The perturbation method has been used to measure stiffness of the human arm with a manipulator. Results are averages of stiffness during short perturbation intervals (<0.4 s) and also vary with muscle activation. We therefore propose a novel method for estimating static arm stiffness from muscle activation without the use of perturbation. We developed a mathematical muscle model based on anatomical and physiological data to estimate joint torque solely from EMG. This model expresses muscle tension using a quadratic function of the muscle activation and parameters representing muscle properties. The parameters are acquired from the relation between EMG and measured torque. Using this model, we were able to reconstruct joint torque from EMG signals with or without co-contraction. Joint stiffness is directly obtained by differentiation of this model analytically. We confirmed that the proposed method can be used to estimate joint torque, joint stiffness, and stiffness ellipses simultaneously for various postures with the same parameters and produces results consistent with the conventional perturbation method.

Brain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes. Although several studies have already succeeded in the inference of computer cursor trajectories and finger flexions using human ECoG signals, precise three-dimensional (3D) trajectory reconstruction for a human limb from ECoG has not yet been achieved. In this study, we predicted 3D arm trajectories in time series from ECoG signals in humans using a novel preprocessing method and a sparse linear regression. Average Pearson’s correlation coefficients and normalized root-mean-square errors between predicted and actual trajectories were 0.44∼0.73 and 0.18∼0.42, respectively, confirming the feasibility of predicting 3D arm trajectories from ECoG. We foresee this method contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion.

Simultaneous acquisition of electrooculogram, jaw electromyogram, electroencephalogram, and head movement via consumer-grade wearable devices has become possible. Such devices offer new opportunities to deploy practical biosignal-based interfaces for assistive robots; however, they also pose challenges related to the available signals and their characteristics. In this proof-of-concept study, we demonstrate the possibility of successful control of a 5 + 1 degrees-of-freedom robot arm based on a consumer wireless headband in the form of four control modes predicated on distinct signal combinations. We propose a control approach hybrid at two levels, which seeks a compromise between robot controllability and maintaining the user goal rather than being process-focused. First, robot arm steering combines discrete and proportional aspects. Second, after the robot has been steered toward the approximate target direction, a sparse approach is followed and the user only needs to issue a single command, after which steering adjustment and grasping are performed automatically under stereoscopic vision guidance. We present in detail the associated algorithms, whose implementation is publicly available. Within this framework, we also demonstrate the control of arm posture and grasping force based, respectively, on object visual features and user input. We regard the interface proposed herein as a viable blueprint for future work on controlling wheelchair-mounted and meal-assisting robot arms.