Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests

BMC Medical Informatics and Decision Making

Tien-Lung Sun¹  Kwok-Leung Tsui²  Terrence E. Murphy³  Lisha Yu⁴  Hailiang Wang⁵  Yang Zhao⁶ 

[1]Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, Taiwan

[2]Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, USA

[3]Department of Internal Medicine, Yale University School of Medicine, New Haven, USA

[4]School of Data Science, City University of Hong Kong, Kowloon, Hong Kong

[5]School of Design, The Hong Kong Polytechnic University, Kowloon, Hong Kong

[6]School of Public Health (Shenzhen), Sun Yat-Sen University, Guangdong, People’s Republic of China

关键词: Balance and mobility;    Fall;    Elderly care;    Sensor;    Data mining;    Timed up and go;   

DOI  :  10.1186/s12911-021-01463-4

来源: Springer

【 摘 要 】

BackgroundPoor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score.MethodsData were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation.ResultsEighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively.ConclusionsThe study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

【 授权许可】

CC BY