Toward a Better Robotic Hand Prosthesis Control: Using EMG and IMU Features for a Subject Independent Multi Joint Regression Model

Abstract

The interest on wearable prosthetic devices has boost the research for a robust framework to help injured subjects to regain their lost functionality. A great number of solutions exploit physiological human signals, such as Electromyography (EMG), to naturally control the prosthesis, reproducing what happens in the human limbs. In this paper, we propose for the first time a way to integrate EMG signals with Inertial Measurement Unit (IMU) information, as a way to improve subject-independent models for controlling robotic hands. EMG data are very sensitive to both physical and physiological variations, and this is particularly true between different subjects. The introduction of IMUs aims at enriching the subject-independent model, making it more robust with information not strictly dependent from the physiological characteristics of the subject. We compare three different models: the first based on EMG solely, the second merging data from EMG and the 2 best IMUs available, and the third using EMG and IMUs information corresponding to the same 3 electrodes. The three techniques are tested on two different movements executed by 35 healthy subjects, by using a leaveone-out approach. The framework is able to estimate online the bending angles of the joints involved in the motion, obtaining an accuracy up to 0.8634. The resulting joint angles are used to actuate a robotic hand in a simulated environment.

Continuous joint velocity estimation using CNN-based deep learning for multi-DoF prosthetic wrist for activities of daily living

“…A few other researchers also studied various regression models for controlling prosthetic wrist. Stival et al ( 2018 ) combined sEMG and IMU features to control prosthetic systems, and tested their model on a publicly available database as shown in Table 2 . The Pearson's correlation of our study in Table 1 was changed to correlation coefficient similar to the study in Stival et al ( 2018 ).…”

“…Stival et al ( 2018 ) combined sEMG and IMU features to control prosthetic systems, and tested their model on a publicly available database as shown in Table 2 . The Pearson's correlation of our study in Table 1 was changed to correlation coefficient similar to the study in Stival et al ( 2018 ). Our controller performed comparably to theirs on the Bulb and Hammer tasks, and significantly better on the Screw task, exceeding their sEMG and IMU data fusion methods.…”

“…A specific loss function, discrepancy loss, was also introduced for better description of the data. Stival et al ( 2018 ) combined and IMU (Inertial Measurement Unit) features for the control of prosthetic devices. However, the study by Bao et al was limited to simple wrist flexion/extension movements, while Stival et al's study was based on an online database and only presented two movements (flexion of three fingers or flexion of the wrist), which had the best performance.…”

Toward a robot swarm protecting a group of migrants

“…Inertial measurement unit (combination of accelerometers, gyroscopes, and magnetometers) is widely used in the field of robotics to help localization algorithms. The received data can be analyzed: motion measurement of robotics arm or mobile robots [12,13], tracking people or robotic systems [14,15], gait analysis [16,17], and inertial navigation or positioning for mobile robots [18,19]. Its affordability and ease of use on multiple robotic platforms make its integration in robot swarm possible [20].…”

Fast human motion prediction for human-robot collaboration with wearable interfaces

“…IMUs are probably the main alternative to cameras, they are effectively used to learn new behaviors [14] and control robots in industrial setups [15]. In many cases, a multi-modal approach can be used to enrich the information and overcome limitations of uni-modal systems [16]. Many solutions propose the introduction of physiological signals, recorded directly from the human body.…”