Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment

Abstract

Transhumeral amputation has a significant effect on a person’s independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person’s proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance.

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes

“…(Ameri, Scheme, 2014b) contrasted force vs. kinematic outputs; Jiang, Muceli and colleagues (Jiang et al, 2014; Muceli, Jiang, 2014) developed models requiring minimal supervision). Recent studies have even combined EMG inputs with other modalities, such as kinematic signals (Blana et al, 2016). Most studies found that performance is inversely related to the number of DoFs/classes modeled and positively correlated to the number of EMG channels and the use of nonlinear models.…”

Sensor Fusion and Smart Sensor in Sports and Biomedical Applications

“…The development of prostheses and exoskeletons, in robotic control settings, has also used EMG signals associated with other sensors [136], such as in [137,138,139,140]. In [137], an application is presented where EMG electrodes are attached to the arm, inertial sensors are positioned in a prosthesis, and vision systems (allocated in the head) are used for the movement of a prosthesis more efficiently than only using EMG.…”

“…In [137], an application is presented where EMG electrodes are attached to the arm, inertial sensors are positioned in a prosthesis, and vision systems (allocated in the head) are used for the movement of a prosthesis more efficiently than only using EMG. In [139], a combination of inertial sensors and EMG in the arm is used in conjunction with a virtual simulation device for the restoration of the functions of the upper limb. Other systems invest in uniting EEG with EMG and inertial sensors, monitoring and suppressing involuntary shaking of the body [138].…”

Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations

“…Complex measurement and analysis of upper limb movements including kinematics and muscle activities is an exciting and growing subfield of human movement analysis [33–37] that promises better understanding of control patterns during specific movements, and as an example benefit may—on the longer term—advance control techniques currently applied to arm and hand prostheses. This process however needs tighter integration of kinematic measurement and reconstruction (from raw data to anatomical joint angles) as the time and computational overhead of the offline measurement-scaling-inverse kinematics scheme gives a bottleneck in applications where real-time analysis of the control patterns with respect to the actual kinematics would be beneficial.…”