+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Quantitative analysis of the fall-risk assessment test with wearable inertia sensors

Quantitative analysis of the fall-risk assessment test with wearable inertia sensors

Conference Proceedings 2013: 7217-7220

We performed a quantitative analysis of the fall-risk assessment test using a wearable inertia sensor focusing on two tests: the time up and go (TUG) test and the four square step test (FSST). These tests consist of various daily activities, such as sitting, standing, walking, stepping, and turning. The TUG test was performed by subjects at low and high fall risk, while FSST was performed by healthy elderly and hemiplegic patients with high fall risk. In general, the total performance time of activities was evaluated. Clinically, it is important to evaluate each activity for further training and management. The wearable sensor consisted of an accelerometer and angular velocity sensor. The angular velocity and angle of pitch direction were used for TUG evaluation, and those in the pitch and yaw directions at the thigh were used for FSST. Using the threshold of the angular velocity signal, we classified the phase corresponding to each activity. We then observed the characteristics of each activity and recommended suitable training and management. The wearable sensor can be used for more detailed evaluation in fall risk management. The wearable sensor can be used more detailed evaluation for fall-risk management test.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 055319038

Download citation: RISBibTeXText

PMID: 24111410

DOI: 10.1109/embc.2013.6611223

Related references

Assessing fall risk using wearable sensors: a practical discussion. A review of the practicalities and challenges associated with the use of wearable sensors for quantification of fall risk in older people. Zeitschrift für Gerontologie und Geriatrie 45(8): 694-706, 2012

Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering 26(3): 573-582, 2018

Quantitative analysis of 180 degree turns for fall risk assessment using video sensors. Conference Proceedings 2011: 7606-7609, 2011

Prospective Fall-Risk Prediction Models for Older Adults Based on Wearable Sensors. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25(10): 1812-1820, 2017

Recognition of Sedentary Behavior by Machine Learning Analysis of Wearable Sensors during Activities of Daily Living for Telemedical Assessment of Cardiovascular Risk. Sensors 18(10):, 2018

Quantitative wearable sensors for objective assessment of Parkinson's disease. Movement Disorders 28(12): 1628-1637, 2013

Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14(10): 19806-19842, 2014

Quantitative assessment of motor function in patients with Parkinson's disease using wearable sensors. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 35(2): 206-213, 2018

Quantitative analysis of fall risk using TUG test. Computer Methods in Biomechanics and Biomedical Engineering 18(4): 426-437, 2015

Activity Monitoring and Heart Rate Variability as Indicators of Fall Risk: Proof-of-Concept for Application of Wearable Sensors in the Acute Care Setting. Journal of Gerontological Nursing 43(7): 53-62, 2017

Wearable Inertial Sensors Allow for Quantitative Assessment of Shoulder and Elbow Kinematics in a Cadaveric Knee Arthroscopy Model. Arthroscopy 33(12): 2110-2116, 2017

Wearable technology and ECG processing for fall risk assessment, prevention and detection. Conference Proceedings 2015: 7740-7743, 2015

Wearable sensors for reliable fall detection. Conference Proceedings 4: 3551-3554, 2005

How well do Parkinson's disease patients turn in bed? Quantitative analysis of nocturnal hypokinesia using multisite wearable inertial sensors. Parkinsonism and Related Disorders 23: 10-16, 2016

Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients. Bmc Medical Informatics and Decision Making 11: 48, 2011