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. 2022 Aug 18;1(8):e0000088.
doi: 10.1371/journal.pdig.0000088. eCollection 2022 Aug.

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

Affiliations

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

Pascale Juneau et al. PLOS Digit Health. .

Abstract

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Experimental set-up.
Fig 2
Fig 2. Example frame demonstrating a missed step identified within the locking period.
A missed step (vertical blue line, frame 1468) identified within adjusted search range (black dotted line). A foot strike was inserted (vertical orange line, frame 1468) at timestamp corresponding to the peak AP acceleration (green curve) in this period.

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