Automatic Error Analysis of Human Motor Performance for Interactive
  Coaching in Virtual Reality
  In the context of fitness coaching or for rehabilitation purposes, the motor
actions of a human participant must be observed and analyzed for errors in
order to provide effective feedback. This task is normally carried out by human
coaches, and it needs to be solved automatically in technical applications that
are to provide automatic coaching (e.g. training environments in VR). However,
most coaching systems only provide coarse information on movement quality, such
as a scalar value per body part that describes the overall deviation from the
correct movement. Further, they are often limited to static body postures or
rather simple movements of single body parts. While there are many approaches
to distinguish between different types of movements (e.g., between walking and
jumping), the detection of more subtle errors in a motor performance is less
investigated. We propose a novel approach to classify errors in sports or
rehabilitation exercises such that feedback can be delivered in a rapid and
detailed manner: Homogeneous sub-sequences of exercises are first temporally
aligned via Dynamic Time Warping. Next, we extract a feature vector from the
aligned sequences, which serves as a basis for feature selection using Random
Forests. The selected features are used as input for Support Vector Machines,
which finally classify the movement errors. We compare our algorithm to a well
established state-of-the-art approach in time series classification, 1-Nearest
Neighbor combined with Dynamic Time Warping, and show our algorithm's
superiority regarding classification quality as well as computational cost.
