Evaluation of human gait biomechanics in real-world environments

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Walking is a fundamental feature of human movement which allows us to freely explore our environment. Nonetheless, locomotion is an inherently unstable activity in which balance is needed to remain steady and avoid falls. This stability challenge is amplified in real-world environments where irregular, bumpy surfaces may be encountered. Studying gait under these challenging situations is crucial to help researchers develop a better understanding of how different people control movement. Unfortunately, gait research is almost exclusively conducted indoors under well-controlled laboratory conditions using ideal, even surfaces. As a result, it is unclear if these findings can be applied to the real world. Therefore, the aim of my NSERC research is to determine how humans adapt their gait patterns over irregular surfaces that can be found in the real world. It is hypothesized that (1) adaptations will be related to the level of challenge imposed, i.e., a more challenging surface will destabilize people more and lead to larger movement changes to maintain balance and (2) people of different ages will react differently to irregular surfaces based on their balance ability. Healthy children, adolescents, young adults, and older adults will be recruited for this project. In a first study (year 1), an open-source wearable system to measure gait in the real-world (GaitLab2Go) will be developed. In study 2 (years 2-3), participants will be asked to walk over a series of irregular surfaces such as stones, small pebbles, grass, sand, and woodchips. The study will make use of GaitLab2Go and gold-standard motion capture equipment such as cameras that can record three-dimensional movement from small markers fixed on the participant's body and sensors that record muscle activity. From these data, we will be able to determine the movement and muscular adaptations necessary to maintain stability on the irregular surfaces. A final study (years 3-5) is planned in which the movement patterns of participants during their normal daily activities will be recorded with GaitLab2Go over a 2-day period. Afterwards, machine learning algorithms will be used to identify how participants adapt to different irregular surfaces in their own environment and in a large public dataset. Findings from this project will contribute to our understanding of how humans successfully move, navigate, maintain balance, and avoid falls in outdoor environments, an important concern given our aging Canadian population. GaitLab2Go could also help clinicians assess gait of their patients. Moreover, as I plan to release GaitLab2Go in open-source (free) format, the results of this research program can lead to significant cost reductions for laboratories and allow others to review and improve our work. Finally, follow-up studies could positively impact civil engineering and public health via evaluation of pedestrian walkways and outdoor public-space design.