![daz models in heel stance daz models in heel stance](https://storage.googleapis.com/farnsworth-dev.appspot.com/p/37135/i/00-daz3d_capsces-poses-and-expressions-for-damien-demon_.jpg)
In addition, it is necessary to identify in advance the influential predictor factors affecting falls through a gait performance test and use them as fundamental data to prevent falls. Thus, the underlying causes of fall must be identified to predict their risk. Furthermore, gait abnormalities or decreased gait ability decisively imply a reduced physical fitness as a result of aging 9, which may cause a falls. In particular, gait abnormalities in aging, including slow walking speed, greater gait variability, and shorter steps, are considered one of the greatest risk factors for falls 5, 6, 7, 8. Numerous studies have revealed a relationship between falls and risk factors such as advanced age 2, declined cognitive function 3, strength deficit, gait abnormalities, and reduced balance 4. The XGBoost model could inspire future works on fall prevention and the fall-risk assessment potential through the gait analysis of older adults.įalls are among the most common causes of injury, severe health problems, and even death in older adults 1. Thus, we identified the optimal gait features for accurate fall risk level classification in older adults. The model accuracy in classifying fall risk levels ranged between 67–70% with 43–53% sensitivity and 77–84% specificity. At all speeds, three gait features were identified with the XGBoost (stride length, walking speed, and stance phase) that accurately classified the fall risk levels. Moreover, the definition of the fall levels was classified into high- and low-risk groups. The extreme gradient boosting (XGBoost) model was built from gait features to predict the factor affecting the risk of falls. A metric was defined to classify the fall risks, determined based on a set of questions determining the history of falls and fear of falls. Gait tests (20 m walkway) included speed modification (slower, preferred, and faster-walking) while wearing the inertial measurement unit sensors embedded in the shoe-type data loggers on both outsoles. The study included 746 older adults (age: 63–89 years). This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults.