In recent years, low-cost, low-power myoelectric control systems such as the Myo armband from Thalmic Labs have become available and unlocked tremendous possibilities for myoelectric controlled applications. However, due to the embedded system constraints, such sEMG control devices typically samples sEMG signals at a lower frequency. It is in doubt whether existing sEMG feature extraction methods are still valid on such low-resolution sEMG data. In addition, the feature extraction algorithms implemented on embedded devices must have low computational complexity in order to meet the real-time requirement. This paper aims to investigate effective features for low-resolution EMG pattern recognition. In particular, a set of novel computational efficient space-domain (SD) features (referred to as simple SD (SSD) features) have been developed to exploit the spatial relationships of sEMG signals recorded from the sensor array on the Myo armband. The proposed SSD feature set was evaluated with a linear discriminant analysis (LDA)-based classifier on a 9-gesture dataset. The experimental results indicate that using the SSD features increased the classification accuracy by 5% compared to using Hudgins' time-domain features.