In this paper, a novel lower extremity gait rehabilitation device with a single actuator is presented. The proposed device, Mech-Walker, utilizes the single Degree of Freedom (DOF) 8-bar Jansen's mechanism. The mechanism is synthesized to generate the ankle trajectory of human gait, relative to the hip, in terms of both position and time. Two mechanisms, one for each leg, are applied reciprocally and are mechanically synchronized to guarantee symmetric gait. A custom designed seat-type weight support system mounted over a commercially available treadmill is also introduced. It not only supports the user and the mechanisms but also links the mechanism's base to the subject's hip. This vertically movable-base allows ground contact proprioception for the user which is crucial in gait training. As this is a non-anthropomorphic design interacting with the leg only at hip and ankle via seat and footrest respectively, it does not need to be aligned with the subject's joints, which facilitates the donning and doffing of the device. Adjustability of mechanism to different users has also been considered to provide an effective training tool. A prototype is manufactured, and pilot study with a healthy subject is conducted. Due to ease of control, cost-effectiveness and high intrinsic safety, the proposed system potentially offers a novel tool for gait training.

Variations in biofeedback coding schemes for postural control, in recent research, have shown significant differences in performance outcomes due to variations in coding schemes. However, the application of vibrotactile biofeedback coding schemes to gait symmetry training is not well explored. In this paper, we devised various vibrotactile biofeedback modes and identified their efficacy during gait symmetry training of individuals suffering from hemiparesis due to stroke. These modes are composed of variations in vibration type (on-time or intensity), and relation type (proportional or inversely-proportional) with the error in symmetry ratio. Eight individuals with stroke participated in walking trials. From dependent t-tests on the collected data, we found improved achievement of temporal gait symmetry while utilizing all the provided biofeedback modes compared to no biofeedback (P<0.001). Furthermore, two-way repeated measures ANOVA revealed statistically significant difference in symmetry ratio for main effect of vibration type (P-value = 0.016, partial eta squared = 0.585). The participants performed better with modes of biofeedback with varying vibration on-times. Furthermore, participants showed better performance when the biofeedback varied proportionally with the error. These findings suggest that biofeedback coding schemes may have a significant effect on the performance of gait training.

Swarm magnetic control is a promising technology for use in nanorobotics. However, automated control schemes for small and large populations of nanoparticles are not effective due to the complexity of real blood vessels, the lack of technologies for motion control feedback of individual particles, and the inaccuracy of the mathematical models. It is quite difficult to control unexpected magnetic particle movements in a real vessel. Specifically, when magnets are used to guide the particles, the sticking of particles to vessel walls causes the dispersion of particles, which is the main reason for low steering efficiency that may cause the occurrence of side effects in non-targeted areas. In addition, when the aggregation of particles occurs inside the vessels, it can cause clogging of the vessel and the randomness of aggregation makes it difficult for the user to control the particles precisely. In the presented work, we suggest a novel intuitive shared guidance scheme for 3D steering of magnetic nanoparticles (MNPs) in a realistic vessel model. We have performed simulation based user studies with different guidance modes (visual feedback (V), shared control (S), shared control and forbidden region mode (S-F), shared control and adaptive forbidden region mode (S-AF)). The results of these studies show that the suggested S-AF mode can significantly (p-value <; 0.01) increase the delivery rate of MNPs to the target region while significantly (p-value <; 0.01) reducing the sticking rate of MNPs inside the vessel. The proposed intelligent navigation scheme for MNPs can in the future be combined with real-time magnetic particle imaging (MPI) for targeted drug delivery.

The aim of gait rehabilitation is to achieve independent ambulation. Somatosensory augmentation with external haptic sources can improve the subject’s ability to walk or stand. This paper presents the development and evaluation of a robotic system prototype that delivers haptic forces to aid overground gait rehabilitation. This portable system is based on a compact, mobile robot that is equipped with force and LIDAR sensors. The robot is flexibly linked to the user, which allows the force interaction between the user and machine to be halted when desired. During operation, the system can dynamically transition between modes in which force is applied or distance is maintained to emulate the experience of a human walking a dog on a leash. The haptic feedback from our system was evaluated in a pilot study that involved six young, healthy subjects and one individual recovering from a hemiparetic stroke. The study comprised independent and device-assisted walking trials. When using the device, the subjects walked continuously as it transitioned between distance and force modes. Gait speed and step length increased when force was applied and decreased as the force was removed. The improvements exhibited by an individual suffering from stroke were similar to those exhibited by healthy subjects. The application of haptic forces has a high potential for improving the efficiency of overground gait training with simple interactions.

Trunk rehabilitation exercises such as those for remediating core stability can help improve the seated balance of patients with weakness or loss of proprioception caused by diseases such as stroke, and aid the recovery of other functions such as gait. However, there has not yet been any reported method for automatically determining the parameters that define exercise difficulty on a trunk rehabilitation robot (TRR) based on data such as the patient’s demographic information, balancing ability, and training sequence, etc. We have proposed a machine learning (ML)-based difficulty adjustment method to determine an appropriate virtual damping gain ( D_virtual ) of the controller for the TRR’s unstable training mode. Training data for the proposed system is obtained from 37 healthy young adults, and the trained ML model thus obtained is tested through experiments with a separate population of 25 healthy young adults. The leave-one-out cross validation results (37 subjects) from the training group for validation of the designed ML model showed 80.90% average accuracy (R2 score) for using the given information to predict the desired difficulty levels, which are represented by the level of balance performance quantified as Mean Velocity Displacement (MVD) of the center of pressure. Statistical analysis (Repeated measures analysis of variance) of subject performance also showed that ground truth difficulty levels from the training data and predicted difficulty levels did not differ significantly under any of the three exercise modes used in this study (Hard, Medium, and Easy), and the standard deviations were reduced by 16.39, 41.39, and 25.68%, respectively. Moreover, the Planar Deviation (PD) of the center of pressure, which was not the target parameter here, also showed results similar to the MVD, which indicates that the predicted D_virtual affected the difficulty level of balance performance. Therefore, the proposed ML model-based difficulty adjustment method has potential for use with people who have varied balancing abilities.