scholarly journals Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Biosensors ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Tam Nguyen ◽  
Jonathan Young ◽  
Amanda Rodriguez ◽  
Steven Zupancic ◽  
Donald Lie
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Normal Subjects ◽  
The Elderly ◽  
Measurement Unit ◽  
Balance Disorders ◽  
Remote Sensor ◽  
Data Collection And Analysis ◽  
Dynamic Gait Index ◽  
Dynamic Gait

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual’s risk for falls.

scholarly journals Real-Time Classification of Patients with Balance Disorders vs. Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Biosensors ◽  
2016 ◽  
Vol 6 (4) ◽  
pp. 58 ◽  
Author(s):  
Bhargava Nukala ◽  
Taro Nakano ◽  
Amanda Rodriguez ◽  
Jerry Tsay ◽  
Jerry Lopez ◽  
...  
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Normal Subjects ◽  
Balance Disorders ◽  
Real Time Classification

scholarly journals Clinical Measurement of Sit-to-Stand Performance in People With Balance Disorders: Validity of Data for the Five-Times-Sit-to-Stand Test

2005 ◽  
Vol 85 (10) ◽  
pp. 1034-1045 ◽  
Author(s):  
Susan L Whitney ◽  
Diane M Wrisley ◽  
Gregory F Marchetti ◽  
Michael A Gee ◽  
Mark S Redfern ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Balance Disorders ◽  
Stand Test ◽  
Balance Confidence ◽  
Sit To Stand ◽  
Dynamic Gait Index ◽  
Dynamic Gait ◽  
Confidence Scale ◽  
Better Than

Abstract Background and Purpose. People with balance disorders are characterized as having difficulty with transitional movements, such as the sit-to-stand movement. A valid and feasible tool is needed to help clinicians quantify the ability of people with balance disorders to perform transitional movements. The purpose of this study was to describe the concurrent and discriminative validity of data obtained with the Five-Times-Sit-to-Stand Test (FTSST). The FTSST was compared with the Activities-specific Balance Confidence Scale (ABC) and the Dynamic Gait Index (DGI). Subjects and Methods. Eighty-one subjects without balance disorders and 93 subjects with balance disorders were recruited for the study. Each subject was asked to stand from a 43-cm-high chair 5 times as quickly as possible. The ABC and DGI scores were recorded. Results. Subjects with balance disorders performed the FTSST more slowly than subjects without balance disorders. Discriminant analysis demonstrated that the FTSST correctly identified 65% of subjects with balance dysfunction, the ABC identified 80%, and the DGI identified 78%. The ability of the FTSST to identify subjects with balance dysfunction was better for subjects younger than 60 years of age (81%). Discussion and Conclusion. The FTSST displays discriminative and concurrent validity properties that make this test potentially useful in clinical decision making, although overall the ABC and the DGI are better than the FTSST at discriminating between subjects with and subjects without balance disorders.

Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier

2017 ◽  
Vol 5 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Taro Nakano ◽  
B.T. Nukala ◽  
J. Tsay ◽  
Steven Zupancic ◽  
Amanda Rodriguez ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Human Subjects ◽  
Wearable Sensors ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Dynamic ◽  
Linear Svm ◽  
Dynamic Gait

Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.

scholarly journals Design and Implementation of a Connection between Augmented Reality and Sensors

Robotics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Marlon Aguero ◽  
Dilendra Maharjan ◽  
Maria del Pilar Rodriguez ◽  
David Dennis Lee Mascarenas ◽  
Fernando Moreu
Keyword(s):  
Augmented Reality ◽  
Data Collection ◽  
Real Time ◽  
Strain Gauge ◽  
Low Cost ◽  
Contextual Information ◽  
Wireless Sensor ◽  
Infrastructure Management ◽  
Data Collection And Analysis ◽  
The Way

Wireless sensor networks (WSN) are used by engineers to record the behavior of structures. The sensors provide data to be used by engineers to make informed choices and prioritize decisions concerning maintenance procedures, required repairs, and potential infrastructure replacements. However, reliable data collection in the field remains a challenge. The information obtained by the sensors in the field frequently needs further processing, either at the decision-making headquarters or in the office. Although WSN allows data collection and analysis, there is often a gap between WSN data analysis results and the way decisions are made in industry. The industry depends on inspectors’ decisions, so it is of vital necessity to improve the inspectors’ access in the field to data collected from sensors. This paper presents the results of an experiment that shows the way Augmented Reality (AR) may improve the availability of WSN data to inspectors. AR is a tool which overlays the known attributes of an object with the corresponding position on the headset screen. In this way, it allows the integration of reality with a virtual representation provided by a computer in real time. These additional synthetic overlays supply data that may be unavailable otherwise, but it may also display additional contextual information. The experiment reported in this paper involves the application of a smart Strain Gauge Platform, which automatically measures strain for different applications, using a wireless sensor. In this experiment, an AR headset was used to improve actionable data visualization. The results of the reported experiment indicate that since the AR headset makes it possible to visualize information collected from the sensors in a graphic form in real time, it enables automatic, effective, reliable, and instant communication from a smart low-cost sensor strain gauge to a database. Moreover, it allows inspectors to observe augmented data and compare it across time and space, which then leads to appropriate prioritization of infrastructure management decisions based on accurate observations.

