Efficient Deployment of Predictive Analytics inEdge Gateways: Fall Detection Scenario

David Sarabia-Jacome, Ignacio Lacalle, Carlos E. Palau, Manuel EsteveDepartment of Communications

Universitat Politecnica de ValenciaValencia, Spain

dasaja@upv.es, iglaub@upv.es, cpalau@dcom.upv.es, mesteve@dcom.upv.es

Abstract—Ambient Assisted Living (AAL) represents the mostpromising Internet of Things (IoT) application due to its rele-vance in the elders healthcare and improvement of their qualityof life. Recently, the AAL IoT ecosystem has been enrichedwith promising technologies such as edge computing, whichhas demonstrated to be the best approach to overcome thedemanding requirements of AAL and healthcare services byproviding a reduction of the amount of data to transfer tothe cloud, an improvement of the response time, and qualityof experience. Also, the deployment of Artificial Intelligence(AI) technologies at the edge provides intelligence to improvethe decision making timely. However, this approach has beenscarcely studied in AAL scenarios and the few proposals basedon deploying machine learning models at the edge lack efficiency,security, mechanisms of resource management, service manage-ment, and deployment, as well as a real and experimental AALscenario. For these reasons, this paper proposes an innovativeedge gateway architecture to support the deployment of deeplearning (DL) models in AAL and healthcare scenarios efficiently.To do so, we have added a predictive analytics module to deploythe models. Since AI technologies demand more resources, acontainer-based virtualization technology is employed on theedge gateway to manage the limited resources, and providesecurity and lifecycle services management. The edge gatewayperformance was evaluated deploying a DL-based fall detectionapplication on it. As a result, our approach improves the inferencetime compared to that based on the cloud in 34 seconds and tosimilar approaches in 8 seconds.

Index Terms—Deep Learning, Edge Computing, Internet ofThings, Predictive Analytics, Fall, AAL, virtualization

I. INTRODUCTION

The Ambient Assisted Living (AAL) is the IoT applicationfield that has gotten the most attention in the last years. TheAAL goal is the creation of Ambiance Intelligent Environ-ments to promote independent living, the elders quality of lifeand autonomy. Since the elderly population is expected anincrease by 17% in the European Union by 2050 [1], the tele-monitoring and AAL applications are getting more relevanceto provide an Active and Healthy Aging (AHA). Along withthis increment in the elderly population, the ills and affectionsthat this vulnerable population face will increase in the comingyears. Consequently, promote elderly’s independent living andquality of life has become a challenging task that the emergingtechnologies (IoT, Big Data, and AI) are capable of solvingit.

There have been developed several systems to overcomethe open challenges in AAL area. These systems rely onwearable devices and IoT nodes to provide innovative health-care services. Mainly, these systems followed a cloud-basedapproach, but this approach has shown its limitations (highbandwidth occupation, delay, low response time, security andprivacy) in health-care applications and services. Recently,edge computing have been adopted to overcome the cloud-based approach limitations. Edge computing is defined as anycomputer and network resource placed close to the mobiledevices or sensors employed to perform data processing in nearreal time, also known as edge analytics [2]. Several systemshave adopted this approach by implementing IoT gatewaysemploying resource-constrained devices (e.g., Raspberry Pi,Intel Edison) or personal mobile devices (e.g., smartphone,smartwatch) and demonstrated a good performance by reduc-ing the amount of data to transfer to the cloud, and latency inapplications.

However, edge analytics is in its initial stages and needsother technologies to exploit its potential. Currently, the edgeperforms rule-based processing, but this approach does notscale well along with the proliferation of things. Recently,advances proposals in other areas (e.g., video surveillanceapplications) promote the edge intelligence by introducingthe Machine Learning (ML) models to perform predictiveanalytics on edge [3]. By doing so, the inference time (the timeelapsed during the data collection, data transport, predictionrequest, and response with the prediction) is decreased, so thequality of experience is improved. In a health-care scenario,the deployment of ML models at the edge gateways is gettingattention, but it is not efficient yet. Recently, the use ofcontainer-based virtualization techniques at the edge has beenused to improve the performance of the edge gateways [4],but there is scarce research on health-care scenarios and usingit to improve the deployment of ML and Deep Learning (DL)models at the edge. Thus, there is a lack of real implementationand practical experimentation in health-care scenarios, to takeadvantages of the improvement shown in other applicationareas by deploying ML and DL.

