Digital Analysis of Sit-to-Stand in Master Athletes, Healthy Old and Young Adults using a Depth Sensor
Good news, departing from my current research direction, the article "Digital Analysis of Sit-to-Stand in Master Athletes, Healthy Old and Young Adults using a Depth Sensor" has been accepted for publication in MDPI Healthcare. The article finally addressed a strand of my PhD which was put to one side as it was not part of the core aims. I finally had time to come back and finish it off.
The aim of the study was to compare the performance between young adults, healthy old people, and masters athletes using a depth sensor and automated digital assessment framework. Participants were asked to complete a clinically validated assessment of the sit-to-stand technique (five repetitions), which was recorded using a depth sensor. A feature encoding and evaluation framework to assess balance, core, and limb performance using time- and speed-related measurements was applied to markerless motion capture data. The associations between the measurements and participant groups were examined and used to evaluate the assessment framework suitability. The proposed framework could identify phases of sit-to-stand, stability, transition style, and performance between participant groups with a high degree of accuracy. In summary, we found that a depth sensor coupled with the proposed framework could identify performance subtleties between groups.
You can read the full article over at MDPI Healthcare here.
Length of time in review
I submitted the manuscript via MDPI Healthcare manuscript submission system on the 8 January 2018, it underwent two revisions and was accepted 28 February 2018 and published the 2 March 2018. That really is a lightening quick turnaround time! I will be publishing MDPI in future.