Abstract
The 6-min walk test (6MWT) is commonly used to assess a person’s physical mobility and aerobic capacity. However, richer knowledge can be extracted from movement assessments using artificial intelligence (AI) models, such as fall risk status. The 2-min walk test (2MWT) is an alternate assessment for people with reduced mobility who cannot complete the full 6MWT, including some people with lower limb amputations; therefore, this research investigated automated foot strike (FS) detection and fall risk classification using data from a 2MWT. A long short-term memory (LSTM) model was used for automated foot strike detection using retrospective data (n = 80) collected with the Ottawa Hospital Rehabilitation Centre (TOHRC) Walk Test app during a 6-min walk test (6MWT). To identify FS, an LSTM was trained on the entire six minutes of data, then re-trained on the first two minutes of data. The validation set for both models was ground truth FS labels from the first two minutes of data. FS identification with the 6-min model had 99.2% accuracy, 91.7% sensitivity, 99.4% specificity, and 82.7% precision. The 2-min model achieved 98.0% accuracy, 65.0% sensitivity, 99.1% specificity, and 68.6% precision. To classify fall risk, a random forest model was trained on step-based features calculated using manually labeled FS and automated FS identified from the first two minutes of data. Automated FS from the first two minutes of data correctly classified fall risk for 61 of 80 (76.3%) participants; however, <50% of participants who fell within the past six months were correctly classified. This research evaluated a novel method for automated foot strike identification in lower limb amputee populations that can be applied to both 6MWT and 2MWT data to calculate stride parameters. Features calculated using automated FS from two minutes of data could not sufficiently classify fall risk in lower limb amputees.
Highlights
Participants are allowed to walk with mobility aids, stopping is allowed if needed, and distance walked moderately correlates with more complex aerobic capacity tests, such as VO2 max, minimizing the burden for patient and clinician [8,9,10,11,12]
This means that ~52% of people who had fallen in the past six months were mistakenly classified as not being at risk of falling. This could translate to patients not being referred for further testing and a delay in the implementation of fall intervention strategies. These results indicated that step-based features from a 2-min walk test (2MWT)
This research determined that a smartphone app can provide accelerometer and gyroscope signals during a 6-min walk test (6MWT) or 2MWT for AIbased analyses to automatically determine foot strikes
Summary
The six-minute walk test (6MWT) is a sub-maximal movement assessment used to evaluate aerobic capacity and mobility [1]. The 6MWT was originally developed for those with chronic respiratory or cardiovascular disease [2] but is used to assess a number of populations, commonly older adults, people who have suffered a stroke, people with. Participants are allowed to walk with mobility aids, stopping is allowed if needed, and distance walked moderately correlates with more complex aerobic capacity tests, such as VO2 max, minimizing the burden for patient and clinician [8,9,10,11,12]. The two-minute walk test (2MWT) is a similar assessment to the 6MWT but only requires two minutes of walking. Distances walked during a 2MWT correlate well with distance walked during a 6MWT, so the 2MWT is a viable alternative [13,14,15]
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