This paper presents an innovative edge gateway architectureto enable the deployment of predictive analytics for the AALIoT ecosystem to provide edge intelligence based on AItechnologies with high efficiency, unlike current approaches.978-1-5386-4980-0/19/$31.00 ©2019 IEEE

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For this aim, the edge gateway uses a container-based ap-proach on a resource-constrained edge device (Raspberry Pi).Moreover, the paper provides a practical and experimentalapplication deployment using an AAL use case: fall detectionto introduce the use of DL at the edge. To do so, DL modelsare implemented and trained in the cloud to detect the falland deployed remotely to the edge gateway designed usingthe Docker engine. Finally, the edge gateway performance isevaluated to determine its resources utilization during the DLmodel predictions process, and its limitations.

This paper is structured as follows. Section II presents arelated works review. Section III describes the edge gatewayarchitecture design. Section IV describes the AAL use caseimplementation. Section V presents the experimental evalu-ation of the edge gateway with the specific AAL use caseof fall detection, proving its usability in improving systemsof fall detection. Finally, Section VI presents our conclusionsand future improvements.

II. RELATED WORKS

The main motivation of this paper is to enhance the infer-ence time to detect risky situations in health-care applications.In the current literature, several proposals have been madeto overcome this goal in different health-care applications.Mainly, the cloud-based approach has been used to providesome health-care services, but this approach faces some issuessuch as a high inference time and high time to make decision-making due to the time to take transmitting data, processingthem, and making a prediction (inference time). This high timeis critical in AAL and health-care scenarios in which a timelyreaction can save a life.

The use of edge computing is proposed to overcome thecloud-based approach limitations. The edge computing iscomposed of generally network devices located on the edge ofthe network (e.g., gateways, routers) with limited processing,storage, and power consumption capabilities. They supportIoT devices protocols such as CoAP (Constrained ApplicationProtocol), MQTT (Message Queue Telemetry Transport), andlow-power wireless technologies such as ZigBee, Bluetooth,and 6LowPAN, to enable the communication of IoT deviceswith the cloud layer. Besides, the edge gateways are capableof performing some data processing, known as edge analytics,reducing the end-to-end latency insensitive time responseapplication and the bandwidth occupation by sending to thecloud only the results of the edge analytics [5]. In [6], the au-thors proposed an edge-based architecture to support human-centric applications in health-care scenarios. An edge gatewayimplemented using a Raspberry Pi was employed to providemulti-radio and multi-technology communication to enablethe IoT nodes connections. Also, they proposed a MobileBodyClient to enable the connection between smartphones andthe edge gateway. The edge gateway was capable of dealingwith the technical interoperability, quality of service, delay andbandwidth occupancy issues, but it did not deal with securityand privacy, and resource manager. Similarly, Rahmani et al.[7] proposed a smart e-Health Gateway to exploit the benefits

of edge computing by implementing local storage, real-timelocal data processing, data fusion, and security and privacy,interoperability and mobility mechanisms. The authors testedthe smart e-Health gateway architecture by pre-processingECG signal (heart rate calculation and feature extraction) andsent it to the cloud for further processing. As results, the pre-processing tasks at the edge reduces the volume and speed ofthe data generated by the IoT nodes, solving the bandwidthand delay issues in the cloud-based approach.

