the trade-off between stronger protection and quality of service. It also compares battery-lifetime performance between the proposed FFC technique and CBS.

  • A. privacy and security: Figure 5 compares the proposed FFC technique with the CBS scheme in terms of battery lifetime.
  • IV. CHALLENGES AND OPPORTUNITIES: MEC-based s-health systems face privacy and security threats affecting wireless medical devices, data-processing algorithms, and storage.Examples include patient tracking, relaying, and denial-of-service attacks that can violate confidentiality and integrity.
  • A. privacy and security: The paper proposes keeping collected data near patients and allowing patients to own and control whether data remain at the edge or reach the cloud.Private information can be removed or hidden before transmission.
  • A. privacy and security: Stronger cryptography and key management increase processing and edge overhead, potentially harming QoS in real-time applications with strict delay and throughput requirements.The paper calls for joint QoS and security mechanisms.

B. Collaborative edge

Collaborative edge computing connects geographically distributed stakeholders’ edge nodes to support healthcare data sharing and direct patient-to-hospital monitoring. The approach can improve efficiency while reducing energy use and operational cost.

  • B. Collaborative edge: Collaborative edge connects the geographically distributed edges of hospitals, disease-control centers, pharmacies, and insurance companies.It addresses healthcare collaboration across multiple stakeholder domains where privacy concerns and transfer costs restrict data sharing.
  • B. Collaborative edge: Collaborative edge improves spectrum and energy efficiency and supports data transfer in geographically remote areas through D2D communication.
  • B. Collaborative edge: A patient’s edge node can connect directly to the nearest hospital edge for continuous monitoring without routing through the cloud.The paper associates this arrangement with increased monitoring efficiency, lower energy consumption, and reduced operational cost.

C. Combining heterogeneous sources of information

Hybrid sensing in s-health combines multiple information sources at the edge to support automated supervision and remote monitoring, but heterogeneous streams, energy limits, and signal artifacts complicate deployment. MEC-based processing addresses these constraints through multimodal correlation, compression, and event detection, reducing data transfer and supporting efficient, responsive services.

  • Challenges: Power consumption limits transmission of informative biosignals such as EEG, EMG, and electrocardiograms from battery-operated devices.Hybrid sensing also faces artifacts from muscle activity, movement, noise, interference, and signal offset, which affect data quality.
  • Edge-based processing: MEC-based multimodal processing correlates different modalities and temporal patterns within each modality to handle heterogeneous, variable data streams.The architecture is intended to address system complexity associated with multiple data-stream inputs.
  • Edge-based processing: Edge compression and event detection reduce data transferred toward the cloud while supporting efficient processing and limited-energy devices.The paper presents these as computing tasks implemented at the edge to reduce a major s-health bottleneck.
  • System requirements: Integrating wireless-network characteristics, acquired data, and application requirements supports sustainable, high-quality s-health services.The identified edge approaches target short response time, efficient processing, and minimal energy and bandwidth consumption.
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