Electromyography and dynamometry in the prediction of risk of falls in the elderly using machine learning tools

• Published in: Biomedical signal processing and control 2024-02, Vol.88, p.105635, Article 105635
• Main Authors: da Silva, Daniele Alves, Castelo Branco, Nayra Ferreira Lima, de Andrade Mesquita, Laiana Sepúlveda, Castelo Branco, Hermes Manoel Galvão, de Alencar Barreto, Guilherme
• Format: Article
• Language: English
• Subjects: Berg balance scale; Elderly; Fall risk; Feature selection; Machine learning
• ISSN: 1746-8094
• DOI: 10.1016/j.bspc.2023.105635

•Assessing the risk of falls in the elderly is important for effective care.•ML tools can be used to predict the risk of falls with both EMG and force signals.•ML tools are more effective in predicting BBS score using EMG signal. The aging process affects mechanisms for maintaining physical integrity. The assessment of the risk of falls is routine in the services of assistance to the elderly, but subjective and time-consuming, so that the automation of the process is desirable as a supporting tool. In this regard, the aim of this study is to carry out a comprehensive evaluation study on the use of machine learning techniques as an instrument to predict the Berg Balance Scale (BBS) score, using different sets of electromyographic and dynamometric data collected during a voluntary isometric contraction. Thirty participants were evaluated with the BBS and with electromyography and dynamometry of the vastus lateralis, biceps femoris, lateral gastrocnemius and tibialis anterior muscles during maximal isometric voluntary contractions. After pre-processing the dataset, the features were selected through principal components analysis (PCA), correlation-based function select (CFS) and relief-F to then be applied to the multilayer perceptron (MLP), random forest (RF), random tree (RT), k-nearest neighbors (KNN), Multiple Linear Regression (MLR), and least-squares support vector regression (LS-SVR). From the fitted regression models, our ultimate goal is to infer which selected features correlate most with the risk of falling for elderly people and how those features connect themselves to certain groups of muscles. In this regard, the features extracted from myoelectric signals proved to be more effective for use in predicting the risk of falls in the elderly in relation to the force-related signal. The proposed models obtained good results for predicting the BBS score and classifying the risk of falls.