A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data

TL;DR

This paper reviews the use of Kinect-based motion analysis in healthcare, proposes a new method for representing human motion data, and demonstrates its effectiveness in detecting age-related motion changes with high accuracy.

Contribution

It introduces a novel approach to represent human motion from Kinect data and applies it to identify age-related motion impairments with high precision.

Findings

High accuracy in detecting age-related motion changes (F1-score 0.9-1.0)

Potential for early detection of motion impairments

Effective representation of arbitrary-length time-series motion data

Abstract

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.