Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging evidence-based design of new studies and/or real-life applications. We conducted a systematic review and meta-analysis to appraise the best available evidence in the field. Namely, we applied established statistical methods for the analysis of categorical data to identify optimal combinations of sensor locations, tasks and feature categories. We also conducted a metaanalysis on sensor-based features to identify a set of significant features and their pivot values. The results demonstrated that with a walking test, the most effective feature to assess the risk of falling was the velocity with the sensor placed on the shins. Conversely, during quite standing, linear acceleration measured at the lower back was the most effective combination of feature-placement. Similarly, during the sit-to-stand and/or the stand-to-sit tests, linear acceleration measured at the lower back seems to be the most effective feature-placement combination. The meta-analysis demonstrated that four features resulted significantly higher in fallers: the root-mean-square acceleration in the mediolateral direction during quiet standing with eyes closed (Mean Difference (MD): 0.01 g; 95% Confidence Interval (CI95%): 0.006 to 0.014); the number of steps (MD: 1.638 steps; CI95%: 0.384 to 2.892) and total time (MD: 2.274 seconds; CI95%: 0.531 to 4.017) to complete the Timed Up and Go test; and the step time (MD: 0.053; CI95%: 0.012 to 0.095; p=0.01) during walking.
Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis https://www.embs.org/tnsre/wp-content/uploads/sites/15/2018/02/Fig1_HR-1.jpg 780 991 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE) //www.embs.org/tnsre/wp-content/uploads/sites/15/2022/06/ieee-tnsre-logo2x.png