Context- and Terrain-aware Gait Analysis

Walking is a crucial ability to allow people to live normal, healthy lives. However, various conditions which affect the brain or the body such as Parkinson's disease, dementia, stroke, multiple sclerosis, and amputations threaten a person's ability to walk and can lead to falls or the fear of falling. Either of these fall-related burdens can severely affect the quality of life for a person, particularly those who are most vulnerable, such as older people. To detect fall-related issues in a person's manner of walking (their gait), a process called gait-analysis can be performed which involves a team of specialists using video cameras to record someone walking in a laboratory environment and analyse the video to identify problems. However, current technology is rapidly advancing towards the capacity for remote gait analysis, which uses wearable sensor technologies to capture one's gait. This provides many benefits such as a more natural walking style, automatic data analysis, and reduced time needed by specialists to perform the analysis. The largest of these benefits, however, is the capacity to wear the device outside of the laboratory to see how a person walks on real terrains. Many current studies have shown great strides in producing highly accurate gait analysis systems. However, a real-environment dataset for these systems to be tested on does not yet exist. Furthermore, datasets including a range of people with conditions that increase their risk of falling are scarce and typically only focus on one group. This study aims to produce and analyse the first real-world gait analysis dataset which includes a wide range of gait-affecting conditions, and to highlight what worked and what didn't for future researchers to build off when designing and implementing practical solutions to real-environment gait analysis.