Shared human-robot control for assistive machines can improve the independence of individuals with motor impairments. Monitoring elevated levels of workload can enable the assistive autonomy to adjust the control-sharing in an assist-as-needed way, to achieve a balance between user fatigue, stress and independent control. In this work, we aim to investigate how heart-rate variability features can be utilized to monitor elevated levels of mental workload while operating a powered wheelchair, and how that utilization might vary under different control interfaces. To that end, we conducted a 22 person study with three commercial interfaces. Our results show that the validity and reliability of using the ultra-short-term heart-rate variability features as predictors for workload indeed are affected by the type of interface in use.