More false positives for fall risk seen with automated detection of foot strikes compared with manual labeling in lower-limb amputees<\/p>\n<\/h3>\n
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FRIDAY, Aug. 19, 2022 (HealthDay News) — In lower-limb amputees, automated foot strikes from a six-minute walk test (6MWT) can be used to calculate step-based features for fall risk classification, according to a study published online Aug. 18 in PLOS Digital Health<\/em>.\n<\/p>\n \n\tPascale Juneau, from Ottawa Hospital Research Institute in Ontario, Canada, and colleagues evaluated fall risk classification using the random forest model with a recently developed automated foot strike detection approach. A total of 80 lower-limb amputees (27 fallers and 53 nonfallers) performed a 6MWT with a smartphone at the posterior pelvis. A novel Long Short-Term Memory approach was used to complete automated foot strike detection. Using manually labeled or automated foot strikes, step-based features were calculated.\n<\/p>\n \n\tThe researchers found that for 64 of 80 participants, manually labeled foot strikes correctly classified fall risk (accuracy, 80 percent; sensitivity, 55.6 percent; specificity, 92.5 percent). Fifty-eight of 80 participants were correctly classified by automated foot strikes (accuracy, 72.5 percent; sensitivity, 55.6 percent; specificity, 81.1 percent). Equivalent fall classification results were seen with both approaches, but six more false positives were seen with automated foot strikes.\n<\/p>\n \n\t“This study demonstrated that automatically detected foot strikes from a single smartphone sensor location on the body can be used to calculate step-based features for lower limb amputees after completing a 6MWT, leading to preliminary fall risk classification,” the authors write.\n<\/p>\n