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 wear-ables 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 only10% 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 ARMCortex-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 wrist-hand movement detection for standalone, battery-powered EMG wearables.