Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics

Abstract

Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians’ attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician’s judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects’ locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults’ fall-risk status with relatively high sensitivity to geriatrician’s expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants’ gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations.

Classification of Subjects With Balance Disorders Using 1D-CNN and Inertial Sensors

“…The result of subjects classification gives the accuracy of 87.5%. These results are better than some of the results obtained with similar research [23], while using the smaller number of sensors. It should also be noted that the study group revealed only very subtle balance and gait abnormalities in the clinical examination.…”

“…With regard to research concerning the use of CNNs to analyse IMU data, Roshdibenam et al proposed a solution for detecting risk of falls in elders using CNN model with the raw kinematics signal. The signal was obtained during the TUG test from proprietary IMU sensors, developed in MEMS technology, placed on the neck and feet [23]. The CNN model was using three seconds time series segments as the input, multiple deep layers and binary output distinguishing faller from non-faller.…”

Perfil epidemiológico e avaliação do risco de queda em idosos usuários da Estratégia Saúde da Família

“…Apesar de autores considerarem essa análise ainda limitada e influenciada por outras variáveis, estudo retrospectivo evidenciou associação significativa positiva entre o tempo de realização do teste e histórico de quedas em idoso. Além disso, o teste foi relatado como de sensibilidade considerável quando se trata de idosos com dependência funcional (Roshdibenam, et al, 2021).…”

How accurate are geriatricians’ fall predictions?

“…All subjects were sent questionnaires 6 to12 months (mean for all subjects was 9 months) following clinic evaluation to query if they had fallen since the initial evaluations. The inertial measurement data were stored for use in a follow-up experiment reported elsewhere [ 9 ].…”