Motion Capture Quantification of User Variation in Topical Microparticle Application.

Motion capture has the potential to shed light on topical drug delivery application. This approach holds promise both as a training tool, and for the development of skin technology, but first, this approach requires validation. Elongated microparticles (EMP) are a physical delivery enhancement technology that relies on a user working in the microparticles using a textured applicator. We used this approach to test the hypothesis that motion capture data can be used to characterize the topical application process. Motion capture was used to record participants while applying a mixture of EMP and sodium fluorescein to ex-vivo porcine skin samples. Treated skin was assessed using reflectance confocal and fluorescence microscopy. Image analysis was used to quantify the microparticle density and the presence of a fluorescent drug surrogate, sodium fluorescein. A strong correlation was present between applicator motion and microparticle and drug delivery profiles. There were quantitative and qualitative differences in the intra- and inter- user application methods that went beyond the level of training. Frequency and velocity of the applicator motion were key factors that correlated with EMP density. Our quantitative analysis of an experimental dermatological device supports the hypothesis that self-application may benefit from some form of digital monitoring or training with feedback. Our conclusion is that the integration of motion capture into experimental dermatological research offers an improved and quantifiable perspective that could be broadly useful with respect to topical applications, and with respect to the instruction provided to patients and clinicians.

Considerations of handheld respiratory rate estimation via a stabilized Video Magnification approach.

Respiratory rate can be a vital indicator of illness; however, tracking this is a non-trivial process. Phase-based Eulerian Video Magnification (EVM) is an exciting spatiotemporal video processing approach able to reveal subtle breathing motions within video sequences; however, its results are variant to large motions and camera blur. In the case of camera motion, a compensation strategy of stabilizing (without smoothing) the video has the may reduce estimation error in handheld cases. This work explores the extent of removing motion artefacts and its impact on identifying subtle breathing motions. Tests across six indoor scenes show a reduction mean breathing estimate error for 4 of 6 cases and highlights the sensitivity of this approach to unwanted body movements. The results of this project suggest the plausibility that non-smoothing video amplification processes can be an effective method to track breathing motion and that implementing correction techniques which may allow a smartphone to provide a compact, non-invasive, online breathing monitor.