Automated step detection with 6-minute walk test smartphone sensors signals for fall risk classification in lower limb amputees

• Published in: PLOS digital health Vol. 1; no. 8
• Main Authors: Pascale Juneau, Edward D. Lemaire, Andrej Bavec, Helena Burger, Natalie Baddour
• Format: Journal Article
• Language: English
• Published: Public Library of Science (PLoS) 01.08.2022
• ISSN: 2767-3170

Predictive models for fall risk classification are valuable for early identification and intervention. However, lower limb amputees are often neglected in fall risk research despite having increased fall risk compared to age-matched able-bodied individuals. A random forest model was previously shown to be effective for fall risk classification of lower limb amputees, however manual labelling of foot strikes was required. In this paper, fall risk classification is evaluated using the random forest model, using a recently developed automated foot strike detection approach. 80 participants (27 fallers, 53 non-fallers) with lower limb amputations completed a six-minute walk test (6MWT) with a smartphone at the posterior pelvis. Smartphone signals were collected with The Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app. Automated foot strike detection was completed using a novel Long Short-Term Memory (LSTM) approach. Step-based features were calculated using manually labelled or automated foot strikes. Manually labelled foot strikes correctly classified fall risk for 64 of 80 participants (accuracy 80%, sensitivity 55.6%, specificity 92.5%). Automated foot strikes correctly classified 58 of 80 participants (accuracy 72.5%, sensitivity 55.6%, specificity 81.1%). Both approaches had equivalent fall risk classification results, but automated foot strikes had 6 more false positives. This research demonstrates that automated foot strikes from a 6MWT can be used to calculate step-based features for fall risk classification in lower limb amputees. Automated foot strike detection and fall risk classification could be integrated into a smartphone app to provide clinical assessment immediately after a 6MWT. Author summary Lower limb amputees have a high risk of falling. Despite this, most fall risk and prevention research focuses on healthy older adults. Artificial intelligence (AI) can be used to for fall risk analysis by identifying steps during walking for step-based feature calculation. However, the variability and instability of lower limb amputees make using traditional AI step detection methods ineffective, so manual labelling of steps was required. In this research, we validate the clinical application of a new deep learning step detection approach for lower limb amputees. Steps automatically-detected by a Long-Short Term Memory model were used to calculate features for fall risk analysis and compared to the previous method of using features calculated from manually-labelled steps. We demonstrate that both approaches correctly classify the same number of fall risk participants. Additionally, the approach using automated steps correctly classified over 80% of non-fall risk participants, though this approach did misclassify more participants who were not at risk of falls than the manual approach.