Abstract
BackgroundClinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest.ObjectiveThe objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults.MethodsIn total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals.ResultsThe framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN.ConclusionsThe sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community’s burden of continuous health monitoring.
Highlights
Falls prevail among the aging population, and led to more than $30 billion in direct medical costs in 2015 [1]
The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class support vector machine (SVM) and k-nearest neighborhood (k-NN)
The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and enabled an efficient balance assessment tool for medical professionals
Summary
Falls prevail among the aging population, and led to more than $30 billion in direct medical costs in 2015 [1]. Falls pose a threat to the physical and psychological aspects of older adults’ health [3,4]. 1 (page number not for citation purposes) to identify older adults at the risk of falls and take interventions in advance [5]. Outpatients and community dwellers usually suffer less from illnesses; intrinsic mobility and balance are the most discriminative intrinsic indicators of falls [6]. Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. The current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest
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