Predictive Simulations with OpenSim Moco to Investigate the Interaction Between Human and Assistive Exoskeleton.

The purpose of this work was to investigate the interaction between human and lower limbs assistive exoskeleton under different levels of assistance, by using computational simulations. To this, a human-exoskeleton interaction model was used and three predictive simulations were carried out with the OpenSim Moco. The results proved that the increase in the level of robot assistance causes a reduction in human effort. In addition, it was possible to verify the RMS torque of both the robot and the human, as well as the muscle activations, for the different levels of assistance simulated. For future work, we intend to run predictive simulations with more complex movements, such as gait free and with obstacles, in addition to using models that can represent a human being with muscle weakness on one side of the body (hemiparesis).

Towards Visual-Tactile Integration of Shoulder and Hand Using Immersive Virtual Reality.

Shoulder-controlled hand neuroprostheses are wearable devices designed to assist hand function in people with cervical spinal cord injury (SCI). They use preserved shoulder movements to control artificial actuators. Due to the concurrent afferent (i.e., shoulder proprioception) and visual (i.e., hand response) feedback, these wearables may affect the user's body somatosensory representation. To investigate this effect, we propose an experimental paradigm that uses immersive virtual reality (VR) environment to emulate the use of a shoulder-controlled hand neuroprostheses and an adapted version of a visual-tactile integration task (i.e., Crossmodal Congruency Task) as an assessment tool. Data from seven non-disabled participants validates the experimental setup, with preliminary statistical analysis revealing no significant difference across the means of VR and visual-tactile integration tasks. The results serve as a proof-of-concept for the proposed paradigm, paving the way for further research with improvements in the experimental design and a larger sample size.

Soft robotics and functional electrical stimulation advances for restoring hand function in people with SCI: a narrative review, clinical guidelines and future directions.

BACKGROUND: Recovery of hand function is crucial for the independence of people with spinal cord injury (SCI). Wearable devices based on soft robotics (SR) or functional electrical stimulation (FES) have been employed to assist the recovery of hand function both during activities of daily living (ADLs) and during therapy. However, the implementation of these wearable devices has not been compiled in a review focusing on the functional outcomes they can activate/elicit/stimulate/potentiate. This narrative review aims at providing a guide both for engineers to help in the development of new technologies and for clinicians to serve as clinical guidelines based on the available technology in order to assist and/or recover hand function in people with SCI. METHODS: A literature search was performed in Scopus, Pubmed and IEEE Xplore for articles involving SR devices or FES systems designed for hand therapy or assistance, published since 2010. Only studies that reported functional outcomes from individuals with SCI were selected. The final collections of both groups (SR and FES) were analysed based on the technical aspects and reported functional outcomes. RESULTS: A total of 37 out of 1101 articles were selected, 12 regarding SR and 25 involving FES devices. Most studies were limited to research prototypes, designed either for assistance or therapy. From an engineering perspective, technological improvements for home-based use such as portability, donning/doffing and the time spent with calibration were identified. From the clinician point of view, the most suitable technical features (e.g., user intent detection) and assessment tools should be determined according to the particular patient condition. A wide range of functional assessment tests were adopted, moreover, most studies used non-standardized tests. CONCLUSION: SR and FES wearable devices are promising technologies to support hand function recovery in subjects with SCI. Technical improvements in aspects such as the user intent detection, portability or calibration as well as consistent assessment of functional outcomes were the main identified limitations. These limitations seem to be be preventing the translation into clinical practice of these technological devices created in the laboratory.

Tracking and Classification of Head Movement for Augmentative and Alternative Communication Systems.

The use of assistive technologies can mitigate or reduce the challenges faced by individuals with motor disabilities to use computer systems. However, those who feature severe involuntary movements often have fewer options at hand. This work describes an application that can recognize the user's head using a conventional webcam, track its motion, model the desired functional movement, and recognize it to enable the use of a virtual keyboard. The proposed classifier features a flexible structure and may be personalized for different user need. Experimental results obtained with participants with no neurological disorders have shown that classifiers based on Hidden Markov Models provided similar or better performance than a classifier based on position threshold. However, motion segmentation and interpretation modules were sensitive to involuntary movements featured by participants with cerebral palsy that took part in the study.

Towards balance assessment using Openpose.

The ability to assess balance is essential to determine a patients ability to mitigate any risk of falling. While current assessment tools exist, they either have limitations in that there is no quantitative data recorded, or that they are impractical for general use in clinical settings. In this work, we aim at assessing balance using single-camera videos. In particular, the proposed method uses OpenPose to calculate the Center of Mass and Center of Pressure trajectories. To determine the validity of this approach, estimates obtained in an experimental study were compared to recordings obtained through the use of 3D motion capture and force plate. Our results indicate that this inexpensive, easy to use, and portable alternative has the potential to act as a suitable replacement to assess balance in clinical settings.

Passive Knee Orthoses Assistance in Functional Electrical Stimulation Cycling in an Individual With Spinal Cord Injury.

