Characterizing Cycling Smoothness and Rhythm in Children With and Without Cerebral Palsy

Abstract

Stationary cycling is a practical exercise modality in children with cerebral palsy (CP) that lack the strength for upright exercises. However, there is a lack of robust, sensitive metrics that can quantitatively assess the motor control during cycling. The purpose of this brief report was to characterize the differences in motor control of cycling in children with CP and with typical development by developing novel metrics to quantify cycling smoothness and rhythm. Thirty one children with spastic diplegic CP and 10 children with typical development cycled on a stationary cycle. Cycling smoothness was measured by cross-correlating the crank angle with an ideal cycling pattern generated from participant-specific cadence and cycling duration. Cycling rhythmicity was assessed by evaluating the revolution-to-revolution variability in the time required to complete a revolution. Statistically significant differences (p < 0.001) using the Wilcoxon Rank Sum test were found between the two groups for both the metrics. Additionally, decision tree analysis revealed thresholds of smoothness <0.01 and rhythm <0.089–0.115 s for discriminating a less smooth, irregular cycling pattern characteristic of CP from typical cycling. In summary, the objective measures developed in this study indicate significantly less smoothness and rhythm of cycling in children with CP compared to children with typical development, suggestive of altered coordination and poor motor control. Such quantitative assessments of cycling motion in children with CP provide insights into neuromotor deficits that prevent them from cycling at intensities required for aerobic benefits and for participating in cycling related physical activities with their peers.

Quantification of Cycling Smoothness in Children with Cerebral Palsy

“…Five Hz frequency was chosen by Frequency power analysis of the crankIS. There is a discontinuity in the crank angle as it crosses from 360 • →0 • (Figure 2A [16]), i.e., as it transitions from the end of one revolution to beginning of the next revolution. To eliminate this discontinuity, we converted the crank angle to a linear form by concatenating the angle data in series.…”

“…To eliminate this discontinuity, we converted the crank angle to a linear form by concatenating the angle data in series. The resultant angle-in-series data (crankIS) thus consists of a time series representing the angular progression of the crank from zero to N ×360 • , where N is the number of revolutions (Figure 2B [16]). To quantify the deviation of each subject's crankIS from the theoretically smooth crank angle, the ideal crank angle (crankIDL), was defined as the straight line that connected the beginning to the end of angle-in-series data points.…”

“…Despite the different cycling systems for CP and TD groups, anthropometric differences of individuals within and across groups were minimized by standardizing positioning in both cycling setups (Figure 1C). Additionally, the force transducer pedals used for the TD group by Johnston et al, [31] also used on the recumbent tricycle used for the CP group in Sansare et al [16,30], which further standardized foot and shank positioning across the two datasets. The bicycle crank and spindle assembly for both systems were instrumented with sensors to indicate crank position and cadence.…”

“…When used as a smoothness metric, the higher the XCorr factor, the greater the similarity of the subject's crank motion to the ideal crank motion. In a study conducted by Sansare et al [16], XCorr could successfully differentiate the cycling smoothness of children with CP and typical development (TD (p < 0.0001). To further evaluate this metric, XCorr is included in our comparison to assess its performance in comparison with the described metrics.…”

“…In addition, Balasubramanian et al suggested that a reliable metric must be sensitive to changes in movement parameters within the physiological range, and it must be robust and computationally inexpensive for practical implementation [14]. Therefore, we compared the ability of the following six potential metric candidates that have the desired characteristics to quantify cycling crank angle smoothness: dimensionless jerk [13]; spectral arc length metric (SALM) [14]; roughness index (RI) [15]; cross-correlation (Xcorr) [16]; arc length (ArcL); and root mean square error (RMSE).…”