DOI: 10.5281/zenodo.10000899
title: RPC-Net Dataset. Simultaneous HD-sEMG Recordings on the Forearm and angles of a 24-DOF Hand Kinematic Model

description: This data set refers to the journal paper "Developing RPC-Net: Leveraging High-Density electromyography and Machine Learning for Improved Hand Position Estimation", currently undergoing peer-review. 
We report the data acquisition process as defined in the paper. Please refer to the updated figures (fig1.png and fig2.png) and to the original paper for additional information about the acquisition process:
High-Density surface EMG data:EMG was recorded on the surface of the forearm using the MEACS system, the EMG amplifier developed at LISiN (Politecnico di Torino, Turin, Italy). The system is made up of multiple Sensor Units (SU), each measuring 34 mm x 30 mm x 15 mm and sampling 32 channels at fs=2.048 kHz (192 V/V gain, 16 bit resolution, 2.4 V dynamic range). Three SUs were used, each connected to an anisotropic electrode array (2 rows and 16 columns, with 10 mm and 15 mm inter-electrode distance respectively) for a total of N=96 acquired monopolar electromyographic channels. The electrodes were arranged in 6 rows and 16 columns around the circumference of the forearm, covering approximately a third of its length (Fig. 1). The proximal row of electrodes (row 1) was positioned at 20 \% of the distance between the medial epicondyle and the pisiform bone. The reference electrode was positioned on the lateral epicondyle. The electromyographic signal was used as input for RPC-Net during the phases of training and testing.
Hand position data:Hand position data were acquired using a motion capture system (VICON Motus; VICON Motion Systems, Centennial, Oxford, UK) sampling at 100 samples/s. The setup included 12 infrared cameras (Vero v2.2). A total of Mh=21 infrared reflective markers (diameter of 6 mm) were positioned on the dominant hand of the subject, embedded in a glove. Additionally, 12 markers were placed on the arm, chest and back, resulting in M=33 markers in total (Fig. 1). The hand position data, translated to joint angles using the Inverse Kinematic Algorithm (IKA) defined below, was used both as input and to provide the target values for the training phase of RPC-Net. 
description: The data files are uploaded in folders S00-11, each corresponding to a subject. Each folder includes .mat files, each corresponding to a trial. Each .mat file consists of a p-by-120 array, where p is the number of samples. Sampling frequency is 81.96 Hz. Channels 
EMG channels are numbered according to Fig. 1. Electrodes 1-16 are in row 1, with electrode 1 in column 1, electrode 2 in column 2, and so on. Electrodes 17-32 are in row 2, with electrode 17 in column 1 and electrode 18 in column 2 and so on.
Hand joint angles are columns 97-120 in the .mat file. Angles are in rad, defined according to the kinematic model described in the paper. The following list associates columns of the .mat file with the corresponding joint/axis of rotation, following the notation in Fig. 2. (F-E: flexion-extension, A-A: Adduction-Abduction, P-S: Pronation-Supination).

Channel 1: Wrst F-E
Channel 2: Wrst A-A
Channel 3: Wrst P-S
Channel 4: InMP F-E
Channel 5: InMP A-A
Channel 6: InIP F-E
Channel 7: InID F-E
Channel 8: MiMP F-E
Channel 9: MiMP A-A
Channel 10: MiIP F-E
Channel 11: MiID F-E
Channel 12: RiMP F-E
Channel 13: RiMP A-A
Channel 14: RiIP F-E
Channel 15: RiID F-E
Channel 16: LiMP F-E
Channel 17: LiMP A-A
Channel 18: LiIP F-E
Channel 19: LiID F-E
Channel 20: ThMP F-E
Channel 21: ThMP A-A
Channel 22: ThIP F-E
Channel 23: ThIP A-A
Channel 24: ThIP F-E
 
description: Caption of Fig. 1
(a) Position of electrodes on the forearm of the subject. Ventral (L) and dorsal (R) view (A: Medial epicondyle, B: Lateral epicondyle, C: Pisiform bone). (b) Placement of 12 infrared markers on the body of the subject. Plane W is highlighted in blue. (c) Placement of 21 infrared markers on the hand (and 2 forearm markers, also depicted in (b)). Markers are positioned proximally to each joint. Planes K and T are highlighted in orange and green respectively. Q, H and G lines shown in black.ù
Caption of Fig. 2
Kinematic model used for the Inverse Kinematic Algorithm. Each joint of the hand has 1 to 3 kinematic DoFs. Each DoF can be characterized as Flexion-Extension (F-E), Adduction-Abduction (A-A) or Pronation-Supination (P-S), as shown in legend (bottom left). Each joint is given a four letter code. The arrow represents the positioning of the axis of rotation. The circle at the origin represents a rotation axis orthogonal to the back of the hand (dorsal direction).