Gait impairments are among the most common hallmarks of Parkinson’s disease (PD), usually appearing in the early stage and becoming a major cause of disability with disease progression. Accurate assessment of gait features is critical to personalized rehabilitation for patients with PD, yet difficult to be routinely carried out as clinical diagnosis using rating scales relies heavily on clinical experience. Moreover, the popular rating scales cannot ensure fine quantification of gait impairments for patients with mild symptoms. Developing quantitative assessment methods that can be used in natural and home-based environments is highly demanded. In this study, we address the challenges by developing an automated video-based Parkinsonian gait assessment method using a novel skeleton-silhouette fusion convolution network. In addition, seven network-derived supplementary features, including critical aspects of gait impairment (gait velocity, arm swing, etc.), are extracted to provide continuous measures enhancing low-resolution clinical rating scales. Evaluation experiments were conducted on a dataset collected with 54 patients with early PD and 26 healthy controls. The results show that the proposed method accurately predicted the patients’ unified Parkinson’s disease rating scale (UPDRS) gait scores (71.25% match on clinical assessment) and discriminated between PD patients and healthy subjects with a sensitivity of 92.6%. Moreover, three proposed supplementary features (i.e., arm swing amplitude, gait velocity, and neck forward bending angle) turned out to be effective gait dysfunction indicators with Spearman correlation coefficients of 0.78, 0.73, and 0.43 matching the rating scores, respectively. Since the proposed system requires only two smartphones, it holds a significant benefit for home-based quantitative assessment of PD, especially for detecting early-stage PD. Furthermore, the proposed supplementary features can enable high-resolution assessments of PD for providing subject-specific accurate treatments.

Parkinson’s disease (PD) is a prevalent brain disorder, and PD diagnosis is crucial for treatment. Existing methods for PD diagnosis are mainly focused on behavior analysis, while the functional neurodegeneration of PD has not been well investigated. This paper proposes a method to signify functional neurodegeneration of PD with dynamic functional connectivity analysis. A functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation from 50 PD patients and 41 age-matched healthy controls in clinical walking tests. Dynamic functional connectivity was constructed with sliding-window correlation analysis, and k-means clustering was applied to generate the key brain connectivity states. Dynamic state features including state occurrence probability, state transition percentage and state statistical features were extracted to quantify the variations of brain functional networks. A support vector machine was trained to classify PD patients and healthy controls. Statistical analysis was conducted to investigate the difference between PD patients and healthy controls as well as the relationship between dynamic state features and the MDS-UPDRS sub-score of gait. The results showed that PD patients had a higher probability of transiting to brain connectivity states with high levels of information transmission compared with healthy controls. The MDS-UPDRS sub-score of gait and the dynamics state features showed a significant correlation. Moreover, the proposed method had better classification performances than the available fNIRS-based methods in terms of accuracy and F1 score. Thus, the proposed method well signified functional neurodegeneration of PD, and the dynamic state features may serve as promising functional biomarkers for PD diagnosis.

Disabilities after neural injury, such as stroke, bring tremendous burden to patients, families and society. Besides the conventional constrained-induced training with a paretic arm, bilateral rehabilitation training involves both the ipsilateral and contralateral sides of the neural injury, fitting well with the fact that both arms are needed in common activities of daily living (ADLs), and can promote good functional recovery. In this work, the fusion of a gesture sensor and a haptic sensor with force feedback capabilities has enabled a bilateral rehabilitation training therapy. The Leap Motion gesture sensor detects the motion of the healthy hand, and the omega.7 device can detect and assist the paretic hand, according to the designed cooperative task paradigm, as much as needed, with active force feedback to accomplish the manipulation task. A virtual scenario has been built up, and the motion and force data facilitate instantaneous visual and audio feedback, as well as further analysis of the functional capabilities of the patient. This task-oriented bimanual training paradigm recruits the sensory, motor and cognitive aspects of the patient into one loop, encourages the active involvement of the patients into rehabilitation training, strengthens the cooperation of both the healthy and impaired hands, challenges the dexterous manipulation capability of the paretic hand, suits easy of use at home or centralized institutions and, thus, promises effective potentials for rehabilitation training.

Hysteresis is a typical nonlinear characteristic that exists in mechanical systems, which brings significant challenges to the robust tracking control of twisted tendon-sheath systems. In this paper, an active data-driven model is proposed to describe the hysteresis phenomenon of a twisted tendon-sheath system based on the Koopman operator, and a robust controller is designed to cancel the effect of the model error and deal with the physical constraints in practical applications. First, by utilizing the Koopman theory, an active data-driven model is built to describe the twisted tendon-sheath hysteresis system in a straightforward linear form. Then, an active model is proposed based on a modified set-membership filter to estimate the finite-dimensional approximation error. Furthermore, a robust controller is developed by taking advantage of both the magnitude and bound of the model error (obtained by the active model) to enhance the control performance while considering security constraints. To the best of our knowledge, the rule-based constraint term is first considered in the data-driven model-based control scheme to prevent potential instabilities for the twisted tendon-sheath system. The theoretical stability of the closed-loop system is proven by using the barrier Lyapunov theory to ensure the security boundary. Extensive experiments are also carried out on a self-built robotic ureteroscopy prototype to demonstrate the superior tracking performance and robustness of the proposed method. Note to Practitioners —This paper is motivated by the accurate transmission problems of twisted tendon-sheath hysteresis systems, which aims to provide a precise active modeling method and a robust controller for the robotic-assisted instrument (e.g., endoscope, catheter, etc.) twisting in the sheath/orifice. Most existing studies on tendon-sheath hysteresis systems realize trajectory tracking controllers by using parametric-model-based compensation, which still lacks a practical data-driven modeling approach to characterize the hysteresis phenomenon in the linear form, and ignore the security constraints of tendon outputs. Based on the set-membership filter, this paper builds an active Koopman-based model, which is a practical method to follow for systems characterized by complex dynamics. Subsequently, by employing the constructed active model and a rule-based term to handle output constraints, a robust controller is elaborately designed to realize accurate tracking control for twisted tendon-sheath hysteresis systems. In particular, no priori knowledge of the complex dynamics is required in the implementation and gains selection of the proposed controller, which holds theoretically and practically significance for various tendon-sheath hysteresis systems. A series of comparative hardware experiments further validate the effectiveness and robustness of the suggested control scheme. In future work, we will aim to extend the applicability of the proposed active modeling and control scheme to interventional procedures of endoscopic operation robots for complex steerings with varying sheath configurations.