Fall injuries are responsible for physical dysfunction, significant disability, and loss of independence among elderly.
Poor postural control is one of the major risk factors for falling but can be trained in fall prevention programs.
These however suffer from low therapy adherence, particularly if prevention is the goal. To provide a fun and
motivating training environment for elderly, exercise games, or exergames, have been studied as balance training
tools in the past years. The present paper reviews the effects of exergame training programs on postural control of
elderly reported so far. Additionally we aim to provide an in-depth discussion of technologies and outcome
measures utilized in exergame studies. Thirteen papers were included in the analysis. Most of the reviewed studies
reported positive results with respect to improvements in balance ability after a training period, yet few reached
significant levels. Outcome measures for quantification of postural control are under continuous dispute and no
gold standard is present. Clinical measures used in the studies reviewed are well validated yet only give a global
indication of balance ability. Instrumented measures were unable to detect small changes in balance ability as they
are mainly based on calculating summary statistics, thereby ignoring the time-varying structure of the signals. Both
methods only allow for measuring balance after the exergame intervention program. Current developments in
sensor technology allow for accurate registration of movements and rapid analysis of signals. We propose to
quantify the time-varying structure of postural control during gameplay using low-cost sensor systems. Continuous
monitoring of balance ability leaves the user unaware of the measurements and allows for generating user-specific
exergame training programs and feedback, both during one game and in timeframes of weeks or months. This
approach is unique and unlocks the as of yet untapped potential of exergames as balance training tools for
community dwelling elderly.