For stroke survivors, diminished hand functions limit their ability to perform activities of daily living (ADLs). Recently, soft-robotic gloves have assisted stroke survivors in active rehabilitation by facilitating their finger movements based on intentions expressed through biosignals, such as electromyogram and electroencephalogram. In this regard, helping stroke survivors actively train multiple hand postures can improve hand functions required for ADLs. However, detecting intentions regarding multiple hand postures remains challenging, often resulting in low online classification performance. To address this, we propose a hand rehabilitation system comprising a vision-based intention detection framework and 8-degree-of-freedom soft-robotic glove. Our proposed framework, depth enhanced hand posture intention network, analyzes images and depths data observing users' arm behavior and hand-object interactions to predict intentions for multiple hand postures. The 8-degrees-of-freedom soft-robotic glove facilitates flexion and extension of individual fingers to help users perform desired hand postures. To support active rehabilitation, we operate our glove to facilitate user's finger movements when the user exerts effort to generate desired hand postures. We test our system on a real-time pick and place task involving five hand postures most commonly utilized in ADLs. Our vision-based system could predict and facilitate the desired hand postures for five healthy individuals and three stroke survivors with average accuracy of 90.4 ± 3.6% and 80.3 ± 4.6%, respectively, outperforming methods reported in previous studies.

OBJECTIVE: The changes in neural drive to muscles associated with modulation of inter-muscular coordination in the upper extremity have not yet been investigated. Such information could help elucidate the neural mechanisms behind motor skill learning. METHODS: Six young, neurologically healthy participants underwent a six-week training protocol to decouple two synergist elbow flexor muscles as a newly learned motor skill in the isometric force generation in upward and medial directions. Concurrent electroencephalography and surface electromyography from twelve upper extremity muscles were recorded in two conditions (As-Trained & Habitual) across two assessments (Week 0 vs. Week 6). Changes to inter-muscular connectivity (IMC), functional muscle networks, cortico-muscular connectivity (CMC), cortico-cortical connectivity (CCC) as well as functional brain network controllability (FBNC) associated with the modulation of inter-muscular coordination patterns were assessed to provide a perspective on the neural mechanisms for the newly learned motor skills. RESULTS: Significant decreases in elbow flexor IMC, CMC, and increases in CCC were observed. No significant changes were observed for FBNC. CONCLUSION: The results of this study suggest that modulating the inter-muscular coordination of the elbow flexor muscle synergy during isometric force generation is associated with multiple yet distinct changes in functional connectivity across the central and peripheral perspectives. SIGNIFICANCE: Understanding the neural mechanisms of modulating inter-muscular coordination patterns can help inform motor rehabilitation regimens.

The Modified Ashworth Scale (MAS) is one of the most commonly used classification metrics for spasticity. While MAS is easy to implement, it relies on the subjective judgement of the clinician which leads to the poor inter-rater reliability between clinicians. We developed a device worn over the arm which measures angular velocity, angular acceleration, resistance, mechanical power, and elbow extension angle of the elbow joint. This device was used on 32 subjects who have spasticity in the upper forearm by 29 clinicians who evaluated the subjects, yielding a data set of 634 evaluations. Predictive models were developed including linear, decision tree, random forest, and support vector machines using variants of the models for regression and classification. Model hyperparameter tuning and testing was performed using clinician-wise nested cross-validation. The support vector classifier model was found to have the best root mean square error of 0.4449. Using a trained models can help eliminate much of the known variability in the MAS. By creating a more accurate way of assessing spasticity, better treatment plans can be more readily compared to aid in the treatment of spasticity and improve their quality of life.

Neuroplastic changes in motor cortex is essential for the recovery motor function of patients with neurological diseases. To enlarge neuroplastic change, various movements should be provided to stimulate larger motor cortical area, and because hands occupy the largest area, it is especially important. Many wearable robotic devices have been developed for rehabilitation of the hand, and soft robotic devices in particular have drawn attention for their compact design. However, most soft devices provide simple thumb motions, which flex or extend all joints without assistance of opposition/reposition of the carpometacarpal joint although the importance in producing various grasps. In this study, the design of a cable actuated dexterous (CADEX) glove is proposed. For dexterous motion, the structure and orientation of major finger tendons were replicated with exotendons (actuated cables), and four exotendons were used for the thumb with the path optimized to provide flexion/extension of the thumb and decoupled opposition/reposition of the carpometacarpal with other joints. To provide consistent motion, silicon was used for stable anchoring of exotendons while preventing slippage and reducing deformation. The motion generated by the CADEX glove was experimentally evaluated for a single healthy subject. The result shows that the CADEX glove could flex and extend the finger with various ratios among joints, and the opposition/reposition of carpometacarpal joint of the thumb could be achieved consistently with minimal effect on the other joints. The CADEX glove is expected to help providing various tasks which is expected to enhance the functional recovery of patients with neurological disease.

A horseback riding simulator is developed to overcome challenges occurring on real horses. The goal of the simulator is to provide a real horseback riding-like sensation to the user in various situations with limited workspace. This paper focuses on generating various trajectories at different walking speeds with dynamic effects. A walking horse dynamic model is purposed based on a two-linked rimless wheel and is modified with 3D-captured data from a real horse. A controller is also designed for passing and compression-recoil modes, aiding in changing the models walking speed using speed change input. A dynamics simulation is performed and results show that the suggested walking model covers trajectory with measured data and shows more variation.

This paper presents significance of causality analysis in attaining transparency of bilateral teleoperation. The causality analysis enabled novel understanding of teleoperation systems, thereby providing methods for finding transparency-attainable control architectures with proper control parameters. Furthermore, when combined with the causality-based stability, stability could still be guaranteed while attaining transparency. Experimental verification is given for simple 1-DOF teleoperation tasks.

This paper investigates transparent teleoperation using two-channel control architectures. First, we show the transparency attainability using hybrid parameters and present transparency conditions according to each class of two-channel control architectures. In addition, for real implementation, the following practical solutions are introduced: design guidelines for control parameters to reduce the effect of impedance estimation error, and a hybrid control law based upon time delay control for unknown impedance. Finally, the proposed scheme is applied to a haptic simulation using a 1-DOF master and a teleoperation task using a 2-DOF master-slave system. The experimental results show the validity of the theoretical works.

In this paper, causality of teleoperation is described. By using bond-graph modeling, the causality of ideal operation is derived and desirable architectures for the causality are suggested. The importance of causality consideration is demonstrated by that inconsistent causality gives rise to instability, and is confirmed through experiments.

It is widely and frequently observed that industrial robots conducting fast motion involve serious residual vibration, the period of which varies with time. To this problem, this paper presents a practical solution by providing a practical design and application of time-varying input shaping technique (TVIST) for an industrial robot. To suppress the time-varying vibration, at first, a guideline for designing practical TVIST is presented. Following the guideline, then, we design TVIST for a large size 6 degrees of freedom industrial robot. In doing so, a simple yet effective equation is derived from robot dynamic equations to estimate the time-varying period. Furthermore, a simple payload adaptation scheme is also included. The TVIST thus designed is experimented on the industrial robot under spatial motion and payload variation conditions. The experimental results show that the residual vibration is reduced to less than 10% of original one in magnitude, demonstrating that the efficiency of the TVIST does not compromise its effectiveness.