scholarly journals Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

2018 ◽  
Author(s):  
Javier Garcia-Guzman ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Max Montalvo Martinez ◽  
María Jesús López Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
State Variables ◽  
Angle Estimation ◽  
Experimental Environment ◽  
Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

scholarly journals Low-Cost WASN for Real-Time Soundmap Generation

2021 ◽  
Vol 6 (1) ◽  
pp. 57
Author(s):  
Gerardo José Ginovart-Panisello ◽  
Ester Vidaña-Vila ◽  
Selene Caro-Via ◽  
Carme Martínez-Suquía ◽  
Marc Freixes ◽  
...  
Keyword(s):  
Real Time ◽  
Acoustic Analysis ◽  
Road Traffic ◽  
Low Cost ◽  
Traffic Noise ◽  
The Elderly ◽  
Road Traffic Noise ◽  
Public Places ◽  
Changing Patterns ◽  
Real Scenario

Recent advances in technology have enabled the development of affordable low-cost acoustic monitoring systems, as a response of several fields of application that require a close acoustic analysis in real-time: road traffic noise in crowded cities, biodiversity conservation in natural parks, behavioural tracking in the elderly living alone and even surveillance in public places for safety reasons. This paper presents a low-cost wireless acoustic sensor network developed to gather acoustic data to build a 24/7 real-time soundmap. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally to obtain nonsensitive data (i.e., equivalent continuous loudness levels or acoustic event labels) that are sent to a cloud server. Moreover, it has also been studied the placement of the acoustic sensors in a real scenario, following acoustics criteria. The ultimate goal of the deployed system is to enable the following functions: (i) to measure the Leq in real-time in a predefined window, (ii) to identify changing patterns in the previous measurements so that anomalous situations can be detected and (iii) to prevent and attend potential irregular situations. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioural and environmental information, in order to take decisions in real-time.

scholarly journals A disturbed processing of graviceptive pathways may be involved in the pathophysiology of balance disorders in patients with multiple sclerosis

2016 ◽  
Vol 74 (2) ◽  
pp. 106-111 ◽  
Author(s):  
Bruna Antinori Vignola da Fonseca ◽  
Cristiana Borges Pereira ◽  
Frederico Jorge ◽  
Renata Simm ◽  
Samira Apostolos-Pereira ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Balance Disorders ◽  
Visual Vertical ◽  
Perception Of Verticality ◽  
Risk Of Fall ◽  
Multiple Sclerosis Patients ◽  
Dynamic Gait Index ◽  
Dynamic Gait ◽  
The Relationship ◽  
Gait Index

ABSTRACT The purpose of this study was to determine the relationship between perception of verticality and balance disorders in multiple sclerosis patients. We evaluated patients and healthy controls. Patients were divided into two groups according to their risk of fall, with or without risk of fall, measured by a Dynamic Gait Index scale. Graviceptive perception was assessed using the subjective visual vertical test. Patients with risk of fall showed worse perception than those without risk of fall, p < 0.001. Misperception of verticality was correlated with the dynamic gait index scores (p < 0.001), suggesting that the larger the error for verticality judgment, the greater risk for falling. Considering that the perception of verticality is essential for postural control, our results suggested that the disturbed processing of graviceptive pathways may be involved in the pathophysiology of balance disorders in these patients.

scholarly journals Real-Time Visual Tracking of Moving Targets Using a Low-Cost Unmanned Aerial Vehicle with a 3-Axis Stabilized Gimbal System

2020 ◽  
Vol 10 (15) ◽  
pp. 5064
Author(s):  
Xuancen Liu ◽  
Yueneng Yang ◽  
Chenxiang Ma ◽  
Jie Li ◽  
Shifeng Zhang
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Flight Control ◽  
Low Cost ◽  
Detection Algorithm ◽  
Outdoor Environment ◽  
Measurement Unit ◽  
Tracking Tasks ◽  
Great Performance ◽  
Aerial Vehicle

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping, which are widely used in reconnaissance, surveillance, and target acquisition (RSTA) applications. In this paper, we present an onboard vision-based system for low-cost UAVs to autonomously track a moving target. Real-time visual tracking is achieved by using an object detection algorithm based on the Kernelized Correlation Filter (KCF) tracker. A 3-axis gimbaled camera with separate Inertial Measurement Unit (IMU) is used to aim at the selected target during flights. The flight control algorithm for tracking tasks is implemented on a customized quadrotor equipped with an onboard computer and a microcontroller. The proposed system is experimentally validated by successfully chasing a ground and aerial target in an outdoor environment, which has proven its reliability and efficiency.

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