Electromyographic (EMG) signals sensed on the forearm skin surface, when placed over specific muscles, enable a wide range of wrist-hand movements to be accurately detected via complex time-frequency EMG analysis. This capability, however is not available for EMG wearables, which sense EMG from random positions and experience substantially reduced detection performance as a result. In addition, the complexity of the time-frequency analysis process currently precludes real-time detection using the simple embedded processors on EMG wearables is not possible. This paper describes an approach which resolves both these shortcomings. It shows that, when random sensor placement is adopted, wrist-hand movement detection with performance equal the state-of-the-art can be achieved, with only 10% of the computational complexity. This latter property allows the first real-time wrist-hand movement detector using only simple embedded processors; specifically when using on ARM Cortex-A53 processor, execution time is lowered by 90% against the state-of-the-art, with no reduction in detection performance. It is shown how this can be further reduced by 30% by using fewer EMG channels or features, whilst maintaining good detection performance. To the best of the authors' knowledge, this is the first record of real-time high-performance wristhand movement detection for standalone, battery-powered EMG wearables.