Due to the movement expressiveness and privacy assurance of human skeleton data, 3D skeleton-based action inference is becoming popular in healthcare applications. These scenarios call for more advanced performance in application-specific algorithms and efficient hardware support. Warnings on health emergencies sensitive to response speed require low latency output and action early detection capabilities. Medical monitoring that works in an always-on edge platform needs the system processor to have extreme energy efficiency. Therefore, in this paper, we propose the MC-LSTM, a functional and versatile 3D skeleton-based action detection system, for the above demands. Our system achieves state-of-the-art accuracy on trimmed and untrimmed cases of general-purpose and medical-specific datasets with early-detection features. Further, the MC-LSTM accelerator supports parallel inference on up to 64 input channels. The implementation on Xilinx ZCU104 reaches a throughput of 18 658 Frames-Per-Second (FPS) and an inference latency of 3.5 ms with the batch size of 64. Accordingly, the power consumption is 3.6 W for the whole FPGA+ARM system, which is 37.8x and 10.4x more energy-efficient than the high-end Titan X GPU and i7-9700 CPU, respectively. Meanwhile, our accelerator also keeps a 4  ∼ 5x energy efficiency advantage against the low-power high-performance Firefly-RK3399 board carrying an ARM Cortex-A72+A53 CPU. We further synthesize an 8-bit quantized version on the same hardware, providing a 48.8% increase in energy efficiency under the same throughput.