Abstract
Faller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers and non-fallers, completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. After segmenting straight and turn sections, cross validation tests were conducted on straight and turn walking features to assess classification performance. The best “classifier model—feature selector” combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, and 0.44 Matthew’s Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back) achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score. All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data. Combining turn and straight walking features decreased performance metrics compared to turn features for similar classifier model—feature selector combinations.
Highlights
Falls within elderly populations are a growing public health concern, with fatal and non-fatal fall injuries costing an estimated $23.3 billion in the United States, with a projected cost of $52 billion by 2020 [1,2]
This paper presents a novel wearable-sensor based faller classification method, using walking-turn accelerometer-based features, and compares older-adult faller classification using straight and turn walking features
To promote classification generalizability and reliability, and to avoid methodological problems associated with validation and training-testing protocols seen in the fall-risk assessment literature [49], two stratified cross-validation methods were used
Summary
Falls within elderly populations are a growing public health concern, with fatal and non-fatal fall injuries costing an estimated $23.3 billion in the United States, with a projected cost of $52 billion by 2020 [1,2]. Fall risk detection and subsequent treatment are needed to mitigate fall incidence and improve quality of life for elderly individuals [3,4,5]. Wearable sensors that can be applied at the point-of-care [6] can facilitate quantitative assessments in clinical or older-adult care environments. Reviews of inertial-sensor applications for fall-risk classification in older-adults have recommended further research to determine if wearable sensors can be used to improve fall-risk prediction as a stand-alone assessment tool or supplement to clinical tests [7,8]. Combining appropriate wearable-sensor based features with machine learning techniques could advance fall-risk prediction tools and improve services for elderly people at risk of falling [6,9].
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