An Efficient Home-Based Risk of Falling Assessment Test Based on Smartphone and Instrumented Insole

The aim of this study is to improve and facilitate the methods used to assess risk of falling among older people at home. We propose an automatic version of One-Leg Standing (OLS) test for risk of falling assessment by using a Smartphone and an instrumented insole. For better clinical assessment tests, this study focuses on exploring methods to combine the most important parameters of risk of falling into a single score. Twenty-three volunteers participated in this study for evaluating the effectiveness of the proposed system which includes eleven elderly participants: seven healthy elderly (67.16 ± 4.24 years), four Parkinson disease (PD) subjects (70 ± 12.73 years); and twelve healthy young adults (28.27 ± 3.74 years). Our work suggests that there is an inverse relationship between OLS score proposed and risk of falling. Proposed instrumented insole and application running on Android could be useful at home as a diagnostic aid tool for analyzing the performance of elderly people in OLS test

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio

Risk of falling in a timed Up and Go test using an UWB radar and an instrumented insole

Previously, studies reported that falls analysis is possible in the elderly, when using wearable sensors. However, these devices cannot be worn daily, as they need to be removed and recharged from time-to-time due to their energy consumption, data transfer, attachment to the body, etc. This study proposes to introduce a radar sensor, an unobtrusive technology, for risk of falling analysis and combine its performance with an instrumented insole. We evaluated our methods on datasets acquired during a Timed Up and Go (TUG) test where a stride length (SL) was computed by the insole using three approaches. Only the SL from the third approach was not statistically significant (p = 0.2083 > 0.05) compared to the one provided by the radar, revealing the importance of a sensor location on human body. While reducing the number of force sensors (FSR), the risk scores using an insole containing three FSRs and y-axis of acceleration were not significantly different (p > 0.05) compared to the combination of a single radar and two FSRs. We concluded that contactless TUG testing is feasible, and by supplementing the instrumented insole to the radar, more precise information could be available for the professionals to make accurate decision

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

Validation of minimal number of force sensitive resistors to predict risk of falling during a timed up and go test

Purpose Several studies use force sensitive resistors (FSR) to compute gait and balance parameters related to falls without investigating the number of sensor units required to produce useful information. We propose a model with minimal sensors for an instrumented insole by investigating and optimizing the location and variety of sensors required to efficiently detect people at risk of falling. Methods Datasets previously recorded on twelve Parkinson’s disease (PD) participants (67.7 ± 10.07 years), nine healthy elderly (66.8 ± 8.0 years) and ten young healthy adults (28.27 ± 3.74 years) were used in this study. We compared the datasets obtained from the use of four FSRs with those of three, two, one and no FSR; each set was combined with an inertial measurement unit (IMU). Results During the walking activity, the risk of falling scores from four FSRs and IMU (acceleration in y-axis only) were not significantly different compared with two FSRs and IMU (p > 0.05), whereas significant difference was found for three FSRs and IMU and one FSR and IMU (p  0.05). Conclusions We concluded that it is feasible to estimate the risk index after reducing the number of sensing units from four to two FSRs during walking test and from four to three FSRs during sit-to-stand and stand-to-sit tests. The FSRs should be placed at strategic positions to avoid information loss

Development of a Fall Risk Asessment Tool Using Gait Analysis

In the United States, falls are one of the leading causes of fatal and non-fatal injuries for people of all ages. Current clinical methods to assess fall risk are impractical, and often do not use individuals’ actual performance. With current technological advances, and the Internet of Things (IoT), the tools are available to create a digital system that can take into account an individual’s actual performance in making a fall risk assessment. A digital insole based sensory computing system can collect and analyze human gait patterns to develop a fall risk assessment platform with great accuracy.The presented research considers current clinical methods and describes a computerized self-service platform that successfully addresses different gait variables and metrics critical to accurate fall risk assessment. The system incorporates a shoe insole with pressure sensors, and an accelerometer. Collected foot data are transferred to an analytics visualization platform. A wide range of gait pattern recognition metrics, and gait data analyses features are then displayed on the platform enabling specific fall risk assessment

How Does Technology Development Influence the Assessment of Parkinson’s Disease? A Systematic Review

abstract: Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD. This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review.Dissertation/ThesisMasters Thesis Computer Engineering 201

Wearable devices for classification of inadequate posture at work using neural networks

Inadequate postures adopted by an operator at work are among the most important risk factors in Work-related Musculoskeletal Disorders (WMSDs). Although several studies have focused on inadequate posture, there is limited information on its identification in a work context. The aim of this study is to automatically differentiate between adequate and inadequate postures using two wearable devices (helmet and instrumented insole) with an inertial measurement unit (IMU) and force sensors. From the force sensors located inside the insole, the center of pressure (COP) is computed since it is considered an important parameter in the analysis of posture. In a first step, a set of 60 features is computed with a direct approach, and later reduced to eight via a hybrid feature selection. A neural network is then employed to classify the current posture of a worker, yielding a recognition rate of 90%. In a second step, an innovative graphic approach is proposed to extract three additional features for the classification. This approach represents the main contribution of this study. Combining both approaches improves the recognition rate to 95%. Our results suggest that neural network could be applied successfully for the classification of adequate and inadequate posture

Reaction time to vibrotactile messages on different types of soil

This study investigates the Reaction Time (RT) to vibrotactile messages presented under the foot plantar on different types of soil. We determine whether reaction time varies while walking on different types of soil (mobile situation). A total of six young participants (n=6) aged between 21 and 28 took part firstly in this study where they had to walk on five types of soil (concrete, carpet, foam, gravel, and sand). The methodology includes 360 repeated measures. The findings have consistently revealed a decrease of reaction time to vibrotactile messages when walking on the three deformable soils (foam, gravel, and sand)

Feasibility of Sensor Technology for Balance Assessment in Home Rehabilitation Settings

The increased use of sensor technology has been crucial in releasing the potential for remote rehabilitation. However, it is vital that human factors, that have potential to affect real-world use, are fully considered before sensors are adopted into remote rehabilitation practice. The smart sensor devices for rehabilitation and connected health (SENDoc) project assesses the human factors associated with sensors for remote rehabilitation of elders in the Northern Periphery of Europe. This article conducts a literature review of human factors and puts forward an objective scoring system to evaluate the feasibility of balance assessment technology for adaption into remote rehabilitation settings. The main factors that must be considered are: Deployment constraints, usability, comfort and accuracy. This article shows that improving accuracy, reliability and validity is the main goal of research focusing on developing novel balance assessment technology. However, other aspects of usability related to human factors such as practicality, comfort and ease of use need further consideration by researchers to help advance the technology to a state where it can be applied in remote rehabilitation settings