Open Database for Accurate Upper-Limb Intent Detection Using Electromyography and Reliable Extreme Learning Machines

Abstract

Surface Electromyography (sEMG) signal processing has a disruptive technology potential to enable a natural human interface with artificial limbs and assistive devices. However, this biosignal real-time control interface still presents several restrictions such as control limitations due to a lack of reliable signal prediction and standards for signal processing among research groups. Our paper aims to present and validate our sEMG database through the signal classification performed by the reliable forms of our Extreme Learning Machines (ELM) classifiers, used to maintain a more consistent signal classification. To perform the signal processing, we explore the use of a stochastic filter based on the Antonyan Vardan Transform (AVT) in combination with two variations of our Reliable classifiers (denoted R-ELM and R-Regularized ELM (RELM), respectively), to derive a reliability metric from the system, which autonomously selects the most reliable samples for the signal classification. To validate and compare our database and classifiers with related papers, we performed the classification of the whole of Databases 1, 2, and 6 (DB1, DB2, and DB6) of the NINAProdatabase. Our database presented consistent results, while the reliable forms of ELM classifiers matched or outperformed related papers, reaching average accuracies higher than 99 % for the IEEdatabase, while average accuracies of 75 . 1 % , 79 . 77 % , and 69 . 83 % were achieved for NINAPro DB1, DB2, and DB6, respectively.

Robust Real-Time Embedded EMG Recognition Framework Using Temporal Convolutional Networks on a Multicore IoT Processor

“…On these data, Palermo et al [11] reached an inter-session accuracy of 25.4% by feeding Wave Length to a Random Forest. Cene et al [16] successfully employed Extreme Learning Machines (ELMs) to raise this intersession accuracy to 41.8%. It is worth to notice that the reason why the accuracy reached on the NinaPro DB6 is much lower than the one reached on other datasets with a similar number of classes and sensors, is that the hand movements of NinaPro DB6 are all grasps, thus much less diverse and discernable than the gestures in ordinary datasets.…”

“…On the Unibo-INAIL dataset, Milosevic et al [10] showed that multi-posture and multi-day training improve intersession generalization. A Radial Basis Function kernel SVM (RBF-SVM) applied on 4-channel single samples of the RMS signal yielded an intra-session recognition accuracy higher than 90%, with an inter-session accuracy drop up to 20% (a value similar to [11], [16]). The aforementioned approaches showed the major limitation of classical ML: it strongly relies on domain-specific knowledge and hand-crafted features, limiting the capability to generalize over time.…”

“…To evaluate the inference performance, we run TEMPONet on the Ninapro DB6 comparing the results of the multisession testing described in [16], that represents, at the best of our knowledge, the previous state-of-the-art inter-session accuracy for the NinaPro DB6. We obtained an average 49.6% accuracy against 41.8% reported in [16]. In this test, the accuracy is evaluated on the entire gestures (i.e.…”

“…The system is based on the combination of a commercial Analog Front End for biopotential acquisition with GAP8, a multicore low-power IoT processor; • a 20 session dataset, collected with our custom platform, which allows us to validate the algorithm and to profile a quantized version of the TCN, suitable for the deployment on a resource-constrained platform. We tested the performance of TEMPONet on the NinaPro DB6 dataset [11], achieving 65.2% inter-session accuracy on steady signals and 49.6% inter-session on the full dataset -7.8% better than the current state-of-the-art [16]. Moreover, after the system design, we tested the same TEMPONet topology on a new dataset we introduce in this work, comprising 20 sessions on three subjects.…”

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

“…ELM is a feed forward neural network raised by professor Guang-Bin Huang [45], with a single input layer, a hidden layer, and an output layer. In most cases, the input weights and hidden nodes are randomly assigned, and the output weights are directly calculated by the least square method in just a single step.…”