scholarly journals SENSOR-BASED ASSESSMENT OF FALLS RISK OF THE TIMED UP AND GO IN REAL-WORLD SETTINGS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S10-S10
Author(s):  
Charlene C Quinn ◽  
Barry R Greene ◽  
Killian McManus ◽  
Stephen J Redmond ◽  
Brian Caulfield
Keyword(s):  
Fall Risk ◽  
Population Based ◽  
Falls Prevention ◽  
Sensor Data ◽  
Gait Velocity ◽  
Community Settings ◽  
Mobility Impairment ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Slow Walking Speed

Abstract Falls are the leading cause of older adult injury and cost $50bn annually. New digital technologies can quantitatively measure falls risk. Objective is to report on a validated wearable sensor-based Timed Up and Go (QTUG) assessment detailing 11 measures of falls risk, frailty and mobility impairment in older adults in six countries in 38 clinical and community settings. Second objective is to generate individual targeted falls prevention programs. 14,611 QTUG records from 8,521 participants (63% female) (72.7±10.7 years) available for analysis. QTUG time was 13.9±7.4 s; gait velocity was 101.9±32.5 cm/s. 25.8% of patients reported falling in previous 12 months; 26.2% of patients were at high fall risk. 21.5% not reporting a fall, were high fall risk. Participants had slow walking speed (29.8%); high gait variability (19.8%); problems with transfers (17.5%). Easily captured and interpreted sensor data is useful in a population-based approach to quantify falls risk stratification.

scholarly journals Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Barry R. Greene ◽  
Killian McManus ◽  
Stephen J. Redmond ◽  
Brian Caulfield ◽  
Charlene C. Quinn
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Falls Prevention ◽  
Sensor Data ◽  
Emergency Department Utilization ◽  
Mobility Impairment ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Assessment Protocol ◽  
Risk Of Falls

AbstractFalls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the “first fall”, delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning). 8521 participants (72.7 ± 12.0 years, 5392 female) from six countries were assessed using a digital falls risk assessment protocol. Data consisted of wearable sensor data captured during the Timed Up and Go (TUG) test along with self-reported questionnaire data on falls risk factors, applied to previously trained and validated classifier models. We found that 25.8% of patients reported a fall in the previous 12 months, of the 74.6% of participants that had not reported a fall, 21.5% were found to have a high predicted risk of falls. Overall 26.2% of patients were predicted to be at high risk of falls. 29.8% of participants were found to have slow walking speed, while 19.8% had high gait variability and 17.5% had problems with transfers. We report an observational study of results obtained from a novel digital fall risk assessment protocol. This protocol is intended to support the early identification of older adults at risk of falls and inform the creation of appropriate personalized interventions to prevent falls. A population-based approach to management of falls using objective measures of falls risk and mobility impairment, may help reduce unnecessary outpatient and emergency department utilization by improving risk prediction and stratification, driving more patients towards clinical and community-based falls prevention activities.

scholarly journals Using a Stacked Autoencoder for Mobility and Fall Risk Assessment via Time–Frequency Representations of the Timed Up and Go Test

2021 ◽  
Vol 12 ◽  
Author(s):  
Shih-Hai Chen ◽  
Chia-Hsuan Lee ◽  
Bernard C. Jiang ◽  
Tien-Lung Sun
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Learning Algorithm ◽  
The Elderly ◽  
Developed Countries ◽  
Mobility Impairment ◽  
Timed Up And Go ◽  
Fall Risk Assessment ◽  
Time Frequency ◽  
Stacked Autoencoder

Fall risk assessment is very important for the graying societies of developed countries. A major contributor to the fall risk of the elderly is mobility impairment. Timely detection of the fall risk can facilitate early intervention to avoid preventable falls. However, continuous fall risk monitoring requires extensive healthcare and clinical resources. Our objective is to develop a method suitable for remote and long-term health monitoring of the elderly for mobility impairment and fall risk without the need for an expert. We employed time–frequency analysis (TFA) and a stacked autoencoder (SAE), which is a deep neural network (DNN)-based learning algorithm, to assess the mobility and fall risk of the elderly according to the criteria of the timed up and go test (TUG). The time series signal of the triaxial accelerometer can be transformed by TFA to obtain richer image information. On the basis of the TUG criteria, the semi-supervised SAE model was able to achieve high predictive accuracies of 89.1, 93.4, and 94.1% for the vertical, mediolateral and anteroposterior axes, respectively. We believe that deep learning can be used to analyze triaxial acceleration data, and our work demonstrates its applicability to assessing the mobility and fall risk of the elderly.

