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

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.

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

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.

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

Foot strike detection is important when evaluating a person's gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.

What are potential barriers and enablers to patient and physician participation in Canadian cell therapy trials for stroke? A stakeholder interview study.

OBJECTIVES: Early phase cell therapy trials face many barriers to successful, timely completion. To optimise the conduct of a planned clinical trial of mesenchymal stem cell (MSC) therapy for chronic stroke, we sought patient and physician views on possible barriers and enablers that may influence their participation. DESIGN: Semistructured interview study. SETTING: Patients were recruited from three rehabilitation centres in Ontario, Canada; physicians were recruited from across Canada through snowball sampling. PARTICIPANTS: Thirteen chronic stroke patients (patients who had experienced a stroke at least 3 months prior; 10 male, 3 female) and 15 physicians (stroke physiatrists; 9 male, 6 female) participated in our interview study. Data adequacy was reached after 13 patient interviews and 13 physician interviews. METHODS: Interview guides and directed content analysis were based on the Theoretical Domains Framework (TDF). Interviews were coded, and relevant themes were identified. RESULTS: Most patients were optimistic about participating in an MSC therapy clinical trial, and many expressed interest in participating, even if it was a randomised controlled trial with the possibility of being allocated to a placebo group. However, the method of administration of cells (intravascular preferred to intracerebral) and goal of the trial (efficacy preferred to safety) may influence their intention to participate. All physicians expressed interest in screening for the trial, though many stated they were less motivated to contribute to a safety trial. Physicians also identified several time-related barriers and the need for resources to ensure feasibility. CONCLUSIONS: This novel application of the TDF helped identify key potential barriers and enablers prior to conducting a clinical trial of MSC therapy for stroke. This will be used to refine the design and conduct of our trial. A similar approach may be adopted by other investigators considering early phase cell therapy trials.

From the Lab to Patients: a Systematic Review and Meta-Analysis of Mesenchymal Stem Cell Therapy for Stroke.

There may be the potential to improve stroke recovery with mesenchymal stem cells (MSCs); however, questions about the efficacy and safety of this treatment remain. To address these issues and inform future studies, we performed a preclinical and clinical systematic review of MSC therapy for subacute and chronic ischemic stroke. MEDLINE, Embase, the Cochrane Register of Controlled Trials, and PubMed were searched. For the clinical review, interventional and observational studies of MSC therapy in ischemic stroke patients were included. For the preclinical review, interventional studies of MSC therapy using in vivo animal models of subacute or chronic stroke were included. Measures of safety and efficacy were assessed. Eleven clinical and 76 preclinical studies were included. Preclinically, MSC therapy was associated with significant benefits for multiple measures of motor and neurological function. Clinically, MSC therapy appeared to be safe, with no increase in adverse events reported (with the exception of self-limited fever immediately following injection). However, the efficacy of treatment was less apparent, with significant heterogeneity in both study design and effect size being observed. Additionally, in the only randomized phase II study to date, efficacy of MSC therapy was not observed. Preclinically, MSC therapy demonstrated considerable efficacy. Although MSC therapy demonstrated safety in the clinical setting, efficacy has yet to be determined. Future studies will need to address the discordance in the continuity of evidence as MSC therapy has been translated from "bench-to-bedside".