NAPRO setup has a higher number of input channels ($N_{F} = 10$) and results in models with a lower number of SVs ($N_{S} = 116 \div 226$). Overall the two have similar memory occupation of up to 12 kB, which can be handled by the proposed MCU equipped with 64 kB of Flash.

The run-time computational load of the application is composed by the signal acquisition, the SVM prediction and the control FSM. The SVM prediction dominates the workload and sets the requirements for real-time execution. [Figure 8](#sensors-17-00869-f008){ref-type="fig"}b shows its execution time, expressed in CPU cycle counts, as measured using the MCU's internal cycle counter (CYCCNT) register. We analyzed the two setups, having input vectors of 4 and 10 and we again measured the performance for the best and worst cases (min and max number of SVs). The computational times are again influenced by the number of input features, the number of recognized gestures and the resulting number of SVs, but this time the obtained combinations do not result in similar requirements. In fact, the proposed approach outperforms the NINAPRO setup and leads to a reduced computation time in all combinations.

[Figure 9](#sensors-17-00869-f009){ref-type="fig"} shows the quantitative differences of the computational load of the algorithm with healthy subjects, amputees and with NINAPRO data, considering the complete and reduced datasets. To compare the complete and reduced approaches, we consider the mean execution times of the 3 classes of subjects on the proposed MCU. The SVM reaches the best performance with the healthy subjects, because the activation patterns of the different gestures are well separated due to the good condition of the forearm muscles. With the NINAPRO healthy subjects the SVM reaches comparable performance, even though the execution is slightly slower.

The test on amputee patients demonstrates that the computation of the classifier's output requires a significant increase of time with the complete dataset, due to the larger SVM model. In 