Surface electromyography (sEMG) measurements have demonstrated the potential to recognize complex hand motions. Additionally, sEMG enables natural recognition without disturbing movements, and thus, can be used in various fields such as teleoperation, assistant robots, and prosthetic hands. However, sEMG signals highly depend on electrode placements due to the complex muscle structures. A shift of the electrode can lead to inconsistent signal measurement. Thus, sEMG-based recognition is not practical for applications that require longterm and repeated usage. This paper proposes compensation of sEMG interface rotation for robust motion recognition. Once the relationship between sEMG signals and motions is trained, additional training for different electrode configuration is not necessary for a band-type interface. The proposed process is simple and fast. The interface rotation can be compensated for by performing only a single motion for approximately two seconds. The single motion for compensation is dependent on the muscle properties of the user. Generally, ulnar deviation may work. To demonstrate the proposed compensation, recognition of five hand motions is conducted. The experimental results indicate that the proposed compensation can cover the overall range of rotation. Additionally, the proposed compensation is validated with a transradial amputee.

Powered prosthetic legs are becoming a promising option for amputee patients. However, developing safe, robust, and intuitive control strategies for powered legs remains one of the greatest challenges. Although a variety of control strategies have been proposed, creating and fine-tuning the system parameters is time-intensive and complicated when more activities need to be restored. In this study, we developed a deep neural network (DNN) model that facilitates seamless and intuitive gait generation and transitions across five ambulation modes: level-ground walking, ascending/descending ramps, and ascending/descending stairs. The combination of latent and time sequence features generated the desired impedance parameters within the ambulation modes and allowed seamless transitions between ambulation modes. The model was applied to the open-source bionic leg and tested on unilateral transfemoral users. It achieved the overall coefficient of determination of 0.72 with the state machine-based impedance parameters in the offline testing session. In addition, users were able to perform in-laboratory ambulation modes with an overall success rate of 96% during the online testing session. The results indicate that the DNN model is a promising candidate for subject-independent and tuning-free prosthetic leg control for transfemoral amputees.

The interpretation of surface electromyographic (sEMG) signals facilitates intuitive gesture recognition. However, sEMG signals are highly dependent on measurement conditions. The relationship between sEMG signals and gestures identified from a specific subject cannot be applied to other subjects owing to anatomical differences between the subjects. Furthermore, an sEMG signal varies even according to the electrode placement on the same subject. These limitations reduce the practicability of sEMG signal applications. This paper proposes a subjectindependent gesture recognition method based on a muscle source activation model; a reference source model facilitates parameter transfer from a specific subject, i.e., donor to any subject, donee. The proposed method can compensate for the angular difference of the interface between subjects. A donee only needs to perform ulnar deviation for approximately 2s for the overall process. Ten subjects participated in the experiment, and the results show that, in the best configuration, the subject-independent classifier achieved a reasonable accuracy of 78:3% compared with the subject-specific classifier (88:7%) for four wrist/hand motions.

Powered lower-limb prosthetic devices may be becoming a promising option for amputation patients. Although various methods have been proposed to produce gait trajectories similar to those of non-disabled individuals, implementing these control methods is still challenging. It remains unclear whether these methods provide appropriate, safe, and intuitive locomotion as intended. This letter proposes the direct mapping of the voluntary movement of a residual limb (i.e., thigh) to the desired impedance parameters for amputated limbs (i.e., knee and ankle). The proposed model was learned from the gait trajectories of intact limb individuals from a publicly available biomechanics dataset, and was applied to control the prosthetic leg without post-tuning the network. Thus, the proposed method does not require training time with individuals with amputation nor configuration time for its use, and it provides a closely resembling gait trajectory of the intact limb. For preliminary testing, three able-bodied subjects participated in bypass tests. The proposed model accomplished intuitive and reliable level-ground walking at three different step lengths: self-selected, long-, and short-step lengths. The results indicate that intact benchmark data with different sensor configurations can be directly used to train the model to control prosthetic legs.

In this study, we introduce a novel design for a three-dimensional (3D) controller, which incorporates the omni-purpose stretchable strain sensor (OPSS sensor). This sensor exhibits both remarkable sensitivity, with a gauge factor of approximately 30, and an extensive working range, accommodating strain up to 150%, thereby enabling accurate 3D motion sensing. The 3D controller is structured such that its triaxial motion can be discerned independently along the X, Y, and Z axes by quantifying the deformation of the controller through multiple OPSS sensors affixed to its surface. To ensure precise and real-time 3D motion sensing, a machine learning-based data analysis technique was implemented for the effective interpretation of the multiple sensor signals. The outcomes reveal that the resistance-based sensors successfully and accurately track the 3D controller’s motion. We believe that this innovative design holds the potential to augment the performance of 3D motion sensing devices across a diverse range of applications, encompassing gaming, virtual reality, and robotics.

Powered lower limb prosthetic devices may be becoming a promising option for amputation patients. Although various methods have been proposed to produce gait trajectories similar to non-disabled individuals, implementing these control methods is still challenging. It remains unclear whether these methods provide appropriate, safe, and intuitive locomotion as users intend. This paper proposes a direct mapping of voluntary movement of a residual limb (i.e., thigh) to the desired movement of amputated limbs (i.e., knee and ankle) to control the prosthetic legs. The proposed model was learned with the gait trajectory of intact limb individuals from a publicly available biomechanics dataset and applied to control the prosthetic leg without post-tuning the network. Thus, the proposed method does not require the training time with amputation patients nor configuration time to use while providing a closely resembling gait trajectory of the intact limb. As preliminary testing, three able-bodied subjects participated in bypass tests. The proposed model accomplished intuitive and reliable level-ground walking at three different step lengths: self-selected, long- and short-steps. The results indicate that intact benchmark data can be directly used to train the model to control prosthetic legs.