Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations

Juneau, Pascale; Baddour, Natalie; Burger, Helena; Bavec, Andrej; Lemaire, Edward D.

doi:10.3390/s21216974

Cited by 7 publications

(16 citation statements)

References 25 publications

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“…To determine if the automated FS detection model from [19] can predict FS in 2MWT data, a simulated 2MWT dataset was created. The first two minutes of the 6MWT trial was determined to replicate most closely that of a 2MWT, so the first two minutes of each participant's trial was exported for a total of 6000 data points per participant.…”

Section: Filtering and Signal Processingmentioning

confidence: 99%

“…An LSTM deep learning approach was developed and evaluated for automated FS detection in [19]. The model was written and evaluated in Python 4.2.…”

Section: Foot Strike Classification Modelsmentioning

confidence: 99%

“…The LSTM layer was imported from Keras [21] and several hyperparameter combinations were evaluated. The LSTM from [19] with the best performance was subsequently used for this analysis. The LSTM had 100 hidden nodes in the LSTM layer, the dense layer had 50 hidden nodes, a batch size of 64 and a dropout value of 0.4 were used.…”

Section: Foot Strike Classification Modelsmentioning

confidence: 99%

“…Juneau et al [19] developed a novel long-short term memory (LSTM) deep learning approach for automated FS detection in lower limb amputees. The LSTM was trained on filtered smartphone signals collected from the TOHRC Walk Test app during a 6MWT.…”

Section: Introductionmentioning

confidence: 99%

“…However, the deep learning automated FS approach has not yet been validated with 2MWT data. The LSTM FS identification model described in [19] was originally trained on data from a 6MWT; however, it is possible that a new algorithm specific to 2MWT data is required. Therefore, this study evaluated LTSM FS identification accuracy on 2MWT data after the model was trained on signals from a 6MWT or signals from a 2MWT, stride parameters were compared between automated FS and manually labeled FS, and fall risk classification was assessed using step-based features from automated and manually labeled FS.…”

Section: Introductionmentioning

confidence: 99%

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Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests

Juneau

Baddour

Burger

et al. 2022

Sensors

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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.

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Section: Filtering and Signal Processingmentioning

confidence: 99%

“…An LSTM deep learning approach was developed and evaluated for automated FS detection in [19]. The model was written and evaluated in Python 4.2.…”

Section: Foot Strike Classification Modelsmentioning

confidence: 99%

Section: Foot Strike Classification Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests

Juneau

Baddour

Burger

et al. 2022

Sensors

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

et al. 2022

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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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Balance confidence classification in people with a lower limb amputation using six minute walk test smartphone sensor signals

Juneau,

Baddour,

Burger

et al. 2024

PLOS Digit Health

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The activities-specific balance confidence scale (ABC) assesses balance confidence during common activities. While low balance confidence can result in activity avoidance, excess confidence can increase fall risk. People with lower limb amputations can present with inconsistent gait, adversely affecting their balance confidence. Previous research demonstrated that clinical outcomes in this population (e.g., stride parameters, fall risk) can be determined from smartphone signals collected during walk tests, but this has not been evaluated for balance confidence. Fifty-eight (58) individuals with lower limb amputation completed a six-minute walk test (6MWT) while a smartphone at the posterior pelvis was used for signal collection. Participant ABC scores were categorized as low confidence or high confidence. A random forest classified ABC groups using features from each step, calculated from smartphone signals. The random forest correctly classified the confidence level of 47 of 58 participants (accuracy 81.0%, sensitivity 63.2%, specificity 89.7%). This research demonstrated that smartphone signal data can classify people with lower limb amputations into balance confidence groups after completing a 6MWT. Integration of this model into the TOHRC Walk Test app would provide balance confidence classification, in addition to previously demonstrated clinical outcomes, after completing a single assessment and could inform individualized rehabilitation programs to improve confidence and prevent activity avoidance.

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Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations

Cited by 7 publications

References 25 publications

Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests

Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests

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

Balance confidence classification in people with a lower limb amputation using six minute walk test smartphone sensor signals

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