A Novel Approach for Fall Risk Prediction Using the Inertial Sensor Data From the Timed-Up-and-Go Test in a Community Setting

2020 ◽  
Vol 20 (16) ◽  
pp. 9339-9350 ◽  
Author(s):  
Yu-Cheng Hsu ◽  
Yang Zhao ◽  
Kuang-Hui Huang ◽  
Ya-Ting Wu ◽  
Javier Cabrera ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Fall Risk ◽  
Inertial Sensor ◽  
Community Setting ◽  
Sensor Data ◽  
Timed Up And Go ◽  
Novel Approach

scholarly journals Inertial Sensor-Based Instrumented Cane for Real-Time Walking Cane Kinematics Estimation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4675
Author(s):  
Ibai Gorordo Fernandez ◽  
Siti Anom Ahmad ◽  
Chikamune Wada
Keyword(s):  
Real Time ◽  
Fall Risk ◽  
Inertial Sensor ◽  
Feedback System ◽  
Sensor Data ◽  
Mobility Impairment ◽  
Orientation Estimation ◽  
Contact Phase ◽  
Optical Motion ◽  
Phase Prediction

Falls are among the main causes of injuries in elderly individuals. Balance and mobility impairment are major indicators of fall risk in this group. The objective of this research was to develop a fall risk feedback system that operates in real time using an inertial sensor-based instrumented cane. Based on inertial sensor data, the proposed system estimates the kinematics (contact phase and orientation) of the cane. First, the contact phase of the cane was estimated by a convolutional neural network. Next, various algorithms for the cane orientation estimation were compared and validated using an optical motion capture system. The proposed cane contact phase prediction model achieved higher accuracy than the previous models. In the cane orientation estimation, the Madgwick filter yielded the best results overall. Finally, the proposed system was able to estimate both the contact phase and orientation in real time in a single-board computer.

scholarly journals Technologies for fall risk assessment and conceptual design in personal health record system

2019 ◽  
Vol 11 (1-2) ◽  
pp. 53-67
Author(s):  
Milla Sinikka Immonen ◽  
Heidi Similä ◽  
Mikko Lindholm ◽  
Raija Korpelainen ◽  
Timo Jämsä
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Personal Health Record ◽  
Public Health Problem ◽  
Falls Prevention ◽  
Screening Tools ◽  
Sensor Data ◽  
Personal Health ◽  
Health Record ◽  
Fall Risk Assessment

Falls among older people are a major economic and public health problem. Due to the demographic change and aging of populations, there is an urgent need for accurate screening tools to identify those at risk to target effective falls prevention strategies. Clinical fall risk assessments are costly and time-consuming and thus cannot be performed frequently. Technologies provide means for assessing fall risk during daily living, making self-evaluations and fast methods for fall risk assessment for professional use. This study collects and evaluates existing technological solutions for fall risk assessment including various different sensor technologies. The study also presents one easy to use solution for assessing fall risk and suggests a concept-design for integrating sensor-based solutions into the Finnish national Kanta Personal Health Record. The optimal solution for technological fall risk assessment is still unclear. A wide implementation still requires extensive validation studies, adoption to health care processes and novel IoT -solutions for collecting large amounts of sensor data. Thorough methods should be utilised in designing the privacy and security aspects of fall risk assessment solutions, as well as different user profiles, to allow suitable interfaces and visualisations to users. It should always be clear what kind of data are collected from users and how the data are utilised. The consent of the users should also always be collected.

scholarly journals Augmenting Clinical Outcome Measures of Gait and Balance with a Single Inertial Sensor in Age-Ranged Healthy Adults

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4537 ◽  
Author(s):  
O’Brien ◽  
Hidalgo-Araya ◽  
Mummidisetty ◽  
Vallery ◽  
Ghaffari ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Inertial Sensor ◽  
Intraclass Correlation ◽  
Sensor Data ◽  
Gait Velocity ◽  
Clinical Test ◽  
Time Step ◽  
Timed Up And Go ◽  
High Resolution Data ◽  
Increased Risk

Gait and balance impairments are linked with reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous, high-resolution data. This study tested and validated the utility of a single IMU to quantify gait and balance features during routine clinical outcome tests, and evaluated changes in sensor-derived measurements with age, sex, height, and weight. Age-ranged, healthy individuals (N = 49, 20–70 years) wore a lower back IMU during the 10 m walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). Spatiotemporal gait parameters computed from the sensor data were validated against gold standard measures, demonstrating excellent agreement for stance time, step time, gait velocity, and step count (intraclass correlation (ICC) > 0.90). There was good agreement for swing time (ICC = 0.78) and moderate agreement for step length (ICC = 0.68). A total of 184 features were calculated from the acceleration and angular velocity signals across these tests, 36 of which had significant correlations with age. This approach was also demonstrated for an individual with stroke, providing higher resolution information about balance, gait, and mobility than the clinical test scores alone. Leveraging mobility data from wireless, wearable sensors can help clinicians and patients more objectively pinpoint impairments, track progression, and set personalized goals during and after rehabilitation.

scholarly journals Mobility Screening for Fall Risk Assessment in a Population-Based Sample of Older Adults: An Analysis From the CLSA

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 269-269
Author(s):  
Marla Beauchamp ◽  
Ayse Kuspinar ◽  
Nazmul Sohel ◽  
Alexandra Mayhew ◽  
Lauren Griffith ◽  
...  
Keyword(s):  
Older Adults ◽  
Risk Assessment ◽  
Fall Risk ◽  
Population Based ◽  
Community Dwelling ◽  
Receiver Operating Curve ◽  
Inclusion Criteria ◽  
Timed Up And Go ◽  
Fall Risk Assessment ◽  
Acceptable Accuracy

