Electrical muscle activity is typically measured via bipolar surface electromyography (EMG), which has poor spatial resolution for measuring intramuscular variations in myoelectric activity. High-density EMG uses a grid of microelectrodes to measure the temporal and spatial properties of a muscle, but it has primarily been used in stationary conditions. The goals of this dissertation were to validate high-density EMG hardware and signal processing methods for use during human walking and running and to use high-density EMG to measure lower limb spatial muscle activation patterns in healthy individuals during locomotion. In my first study, I designed an electrical leg phantom that broadcast simulated EMG signals to quantify the effects of crosstalk and motion artifacts. I found that motion artifacts do not affect all areas of the high-density EMG array uniformly, and traditional filtering measures may not fully remove these artifacts. To address this issue, my second study compared the effectiveness of standard EMG signal processing and alternative signal processing methods at removing motion artifacts from EMG data during locomotion. Canonical correlation analysis decomposition and filtering provided a greater reduction in signal content in the frequency bands associated with motion artifacts than standard EMG processing measures. In my third study, I compared the spatial EMG activity patterns in five lower limb muscles across a range of walking and running speeds. I found heterogeneous spatial EMG activation patterns, evidenced by contrasting spatial entropy and EMG center of gravity locations among the muscles. In my fourth study, I analyzed how localized muscle fatigue affected spatial EMG activation in the medial gastrocnemius during walking and running. Peak EMG activity during locomotion significantly decreased post-fatigue, and the centroid of EMG activity shifted from its pre-fatigue location. Together, these studies establish high-density EMG as an effective tool for studying muscle activity in dynamic environments and provide methods to measure and analyze spatial variations in healthy and clinical populations.