Functional Electrical Stimulation (FES) may be used in rehabilitation and assistance of people with Spinal Cord Injury (SCI). One significant application is facilitating physical exercise, mainly when combining FES with mechanical platforms, such as tricycles. However, there are still technical challenges in FES cycling protocols, such as improving control and cycling performance. Here we show how passive elements in knee orthoses during FES cycling could increase the average cadence, taking advantage of the cycling movement. Our approach is twofold. First, we simulated the forward dynamics of a detailed musculoskeletal model with passive elements over the knees. Simulations showed that specific spring stiffness ranges increased the crankset speed during cycling by more than 50%. Using parameters found in simulations, we built a pair of passive orthoses and performed experiments with one individual with SCI. During two days, the volunteer cycled with similar stimulation magnitude with and without the passive elements. We observed that the average crankset speed was higher by more than 10% when the springs were attached to the passive orthoses. These results show the potential of using passive elements to increase cycling speed for FES cycling with similar or even lower stimulation magnitude, leading to longer exercise duration.

Cadence Tracking and Disturbance Rejection in Functional Electrical Stimulation Cycling for Paraplegic Subjects: A Case Study.

Functional electrical stimulation cycling has been proposed as an assistive technology with numerous health and fitness benefits for people with spinal cord injury, such as improvement in cardiovascular function, increase in muscular mass, and reduction of bone mass loss. However, some limitations, for example, lack of optimal control strategies that would delay fatigue, may still prevent this technology from achieving its full potential. In this work, we performed experiments on a person with complete spinal cord injury using a stationary tadpole trike when both cadence tracking and disturbance rejection were evaluated. In addition, two sets of experiments were conducted 6 months apart and considering activation of different muscles. The results showed that reference tracking is achieved above the cadence of 25 rpm with mean absolute errors between 1.9 and 10% when only quadriceps are activated. The disturbance test revealed that interferences may drop the cadence but do not interrupt a continuous movement if the cadence does not drop below 25 rpm, again when only quadriceps are activated. When other muscle groups were added, strong spasticity caused larger errors on reference tracking, but not when a disturbance was applied. In addition, spasticity caused the last experiments to result in less smooth cycling.

Automatic Human Movement Assessment With Switching Linear Dynamic System: Motion Segmentation and Motor Performance.

Performance assessment of human movement is critical in diagnosis and motor-control rehabilitation. Recent developments in portable sensor technology enable clinicians to measure spatiotemporal aspects to aid in the neurological assessment. However, the extraction of quantitative information from such measurements is usually done manually through visual inspection. This paper presents a novel framework for automatic human movement assessment that executes segmentation and motor performance parameter extraction in time-series of measurements from a sequence of human movements. We use the elements of a Switching Linear Dynamic System model as building blocks to translate formal definitions and procedures from human movement analysis. Our approach provides a method for users with no expertise in signal processing to create models for movements using labeled dataset and later use it for automatic assessment. We validated our framework on preliminary tests involving six healthy adult subjects that executed common movements in functional tests and rehabilitation exercise sessions, such as sit-to-stand and lateral elevation of the arms and five elderly subjects, two of which with limited mobility, that executed the sit-to-stand movement. The proposed method worked on random motion sequences for the dual purpose of movement segmentation (accuracy of 72%-100%) and motor performance assessment (mean error of 0%-12%).

Electrical Stimulation to Reduce the Overload in Upper Limbs During Sitting Pivot Transfer in Paraplegic: A Preliminary Study.

Transfer is a key ability and allows greater interact with the environment and social participation. Conversely, paraplegics have great risk of pain and injury in the upper limbs due to joint overloads during activities of daily living, like transfer. The main goal of this study is to verify if the use of functional electrical stimulation (FES) in the lower limbs of paraplegic individuals can assist the sitting pivot transfer (SPT). The secondary objective is to verify if there is a greater participation of the lower limbs during lift pivot phase. A preliminary study was done with one complete paraplegic individual. Temporal parameters were calculated and a kinetic assessment was done during the SPT. The preliminary results showed the feasibility of FES for assisting the SPT.

On the use of discrete steps in robot-aided flexible needle insertion.

Needle steering devices present great potential for improving the safety and accuracy of medical interventions with percutaneous access. Despite significant advances in the field, needle steerability remains an issue to be solved by the scientific community. In this paper, we propose the use of discrete steps in flexible needle insertion, inspired by the manual procedure performed by physicians. Conceptually, the method relies in alternating between two motions: grasp-push and release-retreat. For experimental evaluation, a modified gripper is used along with a 6DOF robotic manipulator to control needle insertion velocity, rotation and grasping. Preliminary results indicate that the use of discrete steps minimizes some negative effects, such as slippage and needle buckling, observed on alternative methods, while preserving their functional advantages.

An EKF-based approach for estimating leg stiffness during walking.

