Risk Assessment System of Fall in the Elderly Using Artificial Intelligence and Cloud Computing

Research Article

Phys Med Rehabil Int. 2022; 9(2): 1204.

# Risk Assessment System of Fall in the Elderly Using Artificial Intelligence and Cloud Computing

Blasco-García JD¹, Pavón-Pulido N²*, López-Riquelme JA², Feliu-Batlle JJ², Nieto-Galera R³ and Herrero MT¹

1Clinical and Experimental Neuroscience (NiCE), University of Murcia, Spain

2Department of Electrical Engineering and Electronic Technology, Technical University of Cartagena, Spain

3AFA Levante. C/ Alameda San Antón, 29. 30205 Cartagena, Spain

*Corresponding author: Nieves Pavón-Pulido, Department of Automation, Electrical Engineering and Electronic Technology, Technical School of Industrial Engineering, Technical University of Cartagena, Cartagena, Murcia, Spain

Received: July 14, 2022; Accepted: August 10, 2022; Published: August 17, 2022

## Abstract

This paper presents a Cloud-based online tool for helping health professionals to predict the risk of falling in the elderly by using the well-known Tinetti’s Test. This tool implements a Deep Learning-based method for allowing several Tinetti scale’s items to be automatically estimated, simply using a conventional camera or a recorded video. From these sources of information, patients’ skeleton is recognized and their movements analyzed by applying some geometric calculations, which provide an objective risk assessment. Results are represented as a set of plots easily interpretable by experts. Several tests, in a controlled environment, have been carried out to validate the accuracy and reliability of the system. Moreover, some tests have been also made with real elderly patients, whose results have been evaluated by therapists. The benefits of using such remote tool for assessing (objective) fall risk, from a usability point of view, are also highlighted.

Keywords: Artificial intelligence; Tinetti scale; Falling risk; Telemedicine; Cloud computing

## Abbreviations

IoT: Internet of Things; IMU: Inertial Measurement Unit; ML: Machine Learning; AI: Artificial Intelligence

## Introduction

The COVID-19 pandemic has highlighted the need of improving health care systems through Digital Transformation, mainly regarding the use of online platforms capable of making remote attention easier, in a global connected world, where Cloud Computing and Internet of Things (IoT), have emerged as essential tools in many areas. This transformation could be particularly positive for vulnerable elderly people (dependent and multi-pathological), their families as well as caregivers [1].

A common risk that elderly people face is that related to falls which can cause chronic disabilities and economic burden [2] (particularly dangerous because many patients could seriously worsen their health status as a consequence of a fall). Then, it is essential to prevent these situations, and therapists use standard tests for assessing such risk, mainly based on the observation of patients’ movements. However, this procedure requires a face-to-face relationship and this is not always viable or easy. Moreover, simple observation is, in some measure, subjective, and it is not possible to properly store such measurements for further validation or comparison.

There exist some research lines focused on developing solutions [3] to incorporate sensors (accelerometers, Inertial Measurement Units (IMUs) or RGB-D devices), into the diagnostic process and automating fall risk assessment [4-11]. However, designing an automatic usable system capable of helping professionals to objectively assess the risk of falling (by remotely applying well-known standard tests), is still a challenge, especially if an artificial vision system, based on a conventional camera, is pretended to be used without the need of wearing other complementary sensors.

This paper presents a Cloud-based application, capable of evaluating the risk of falling in elderly people, which automatically detects and analyzes the human motion and applies the standard Tinetti’s Test for assessing such risk [12,13], taking a sequence of images, obtained by using a conventional camera, as input. The results are stored in the Cloud and they are presented to the therapist through graphical plots displayed in a WEB-based dashboard.

The outline of the paper is as follows. Section 2 describes the system architecture and details how the system works. Section 3 includes the obtained results and a discussion about the system performance. Section 4 addresses the conclusions and future work.

## Materials and Methods

The system (Figure 1), which helps the therapist to remotely conduct and evaluate the Tinetti’s Test, has been designed using a Client/Server-based software architecture hosted in Google Cloud, where the server side allocates a remote data warehouse implemented by using the Google Data store, which allows the results of the tests to be safely stored, maintaining the privacy of the patient. In particular, an application running in the Google App Engine Standard Framework offers their services through the Google Endpoints Framework. Additionally, a WEB client (written in HTML5, JavaScript and CSS), enables therapists to access a dashboard (where their patients’ data and tests results are properly presented), and to remotely capture patients’ images through a single video call or by providing a video previously recorded. The video sequence is processed by a set of client-side modules written in JavaScript, which make use of the MediaPipe Machine Learning (ML) framework [14]. Such sequence should show the patient carrying out the specific movements needed by the Tinetti’s Test.