On Gait Consistency Quantification Through ARX Residual Modeling and Kernel Two-Sample Testing.

OBJECTIVE: The quantification of the way an individual walks is key to the understanding of diseases affecting the neuromuscular system. More specifically, to improve diagnostics and treatment plans, there is a continuous interest in quantifying gait consistency, allowing clinicians to distinguish natural variability of the gait patterns from disease progression or treatment effects. To this end, the current article presents a novel objective method for assessing the consistency of an individual's gait, consisting of two major components. METHODS: Firstly, inertial sensor accelerometer data from both shanks and the lower back is used to fit an AutoRegressive with eXogenous input model. The model residuals are then used as a key feature for gait consistency monitoring. Secondly, the non-parametric maximum mean discrepancy hypothesis test is introduced to measure differences in the distributions of the residuals as a measure of gait consistency. As a paradigmatic case, gait consistency was evaluated both in a single walking test and between tests at different time points in healthy individuals and those affected by multiple sclerosis (MS). RESULTS: It was found that MS patients experienced difficulties maintaining a consistent gait, even when the retest was performed one-hour apart and all external factors were controlled. When the retest was performed one-week apart, both healthy and MS individuals displayed inconsistent gait patterns. CONCLUSION: Gait consistency has been successfully quantified for both healthy and MS individuals. SIGNIFICANCE: This newly proposed approach revealed the detrimental effects of varying assessment conditions on gait pattern consistency, indicating potential masking effects at follow-up assessments.

Machine learning at the interface of structural health monitoring and non-destructive evaluation.

While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue 'Advanced electromagnetic non-destructive evaluation and smart monitoring'.

Participatory research with native community of Kahnawake creates innovative Code of Research Ethics.

Participatory research requires ethical guidelines to incorporate the needs of the partners, i.e., the researchers and the community. This paper describes the background, development and implementation of an innovative Code of Research Ethics developed for a participatory research project with a Native community in Canada. The document ensures that responsibility and control will be shared by both researchers and community throughout the project including joint publication of the results. It defines community control of data, means of resolving dissension at time of publication, incorporation of new researchers and the differences between community-based and academic researchers.

Developing a Code of Research Ethics for research with a Native community in Canada: a report from the Kahnawake Schools Diabetes Prevention Project.

This paper describes the background and evolution of a Code of Research Ethics that was developed for a primary diabetes prevention project in the Native Mohawk community of Kahnawake in Canada. Embodying the principles of participatory research, this code was written by the researchers in conjunction with the community in the first year of the three-year project. The code ensures that the community is a full partner for the entire research process. For this project a Community Advisory Board is representative of the community.

The Kahnawake Schools Diabetes Prevention Project: intervention, evaluation, and baseline results of a diabetes primary prevention program with a native community in Canada.

OBJECTIVES: Kahnawake Schools Diabetes Prevention Project is a 3-year community-based, primary prevention program for non-insulin-dependent diabetes mellitus in a Mohawk community near Montreal, Canada. Objectives are to improve healthy eating and encourage more physical activity among elementary school children. METHODS: Intervention incorporates behavior change theory, Native learning styles, the Ottawa Charter for Health Promotion, and a health promotion planning model. Evaluation uses a mixed longitudinal and cross-sectional design to measure obesity, fitness, eating habits, and physical activity of elementary school children in the experimental and comparison communities. Intermediate variables are self-efficacy and perceived parental support. Process evaluation provides feedback to the intervention. RESULTS: During 3 years, 63 distinct interventions that included a Health Education Program reinforced by school events, a new Community Advisory Board, a recreation path, and community-based activities promoting healthy lifestyles were implemented. Baseline consent rates were 87 and 71% in the experimental and comparison schools. As expected, anthropometric data increase with age. Between 9 and 10 years there are increased weight, height, BMI, and skinfold thicknesses; decreased fitness; and increased television watching. CONCLUSIONS: Implementing a Native community-based diabetes prevention program is feasible through participatory research that incorporates Native culture and local expertise.