The spring-like behavior is an inherent condition for human walking and running. Since leg stiffness k(leg) is a parameter that cannot be directly measured, many techniques has been proposed in order to estimate it, most of them using force data. This paper intends to address this problem using an Extended Kalman Filter (EKF) based on the Spring-Loaded Inverted Pendulum (SLIP) model. The formulation of the filter only uses as measurement information the Center of Mass (CoM) position and velocity, no a priori information about the stiffness value is known. From simulation results, it is shown that the EKF-based approach can generate a reliable stiffness estimation for walking.

Towards robust 3D visual tracking for motion compensation in beating heart surgery.

In the context of minimally invasive cardiac surgery, active vision-based motion compensation schemes have been proposed for mitigating problems related to physiological motion. However, robust and accurate visual tracking remains a difficult task. The purpose of this paper is to present a robust visual tracking method that estimates the 3D temporal and spatial deformation of the heart surface using stereo endoscopic images. The novelty is the combination of a visual tracking method based on a Thin-Plate Spline (TPS) model for representing the heart surface deformations with a temporal heart motion model based on a time-varying dual Fourier series for overcoming tracking disturbances or failures. The considerable improvements in tracking robustness facing specular reflections and occlusions are demonstrated through experiments using images of in vivo porcine and human beating hearts.

Robust 3D tracking for robotic-assisted beating heart surgery.

The past decades have witnessed the notable development of minimally invasive surgery (MIS). The benefits of this modality of surgery for patients are numerous, shortening convalescence, reducing trauma and surgery costs. In this context, robotic assistance aims to make the surgical act more intuitive and safer. In the domain of cardiac MIS, heartbeat and respiration represent two important sources of disturbances. Even though miniaturized versions of heart stabilizers have been conceived for the MIS scenario, residual motion is still considerable and has to be manually canceled by the surgeon. Our work focuses on computer vision techniques for estimating the 3D motion of the heart relying solely on natural structures on the heart surface for active compensation of physiological motions. We have developed in [2] a visual tracking method for estimating the 3D deformation of a region of interest on the heart surface based on the visual feedback of a stereo endoscope. The method is robust to illumination variations and large tissue deformations (Fig. 1).

Robust 3D visual tracking for robotic-assisted cardiac interventions.

In the context of minimally invasive cardiac surgery, active vision-based motion compensation schemes have been proposed for mitigating problems related to physiological motion. However, robust and accurate visual tracking is a difficult task. The purpose of this paper is to present a hybrid tracker that estimates the heart surface deformation using the outputs of multiple visual tracking techniques. In the proposed method, the failure of an individual technique can be circumvented by the success of others, enabling the robust estimation of the heart surface deformation with increased spatial resolution. In addition, for coping with the absence of visual information due to motion blur or occlusions, a temporal heart motion model is incorporated as an additional support for the visual tracking task. The superior performance of the proposed technique compared to existing techniques individually is demonstrated through experiments conducted on recorded images of an in vivo minimally invasive CABG using the DaVinci robotic platform.

Exploring peripheral mechanism of tremor on neuromusculoskeletal model: a general simulation study.

This paper provides a general simulation study on tremor based on a modular neuromusculoskeletal model. It focuses on the peripheral mechanism. It is known that the reflex loops in the peripheral nervous system have influences on the tremor. A neuromusculoskeletal model with several reflex loops is developed to explore the dynamics of tremor. The muscle model is derived from a Hill-type muscle model. The reflex loops include the spindle organ, Golgi tendon organ, and Renshaw cell. Their effects are investigated quantitatively in detail. A two-muscle (agonist/antagonist) system with interaction is further studied. Moreover, a model in combination with the central oscillation and peripheral system is developed. Some results are in accordance with the previous research, whereas some new findings are proposed according to the simulation study.

Online pathological tremor characterization using extended Kalman filtering.

This paper describes different algorithms that perform online pathological tremor characterization in terms of acceleration. Two distinct parametricmodels are used, an Auto-Regressive (AR) model and an harmonic model. Both models are recursively estimated with Extended Kalman Filters (EKFs). Experimental data was obtained with low cost sensors and the results are compared in terms of spectrogram estimation and prediction performance.

Motion prediction for tracking the beating heart.

In the past few years, several research groups have worked on the design of efficient motion compensation systems for cardiac robotic-assisted Minimally Invasive Surgery (MIS). By providing surgeons with a stabilized work environment, significant improvements of the precision and repeatability of their gestures can be achieved. The design of a motion compensation system requires the accurate measurement of the heart motion, which can be achieved using computer vision techniques for tracking cardiac structures on the heart surface. However, most works in the literature focus on the representation and localization of cardiac structures while few explore their motion dynamics. In this paper we study and implement different adaptive methods for predicting the future heart motion using Kalman filtering. By exploiting the quasi-periodic nature of the heart motion, we are able to increase tracking robustness and computational efficiency. The experimental results indicate the significant increase in tracking performance when heart motion prediction is employed.