The fall detection is one of the most AAL scenarios studiedin the current literature due to its relevance to reducingthe mortality [8]. Mainly, wearable-based systems have beenproposed due to the increases of acceptation in the communityof wearable devices to track health rates and sports activities.Along with this, the threshold-based methodology to detectthe falls have been adopted in several proposals, but thismethodology lacks accuracy and precision. However, theyrequire more processing capabilities than threshold-based, sothey are deployed on the cloud. Recently, the AAL scenariohas taken advantage of edge computing to improve their FallSystems. For example, Cao et al. [11] proposed a threshold-based system to detect falls in the edge, but the algorithmapproach is not precise and increases the false positive cases.To overcome this issue, filtered data at the edge is transmittedto the cloud for further analysis to discriminated whetherthere was a real fall. The author evaluated the response time,obtained 35,67 seconds average. However, machine-learningbased algorithms have proved to be more accurate in fallscenarios. Yacchirema et al. [12] proposed place the machinelearning model (Decision Tree) to detect a fall into a smart IoTgateway improving the accuracy and providing intelligenceto the edge. However, the deployment of the ML to theedge was inefficient due to the authors did not consider thatML algorithms require more resources than the typical rule-based processing at the edge. The necessity of an adequateresource manager was exposed, as well as issues in thedeployment, management, privacy, and security. Our proposal,in comparison to these proposals, provide an efficient wayto deploy ML models to the edge and overcome the issuesexposed.

Virtualization technologies, mainly container-based, havedemonstrated the improvement of resource occupancy, secu-rity, and privacy by isolating processor services. The feasi-bility of using the container-based approach in a resource-constrained device has been studied in [4]. Also, the feasibilityto use Docker containers as edge computing platform wasdiscussed in [13]. The benchmarking evaluation showed thatthe resources are managed in a well-manner than bare metalapproach. In the health-care scenario, the use of Docker engineis in its early development. In the current literature, two relatedworks have been identified. The first one, [14] proposed acontainer-based smart IoT gateway to deploy AAL servicesand applications which are isolated in Docker containers. Theauthors deployed rule-based data processing to analyze thesensor data. Al-Rakhami et al. [15] studied the deployment ofML model (Support Vector Machine model) in a container-

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based smart IoT gateway for recognizing human activity suchas walking, sitting, standing, among others. The use case wasevaluated in a simulated experiment by using a Raspberry Piand two servers in the cloud as virtual micro-computer. Ourproposal, in comparison, provides a real implementation usingDL models for improving the accuracy in an AAL scenario.

As far as we know, there are many attempts to implementedge computing for improving the services in AAL andhealth-care applications. However, these proposals have notstudied the deployment of DL models to provide intelligenceat the network. Also, the few proposals that included AI(ML models) at the edge were inefficient (considering theresource manager). The deployment of models is efficient byusing a container-based, but this approach is used in AALscenarios scarcely. The scarce proposals did not propose a realimplementation, nor employed DL models at the edge, whichin comparison to ML require more resources but provide moreaccurate predictions. Thus, this paper improves the state ofthe art by introducing a container-based gateway for AALscenarios to provide security, high performance, better useof the resource, life-cycle management and deployment ofservices, and by analyzing the feasibility of deploying DLmodels at the edge to reduce the inference time in the falldetection scenario.

III. ARCHITECTURE PROPOSEDThe proposed architecture to deploy DL models in this paper

follows an edge-based architecture. The architecture consistsof IoT devices, edge computing, and cloud computing, as isdepicted in Fig. 1. The cloud provides high resource capabil-ities for processing and storing the IoT big data. In addition,the cloud facilitates the training process of DL models whichrequire power-full resources. Cloud-trained models designed tomake predictions are deployed in the edge gateway efficiently.Our edge Gateway covers the main requirement in the currentliterature such as interoperability, processing, storing, securityand privacy, and introduce the innovative predictive analyticsmodule.

This section describes in detail the design of our architecturefor an efficient deployment on edge gateway of applicationsbased on DL models to enable the predictive analytics, aswell as describes a practical and experimental scenario for thedeployment of an application.

