TY - JOUR
T1 - Assessing Dynamic Balance Performance during Exergaming based on Speed and Curvature of Body Movements
AU - Soancatl Aguilar, Venustiano
AU - van de Gronde, Jasper J.
AU - Lamoth, Claudine J. C.
AU - Maurits, Natasha M.
AU - Roerdink, Jos B. T. M.
PY - 2018/1
Y1 - 2018/1
N2 - Improving balance performance among the elderly is of utmost importance because of the increasing number of injuries and fatalities caused by fall incidences. Digital games controlled by body movements (exergames) have been proposed as a way to improve balance among older people. However, the assessment of balance performance in real-time during exergaming remains a challenging task. This assessment could be used to provide instantaneous feedback and automatically adjust the exergame difficulty. Such features could potentially increase the motivation of the player, thus augmenting the effectiveness of exergames. As clear differences in balance performance have been identified between older and younger people, distinguishing between older and younger adults can help identifying measures of balance performance. We used generalized linear models to investigate whether the assessment of balance performance based on movement speed can be improved by incorporating curvature of the movement trajectory into the analysis. Indeed, our results indicated that curvature improves the performance of the models. Five-fold cross validation indicated that our method is promising for the assessment of balance performance in real-time by showing more than 90% classification accuracy. Finally, this method could be valuable not only for exergaming but also for real-time assessment of body movements in sports, rehabilitation and medicine.
AB - Improving balance performance among the elderly is of utmost importance because of the increasing number of injuries and fatalities caused by fall incidences. Digital games controlled by body movements (exergames) have been proposed as a way to improve balance among older people. However, the assessment of balance performance in real-time during exergaming remains a challenging task. This assessment could be used to provide instantaneous feedback and automatically adjust the exergame difficulty. Such features could potentially increase the motivation of the player, thus augmenting the effectiveness of exergames. As clear differences in balance performance have been identified between older and younger people, distinguishing between older and younger adults can help identifying measures of balance performance. We used generalized linear models to investigate whether the assessment of balance performance based on movement speed can be improved by incorporating curvature of the movement trajectory into the analysis. Indeed, our results indicated that curvature improves the performance of the models. Five-fold cross validation indicated that our method is promising for the assessment of balance performance in real-time by showing more than 90% classification accuracy. Finally, this method could be valuable not only for exergaming but also for real-time assessment of body movements in sports, rehabilitation and medicine.
KW - LOGISTIC-REGRESSION MODELS
KW - PATTERNS
U2 - 10.1109/TNSRE.2017.2769701
DO - 10.1109/TNSRE.2017.2769701
M3 - Article
SN - 1534-4320
VL - 26
SP - 171
EP - 180
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 1
ER -