Exploration of Machine Learning to Identify Community
Dwelling Older Adults with Balance Dysfunction Using
Short Duration Accelerometer Data

Yang Hu*, Alka Bishnoi*, Rachneet Kaur,
Richard Sowers, Manuel Hernandez
University of Illinois at Urbana-Champaign
IEEE EMBC 2020

[Code]
[Slides]
[Video]
[Paper]

Abstract

The incidence of fall-related injuries in older adults is high. Given the significant and adverse outcomes that arise from injurious falls in older adults, it is of the utmost importance to identify older adults at greater risk for falls as early as possible. Given that balance dysfunction provides a significant risk factor for falls, an automated and objective identification of balance dysfunction in community dwelling older adults using wearable sensor data when walking may be beneficial. In this study, we examine the feasibility of using wearable sensors, when walking, to identify older adults who have trouble with balance at an early stage using state-of-the-art machine learning techniques. We recruited 21 community dwelling older women. The experimental paradigm consisted of two tasks: Normal walking with a self-selected comfortable speed on an instrumented treadmill and a test of reflexive postural response, using the motor control test (MCT). Based on the MCT, identification of older women with low or high balance function was performed. Using short duration accelerometer data from sensors placed on the knee and hip while walking, supervised machine learning was carried out to classify subjects with low and high balance function. Using a Gradient Boosting Machine (GBM) algorithm, we classified balance function in older adults using 60 seconds of accelerometer data with an average cross validation accuracy of 91.5% and area under the receiver operating characteristic curve (AUC) of 0.97. Early diagnosis of balance dysfunction in community dwelling older adults through the use of user friendly and inexpensive wearable sensors may help in reducing future fall risk in older adults through earlier interventions and treatments, and thereby significantly reduce associated healthcare costs.

Figure: Filtered acceleration coordinates. Comparing 10 seconds of x, y, z-acceleration coordinates from the two accelerometers at hip (H) and knee (K) for a subject with low (A) and high (B) balance.


Explanatory video

A detailed video, corresponding slides: [Detailed slides].


References

Yang Hu*, Alka Bishnoi*, Rachneet Kaur, Richard Sowers, Manuel E Hernandez. Exploration of Machine Learning to Identify Community Dwelling Older Adults with Balance Dysfunction Using Short Duration Accelerometer Data. In IEEE EMBC 2020 [BibTex] [Text]


Acknowledgements

We would like to thank all of the subjects who participated in this study. We would also like to thank Yanqing Lyu, Nicole Cho and members of the Mobility and Fall Prevention Lab for their contributions to the study, and the Campus Research Board for financial support of this work. RK is thankful to William Chittenden for the William A. Chittenden Award.

Website adapted from Jingxiang, Richard and Deepak.