Figure 1. High-level Architecture Overview

A. Edge Gateway ArchitectureThe edge gateway architecture is shown in Fig. 2 The

module functions are detailed below:

Figure 2. Edge Gateway Architecture

• Communication Module: supports the communicationprotocols and wireless technologies to enable the connec-tion between the medical and IoT devices and the cloudlayer. The technologies supported are ZigBee, Bluetooth,6LowPAN, and WiFi (Wireless-Fidelity).

• IoT Devices Handler: allows the bi-directional commu-nication by employing a broker MQTT. The MQTT is alightweight communication protocol used by the majorityof IoT devices.

• Stream Processing Module: enables data transformationoperators (aggregation, filter, compression, and fusion) toprocess data incoming through the IoT devices handlermodule.

• Temporal Database: stores the data processed and thepredictive results. It provides a mechanism to query datato get a descriptive analysis. A documented orienteddatabase is used to store data due to its support of JSON-like documents.

• Predictive Analytics: detects and predicts patterns fromthe sensor data. To do so, this module implement servicesbased on the DL models trained in the cloud. This modulegenerally presents high resources consumption. Also, theinference time would be affected if the inference modelhas a very deep neural network architecture.

• Resource Manager: manages the limited resources of thenode by using a container-based virtualization architec-ture. A container instance groups the application librariesand executable files, and its dependencies to be executedas an isolated process. The container engine is involvedin the management of the containers and the resourcesprovided by the host Operating System (OS). Moreover,the container engine facilitates the communication be-tween containers, as well as the external communicationby deploying a virtual bridge network in the OS.

• Device Manager: enables the management remotely ofthe edge gateway and the deployment remotely of predic-tive analytics. On the cloud side, a container orchestratoris implemented using Nodejs. The orchestrator in thecloud has tasks of registering the edge gateway andremote management using the Docker API.

IV. AAL SCENARIO: FALL DETECTION

An AAL use case is employed to test the functionalities ofthe architecture proposed. Elders’ fall detection is chosen as

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the use case because of its high importance to enable elders’independence and low risky quality of life. A wearable-basedapproach is employed due to wearable devices have gainedacceptation in the community because they are less invasiveand more effective. A wearable belt prototype using a tri-axialaccelerometer sensor is employed in our use case for sensingthe daily activities of life. The wearable is connected to theproposed edge gateway using wireless technology (Wi-Fi) tomake a Wireless Sensor Network (WSN). Also, IoT nodes tosense environment parameters (temperature, humidity, CO2)are connected using other wireless technologies (ZigBee, IEEE802.15.4). The wearable sent raw tri-axial data (x,y,z) tothe edge gateway using a secure wireless connection (SecureSockets Layer SSL/Transport Layer Security TLS). The rawdata collected by the sensors are sent using the MQTT proto-col. In the edge gateway, an MQTT broker acts as a messagerouter. The different modules such as stream processing,predictive analytics and temporal database using the MQTTbroker to interchange message using the publish/subscribestructure. The stream processing (aggregate and filter) pre-process the data using a sliding-window, defined as 2999 time-steps, before to route the stream data to the predictive analyticsmodule. The edge gateway implements the predictive analyticsto detect elderly falls using a DL model which is trained in thecloud, isolated on a Docker container, and deployed to the edgegateway using the Docker registry. Finally, if the predictiveanalytics module detects a fall sent an alert to the caregiversmartphone, and save the results into the temporal database.This workflow is illustrated in Fig. 3. The implementationdetails are described below.

Figure 3. Work Flow Predictive Analytics Deployment and Detection

A. Wearable and IoT nodes implementation

The wearable device prototype is build using an ADXL345accelerometer (configured for ± 16g, 13 bits of analog todigital converter ADC), a microcontroller NodeMcu v1.0 V3(based on ESP8266 and include a WiFi module) and a PowerPack V1.2 (3.7V 3800 mAh battery), as the Fig. 4 shows.

