Using a Stacked Autoencoder for Mobility and Fall Risk Assessment via Time-Frequency Representations of the Timed Up and Go Test.

Fall risk assessment is very important for the graying societies of developed countries. A major contributor to the fall risk of the elderly is mobility impairment. Timely detection of the fall risk can facilitate early intervention to avoid preventable falls. However, continuous fall risk monitoring requires extensive healthcare and clinical resources. Our objective is to develop a method suitable for remote and long-term health monitoring of the elderly for mobility impairment and fall risk without the need for an expert. We employed time-frequency analysis (TFA) and a stacked autoencoder (SAE), which is a deep neural network (DNN)-based learning algorithm, to assess the mobility and fall risk of the elderly according to the criteria of the timed up and go test (TUG). The time series signal of the triaxial accelerometer can be transformed by TFA to obtain richer image information. On the basis of the TUG criteria, the semi-supervised SAE model was able to achieve high predictive accuracies of 89.1, 93.4, and 94.1% for the vertical, mediolateral and anteroposterior axes, respectively. We believe that deep learning can be used to analyze triaxial acceleration data, and our work demonstrates its applicability to assessing the mobility and fall risk of the elderly.

Estimating Postural Stability Using Improved Permutation Entropy via TUG Accelerometer Data for Community-Dwelling Elderly People.

To develop an effective fall prevention program, clinicians must first identify the elderly people at risk of falling and then take the most appropriate interventions to reduce or eliminate preventable falls. Employing feature selection to establish effective decision making can thus assist in the identification of a patient's fall risk from limited data. This work therefore aims to supplement professional timed up and go assessment methods using sensor technology, entropy analysis, and statistical analysis. The results showed the different approach of applying logistic regression analysis to the inertial data on a fall-risk scale to allow medical practitioners to predict for high-risk patients. Logistic regression was also used to automatically select feature values and clinical judgment methods to explore the differences in decision making. We also calculate the area under the receiver-operating characteristic curve (AUC). Results indicated that permutation entropy and statistical features provided the best AUC values (all above 0.9), and false positives were avoided. Additionally, the weighted-permutation entropy/statistical features test has a relatively good agreement rate with the short-form Berg balance scale when classifying patients as being at risk. Therefore, the proposed methodology can provide decision-makers with a more accurate way to classify fall risk in elderly people.