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In this paper, the mechanical damage behavior is investigated based on the characteristic roughness on the surface and the orientation of superficial structures. The main goal is to explore the surface roughness on mechanically loaded copper conductors as a lifetime indicator. For this purpose, copper conductors are mechanically stressed in accordance with EN 50,396 and then examined metallographically and microscopically. The microstructure examination shows that the roughness is caused by material extrusion and cracks due to work hardening in the surface area. Using confocal microscopy, it is shown for the first time that significant formation of surface roughness takes place over the service life of copper conductors. The roughness increases monotonically, but not linearly with number of cycles, due to internal microstructural processes and can be divided into three sections. First inspections of the conductor surface over lifetime show a correlation between the intensity of structures orientated 45° to the loading direction and the roughness. This phenomenon, already known from microscopic slip lines, is thus also evident in macroscopic roughness formation and is well founded by the research theory on material extrusion along dislocation lines. In summary, a lifetime determination is possible based on its developing roughness which enables the utilization as a sensor element.
Gait analysis is a systematic study of human movement. Combining wearable foot pressure sensors and machine learning (ML) solutions for a high-fidelity body pose tracking from RGB video frames could reveal more insights into gait abnormalities. However, accurate detection of heel strike (HS) and toe-off (TO) events is crucial to compute interpretable gait parameters. In this work, we present an experimental platform to study the timing of gait events using a new wearable foot pressure sensor (ActiSense System, IEE S.A., Luxembourg), and Google’s open-source ML solution MediaPipe Pose. For this purpose, two StereoPi systems were built to capture stereoscopic videos and images in real time. MediaPipe Pose was applied to the synchronized StereoPi cameras, and two algorithms (ALs) were developed to detect HS and TO events for gait and analysis. Preliminary results from a healthy subject walking on a treadmill show a mean relative deviation across all time spans of less than 4% for the ActiSense device and less than 16% for AL2 (33% for AL1) employing MediaPipe Pose on StereoPi videos. Finally, this work offers a platform for the development of sensor- and video-based ALs to automatically identify the timing of gait events in healthy individuals and those with gait disorders.