The tri-axial accelerometer data is acquired using a frequencysample of 200 MHz. These data are sent to the edge gatewayusing the lightweight communication protocol MQTT, whichis suitable for Machine to Machine (M2M) communicationand encapsulated using SSL/TLS secure protocols. Also, thesensors DHT11 to sense the temperature and humidity, as wellas the MQ135 sensor to sense the quality of the air (CO2)are employed as IoT nodes. These sensors are connected tothe Arduino Uno microcontroller and connected to the edgegateway using Bluetooth.

Figure 4. Wearable Hardware. a)Battery b)WiFi Module c)Accelerometer

B. Edge Gateway Implementation

The edge gateway proposed is composed of hardware andsoftware components. The edge gateway is implemented usinga Raspberry Pi 2 (RPi) model B with the following charac-teristics: Quad Core @900MHz ARMv7 Cortex-A7, 1 GBLP-DDR2 400 MHz memory, and MicroSD 8GB storage.Also, the Raspbian Stretch Operative System is used as theRPi OS since this is the latest. Besides, the Docker enginefor ARMv7/ARMv8 architectures is installed to enable thecontainer virtualization and manage the constrained resource.The Docker remote API enables to manage the edge gatewayexposing the port 2375 remotely.

Four containers are created to cover the design proposed.The first one is the fall detection application (predictiveanalytics). A container image with the TensorFlow 1.8.0 isimplemented using a Raspbian Stretch as the based image tosupport any deep learning model. The Raspbian Stretch SObrings by default Python 3.5 which is required for the properfunctioning of TensorFlow 1.8.0. In addition to this deeplearning framework, the container image includes requiredlibraries such as Numpy, Matplot, and Flask. This containerloads the application program and the deep learning modelto provide predictive analytics. To do so, an API-REST isimplemented using the Flask library to receive the raw dataand return the predictions in a JSON format. Second, anMQTT broker is isolated in a container to enable M2Mcommunication with the wearable device, and IoT nodes.In this case, the Eclipse Mosquitto application base imagewas employed to deploy M2M communication service. Also,the Mosquitto application provides QoS levels to warranty

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communication. Third, the temporal database is implementedby using MongoDB. MongoDB is a documented orienteddatabase that is capable of storing JSON files. Finally, aprocessing program is developed using Python libraries toprocess the data coming from the sensors. The results of theprocessing are the input data to fall detection.

C. Fall Deep Learning Model

ML techniques have been used to detect the fall, suchas Support Vector Machine (SVM), k-nearest neighbors (K-NN), Random Forest, or Decision Trees (DT) in the majorityof these systems. However, these systems do not providean acceptable precision and accuracy. Consequently, a DLapproach is considered to improve accuracy and precision.Recurrent Neural Networks (RNN) are commonly employedfor time sequences inputs. RNN uses a previous time input aswell as the current input to make predictions. To do so, theRNN uses memories to preserve sequential information in ahidden state. Currently, there are two relevant RNN variants,Long Short-Term Memory Units (LSTM) and Gated RecurrentUnit (GRU). In our case, the learning models employ as inputsthe time-sequence generated by the wearable belt prototype.Two RNN architecture models (GRU and LSTM) were trainedby employing the fall dataset provided in [16]. The dataset iscomposed of 4000 text file which gathered data from well-documented daily activities (e.g., walking, running, sitting,falling, among others). Each text file contains the values col-lected from 3 sensors (i.e., 2 accelerometers, and 1 gyroscope).The models were implemented following the guide of [10] andemploying the Keras API 2.1.6 and as backend TensorFlow1.8.0. The Keras API provides for libraries to implement RNNand CNN. The models (GRU, LSTM) reached accuracy 97.5,98.4 and precision 98.4, 97,3 respectively. The performanceachieved by the DL models is better than ML models in thecurrent literature and is similar to other DL models.

V. RESULTS

The evaluation is divided into two sections. On the onehand, the DL models performance is evaluated when theyare deployed into the edge gateway proposed. In this case,three volunteers between 35 and 55 years old participatedin the experiments. On the other hand, the edge gatewayperformance is evaluated when it is stressed with more thanan IoT wearable belt. In this case, SisFall dataset [16] wasemployed to simulated multiple data streams. This kind ofevaluation tests is proposed to verify the feasibility of usinga container-based approach at the edge for AAL applications,the feasibility of deploying DL models reducing the inferencetime without a models performance deterioration, and thelimitations of the DL models deployment in a container-basededge gateway.

