As the population ages, increasingly more individuals experience ankle disabilities caused by stroke and cerebral palsy. Studies on parallel robots for ankle rehabilitation have been conducted under this circumstance. This paper presents a novel parallel ankle rehabilitation robot with the key features of a simple configuration and actuator nonredundancy. The mechanical design is determined, and a prototype is built. Additionally, inverse position solution is addressed to calculate the workspace of the parallel robot. Jacobian matrices mapping the velocity and force from the active joint space to the task space are derived, and kinetostatic performance indices, namely, motion isotropy, force transfer ratio, and force isotropic radius are defined. Moreover, the inverse dynamic model is presented using the Newton–Euler formulation. Dynamic evaluation index, i.e., dynamic uniformity, is proposed according to the derived Jacobian matrix and inertia matrix. Based on the workspace analysis, the parallel robot demonstrates a sufficient workspace for ankle rehabilitation compared with measured range of motion of human ankle joint complex. The results of the kinetostatic and dynamic performance analysis indicate that the parallel robot possesses good motion isotropy, high force transfer ratio, large force isotropic radius, and relatively uniform dynamic dexterity within most of the workspace, especially in the central part. A numerical example is presented to simulate the rehabilitation process and verify the correctness of the inverse dynamic model. The simplicity and the performance of the proposed robot indicate that it has the potential to be widely used for ankle rehabilitation.

Numerous robots have been widely used to deliver rehabilitative training for hemiplegic patients to improve their functional ability. Because of the complexity and diversity of upper limb motion, customization of training patterns is one key factor during upper limb rehabilitation training. Most of the current rehabilitation robots cannot intelligently provide adaptive training parameters, and they have not been widely used in clinical rehabilitation. This article proposes a new end-effector upper limb rehabilitation robot, which is a two-link robotic arm with two active degrees of freedom. This work investigated the kinematics and dynamics of the robot system, the control system, and the realization of different rehabilitation therapies. We also explored the influence of constraint in rehabilitation therapies on interaction force and muscle activation. The deviation of the trajectory of the end effector and the required trajectory was less than 1 mm during the tasks, which demonstrated the movement accuracy of the robot. Besides, results also demonstrated the constraint exerted by the robot provided benefits for hemiplegic patients by changing muscle activation in the way similar to the movement pattern of the healthy subjects, which indicated that the robot can improve the patient’s functional ability by training the normal movement pattern.

The main purpose of this study was to investigate the compensatory response of the muscle activities of seventeen major muscle groups in the spinal region, intradiscal forces of the five lumbar motion segment units (MSUs), and facet forces acting on the ten lumbar facet joints in patients with lumbar disc herniation (LDH). Twenty-six healthy adults and seven LDH patients performed trunk flexion, ipsilateral picking up, and contralateral picking up in sequence. Eight optical markers were placed on the landmarks of the pelvis and spinal process. The coordinates of these markers were captured to drive a musculoskeletal model to calculate the muscle activities, intradiscal forces, and facet forces. The muscle activities of the majority of the seventeen major muscle groups were found increases in LDH patients. In addition, the LDH patients displayed larger compressive forces and anteroposterior forces on all the five lumbar MSUs and more lumbar facet inventions on most facet joints. These findings suggest that the LDH patients demonstrate compensatory increases in the most trunk muscle activities and all spinal loads. These negative compensatory responses increase the risk of the aggravation of disc herniation. Therefore, treatment should intervene as earlier as possible for the severe LDH patients.

Muscle synergy has been proven to be simple and intuitive EMG features for myoelectric interface to simplify human machine interaction in upper limb. However, suffered from poor discriminability result from similar feature between locomotion modes, spatial muscle synergy hasn’t been widely applied in myoelectric interface for lower limb assistance. Recent studies suggested to provide synergy-based neural controller with extra time-variant information to improve their dynamic performance, but it remains to be clarified that synergetic pattern in either invariant spatial or variant time domain, is superior for locomotion mode recognition. Instead of traditional locomotion mode recognition with spatial muscle synergy, we extracted the synergetic pattern in both spatial and temporal domains via non-negative matrix factorization (NMF) algorithm, and their performance together with similarities between locomotion modes, that is evaluated by recognizing five locomotion modes using four common machine learning algorithms. The results showed that the performance of time-variant muscle synergy pattern on machine learning classifiers was significantly better than that of spatial time-invariant features. All the used classification models based on time-variant features achieved the better results, with a classification accuracy of 93% for machine learning method and 97% for deep learning method, which supported the view of taking time-variant muscle synergy pattern into consideration for lower limb myoelectric interface. The proposed method improves the classifier performance of commonly used synergy-based locomotion modes recognition, and provides a new research idea that encourages future HMI systems or instruments to develop more dynamic-sensitive measurement setup for synergy-based myoelectric interface.

Background. Low back pain (LBP) continues to be a severe global healthy problem, and a lot of patients would undergo conservative or surgical treatments. However, the improving capacity of spinal load sharing during activities of daily living (ADLs) after interventions is largely unknown. The objective of this study was to quantitatively predict the improvement of spinal musculoskeletal loadings during level walking and stair climbing after two simulated interventions. Material and Methods. Twenty-six healthy adults and seven lumbar disc herniation patients performed level walking and stair climbing in sequence. The spinal movement was recorded using a motion capture system. The experimental data were applied to drive a musculoskeletal model to calculate all the lumbar joint resultant forces and muscle activities of seventeen main trunk muscle groups. Rehabilitation and reconstruction were selected as the representative of conservative and surgical treatment, respectively. The spinal load sharing after rehabilitation and reconstruction was predicted by replacing the patients’ spine rhythm with healthy subjects’ spine rhythm and altering the center of rotation at the L5S1 level, respectively. Results. During both level walking and stair climbing, the joint resultant forces of the lower lumbar intervertebral discs were predicted to reduce after the two simulated inventions. In addition, the maximum muscle activities of the most trunk muscle groups decreased after simulated rehabilitation and conversely increased after simulated reconstruction. Conclusion. The predictions revealed the different compensatory responses on the spinal load sharing after two simulated interventions, severing as guidance for making preoperative planning and rehabilitation planning.