Abstract Existing guidelines for fall prevention in older adults recommend mobility screening for fall risk assessment; however, there is no consensus on which test to use and at what cut-off. This study aimed to determine the accuracy and optimal cut-off values of commonly used mobility tests for predicting falls in the Canadian Longitudinal Study on Aging (CLSA). Mobility tests at baseline included the Timed Up and Go (TUG), Single Leg Stance (SLS), chair-rise, and gait speed test. Inclusion criteria were: age ≥ 65 years and history of a fall or mobility problem at baseline. Accuracy of fall prediction at 18-months for each mobility test was measured by the area under the receiver operating curve (AUC). Of 1,121 participants that met inclusion criteria (mean age 75.2 ± 5.9 years; 66.6% women), 218 (19.4%) participants reported ≥1 fall at 18-months. None of the mobility tests achieved acceptable accuracy for identifying individuals with ≥1 fall at follow-up. Among women 65-74 and 75-85 years, the TUG identified recurrent fallers (≥2 falls) with optimal cut-off scores of 14.1 and 12.9 seconds (both AUCs 0.70), respectively. Among men 65-74 years, only the SLS showed acceptable accuracy (AUC 0.85) for identifying recurrent fallers with an optimal cut-off of 3.6 seconds. Our findings indicate that for a population-based sample of community-dwelling older adults, commonly used mobility tests do not have sufficient accuracy for identifying fallers. The TUG and SLS can identify older adults at risk for recurrent falls, however their accuracy and cut-off values vary by age and sex.

scholarly journals Evaluating audio-visual falls prevention messages with community-dwelling older people using a World Café forum approach

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Lex D. de Jong ◽  
Jacqueline Francis-Coad ◽  
Chris Wortham ◽  
Terry P. Haines ◽  
Dawn A. Skelton ◽  
...  
Keyword(s):  
Older People ◽  
Population Based ◽  
Community Dwelling ◽  
Falls Prevention ◽  
Falls Risk ◽  
Personal Connection ◽  
Prevention Messages ◽  
Prevention Campaign ◽  
World Cafe ◽  
Prevention Behavior

Abstract Background Falls risk increases sharply with older age but many older people are unaware or underestimate their risk of falling. Increased population-based efforts to influence older people’s falls prevention behavior are urgently needed. The aim of this study was to obtain a group of older people’s collective perspectives on newly developed prototypes of audio-visual (AV) falls prevention messages, and evaluate changes in their falls prevention behaviour after watching and discussing these. Methods A mixed-method study using a community World Café forum approach. Results Although the forum participants (n = 38) mostly responded positively to the three AV messages and showed a significant increase in their falls prevention capability and motivation after the forum, the participants collectively felt the AV messages needed a more inspirational call to action. The forum suggested this could be achieved by means of targeting the message and increasing the personal connection. Participants further suggested several alternatives to online falls prevention information, such as printed information in places in the community, as a means to increase opportunity to seek out falls prevention information. Conclusions Falls prevention promotion messages need to be carefully tailored if they are to be more motivating to older people to take action to do something about their falls risk. A wider variety of revised and tailored AV messages, as one component of a community-wide falls prevention campaign, could be considered in an effort to persuade older people to take decisive action to do something about their falls risk. Trial registration This study was registered prospectively: NCT03154788. Registered 11 May 2017.

scholarly journals Implementation and initial evaluation of falls risk reduction resources in a rural Native American Community

2021 ◽  
Vol 8 (S2) ◽  
Author(s):  
Kyle M. Knight
Keyword(s):  
Native American ◽  
Risk Reduction ◽  
Integrated Approach ◽  
Health Care Facilities ◽  
Falls Prevention ◽  
Timed Up And Go ◽  
Falls Risk ◽  
Functional Reach ◽  
Increased Risk ◽  
Serious Injury

Abstract Background Although falls are common and can cause serious injury to older adults, many health care facilities do not have falls prevention resources available. Falls prevention resources can reduce injury and mortality rates. Using the Centers for Disease Control and Prevention’s (CDC) Stopping Elderly Accidents, Deaths & Injuries (STEADI) model, a falls risk clinic was implemented in a rural Indian Health Service (IHS) facility. Methods A Fall Risk Questionnaire was created and implemented into the Provider’s Electronic Health Records system interface to streamline provider screening and referral of patients who may be at risk for falls to a group falls risk reduction class. Results Participants exhibited average improvements in the Timed Up and Go (6.8 s) (P = 0.0001), Five-Time Sit-to-Stand (5.1 s) (P = 0.0002), and Functional Reach (3.6 inches) (P = 1.0) tests as compared to their own baseline. Results were analyzed via paired t test. 71% of participants advanced out of an “increased risk for falls” category in at least one outcome measure. Of the participants to complete the clinic, all were successfully contacted and three (18%) reported one or more falls at the 90-day mark, of which one (6%) required a visit to the Emergency Department but did not require hospital admission. Conclusions In regards to reducing falls in the community, per the CDC STEADI model, an integrated approach is best. All clinicians can play a part in reducing elder falls.

Sign in / Sign up

Export Citation Format

Share Document