A. Performance of Predictive Analytics

The performance of the predictive analytics is analyzed forits inference time, and its precision and accuracy. The resultsare compared to other fall detection systems which employ

edge nodes (smart IoT Gateway, or Smartphone) to deployML models. Table I. summarized the inference time for bothfall detection models (GRU, LSTM) on edge gateway, andML models performance on a smart IoT Gateway (DecisionTree DT) [12] and a smartphone (SVM, and K-NN) [17].Since the DL models perform more operations to generate aprediction in comparison to the model ML models, this couldaffect the inference time, but this does not happen in this casebecause the DL model is optimized before to be deployed.In the same way, the model’s performance comparison amongsimilar works shows that the DL model performance is betterthan ML models due to DL models are capable of extract moreinformation in the training process. Finally, the edge inferencetime in the edge gateway is less than the inference time in thecloud (35,67 seconds) provided by [11] and less than otherML approaches.

Table IMODELS PERFORMANCE

Parameter OUR [17] [12]GRU LSTM SVM K-NN DT

Inference Time [s] 1.14 1.21 9 9 -Accuracy [%] 97.5 98.4 95.6 70.6 91.7Precision [%] 97.2 97.3 82.7 84.2 93.7

B. Deep Learning Edge Gateway

The CPU, memory, and power consumption parameters areevaluated in the experimental test-bed. The measurements ofthe CPU, memory, and power consumption parameters followthe methodology and the RPI energy consumption model pro-posed in [18], [19]. In this case, the number of IoT wearableconnected to the edge gateway is increased to evaluate itslimitations. Fig. 5 and Fig. 6 shows the results of the stressevaluation. The parameters evaluated increase according to thenumber of wearables are increased which is expected behavior.However, the CPU usage is not dramatically increased due tothe use of the Docker engine which is capable of share it withanother isolated process. The power consumption not onlyrelies on CPU usage but also the WiFi interfaces in-boundedconnection (more wearable, more traffic). Finally, the infer-ence time is increased when the number of wearables also do.The edge gateway provides an acceptable time inference lessthan 15 seconds until 10 wearable simultaneous connected, butif the wearable is increased the inference time is not suitable.

VI. CONCLUSION

This paper has shown an edge gateway architecture tosupport the efficient deployment of DL models by employinga container-based approach to be used in AAL scenarios.This approach has many advantages in the AAL scenariosuch as reducing the time to detect the falls, the bandwidthoccupancy, providing security, performance, resource usage,life-cycle management and deployment, and improving thequality of experience. The practical and experimental scenario

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Figure 5. CPU and Power Consumption vs. Wearables Connected

Figure 6. Memory usage and Inference Time vs. Wearables Connected

(fall detection) showed an improvement of the inference timereducing in 34 seconds in comparison to the cloud-basedapproach, and almost 8 seconds to similar approaches. Thedeployment of DL models compared to ML models at the edgeis justified by the high accuracy and sensitivity (robustness) todetect falls provided by DL models. Also, the container-basedapproach employed shows homogeneous resource usage. How-ever, the scalability of the edge IoT gateway proposed is anissue for deploying DL models on it, but this could be solvedby a horizontal scalability approach (more edge gateways).As future research line, more DL models approaches in falldetection (camera-based) will be studied. Finally, a fall systembased on the proposed IoT gateway will be implemented andtested in a nursing home.

ACKNOWLEDGMENT

This research was supported by the Ecuadorian Govern-ment through the Secretary of Higher Education, Science,Technology, and Innovation (SENESCYT) and has receivedfunding from the European Union’s “Horizon 2020” researchand innovation program as a part of the ACTIVAGE projectunder Grant Agreement No 732679.

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