A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data
This addresses the need for accessible motion analysis tools in healthcare settings like GP surgeries and homes, though it is incremental as it builds on existing Kinect technology.
The paper tackled the problem of underused clinical motion analysis by proposing a method to represent human motions from time-series data as a single vector, achieving high certainty (F1-score 0.9-1.0) in detecting age-related changes in motions from 54 individuals.
The analysis of human motion as a clinical tool can bring many benefits such as the early detection of disease and the monitoring of recovery, so in turn helping people to lead independent lives. However, it is currently under used. Developments in depth cameras, such as Kinect, have opened up the use of motion analysis in settings such as GP surgeries, care homes and private homes. To provide an insight into the use of Kinect in the healthcare domain, we present a review of the current state of the art. We then propose a method that can represent human motions from time-series data of arbitrary length, as a single vector. Finally, we demonstrate the utility of this method by extracting a set of clinically significant features and using them to detect the age related changes in the motions of a set of 54 individuals, with a high degree of certainty (F1- score between 0.9 - 1.0). Indicating its potential application in the detection of a range of age-related motion impairments.