Reducing residual forces in spinal fusion using a custom-built rod bending machine.

BACKGROUND AND OBJECTIVE: As part of spinal fusion surgery, shaping the rod implant to align with the anatomy is a tedious, error-prone, and time-consuming manual process. Inadequately contoured rod implants introduce stress on the screw-bone interface of the pedicle screws, potentially leading to screw loosening or even pull-out. METHODS: We propose the first fully automated solution to the rod bending problem by leveraging the advantages of augmented reality and robotics. Augmented reality not only enables the surgeons to intraoperatively digitize the screw positions but also provides a human-computer interface to the wirelessly integrated custom-built rod bending machine. Furthermore, we introduce custom-built test rigs to quantify per screw absolute tensile/compressive residual forces on the screw-bone interface. Besides residual forces, we have evaluated the required bending times and reducer engagements, and compared our method to the freehand gold standard. RESULTS: We achieved a significant reduction of the average absolute residual forces from for the freehand gold standard to (p=0.0015) using the bending machine. Moreover, our bending machine reduced the average time to instrumentation per screw from to . Reducer engagements per rod were significantly decreased from an average of 1.00±1.14 to 0.11±0.32 (p=0.0037). CONCLUSION: The combination of augmented reality and robotics has the potential to improve surgical outcomes while minimizing the dependency on individual surgeon skill and dexterity.

Segmenting/Pre-Processing Data from Bone Screw Thread-Stripping Tests.

Bone screws must be appropriately tightened to achieve optimal patient outcomes. If over-torqued, the threads formed in the bone may break, compromising the strength of the fixation; and, if under-torqued, the screw may loosen over time, compromising the stability. Previous work has proposed a model-based system to automatically determine the optimal insertion torque. This system consists of a reverse-modelling step to determine strength, and a forward modelling step to determine maximum torque. These have previously been tested in isolation, however future work must test the combined system. To do so, the data must be segmented and pre-processed. This was done based on specific features of the recorded data. The methodology was tested on 50 screw-insertion data sets across 5 different materials. With the parameters used, all data sets were correctly segmented. This will form a basis for the further processing of the data and validating the combined systemClinical relevance: The system for torque limit determination must be tested in its entirety to properly asses its performance. This paper discusses some of the steps required to pre-process the data to make this assessment. If successful, this system may improve patient outcomes in orthopaedic surgery.

Automatic patient positioning based on robot rotational workspace for extended reality.

PURPOSE: Understanding the properties and aspects of the robotic system is essential to a successful medical intervention, as different capabilities and limits characterize each. Robot positioning is a crucial step in the surgical setup that ensures proper reachability to the desired port locations and facilitates docking procedures. This very demanding task requires much experience to master, especially with multiple trocars, increasing the barrier of entry for surgeons in training. METHODS: Previously, we demonstrated an Augmented Reality-based system to visualize the rotational workspace of the robotic system and proved it helps the surgical staff to optimize patient positioning for single-port interventions. In this work, we implemented a new algorithm to allow for an automatic, real-time robotic arm positioning for multiple ports. RESULTS: Our system, based on the rotational workspace data of the robotic arm and the set of trocar locations, can calculate the optimal position of the robotic arm in milliseconds for the positional and in seconds for the rotational workspace in virtual and augmented reality setups. CONCLUSIONS: Following the previous work, we extended our system to support multiple ports to cover a broader range of surgical procedures and introduced the automatic positioning component. Our solution can decrease the surgical setup time and eliminate the need to repositioning the robot mid-procedure and is suitable both for the preoperative planning step using VR and in the operating room-running on an AR headset.

Improved distinct bone segmentation in upper-body CT through multi-resolution networks.

PURPOSE: Automated distinct bone segmentation from CT scans is widely used in planning and navigation workflows. U-Net variants are known to provide excellent results in supervised semantic segmentation. However, in distinct bone segmentation from upper-body CTs a large field of view and a computationally taxing 3D architecture are required. This leads to low-resolution results lacking detail or localisation errors due to missing spatial context when using high-resolution inputs. METHODS: We propose to solve this problem by using end-to-end trainable segmentation networks that combine several 3D U-Nets working at different resolutions. Our approach, which extends and generalizes HookNet and MRN, captures spatial information at a lower resolution and skips the encoded information to the target network, which operates on smaller high-resolution inputs. We evaluated our proposed architecture against single-resolution networks and performed an ablation study on information concatenation and the number of context networks. RESULTS: Our proposed best network achieves a median DSC of 0.86 taken over all 125 segmented bone classes and reduces the confusion among similar-looking bones in different locations. These results outperform our previously published 3D U-Net baseline results on the task and distinct bone segmentation results reported by other groups. CONCLUSION: The presented multi-resolution 3D U-Nets address current shortcomings in bone segmentation from upper-body CT scans by allowing for capturing a larger field of view while avoiding the cubic growth of the input pixels and intermediate computations that quickly outgrow the computational capacities in 3D. The approach thus improves the accuracy and efficiency of distinct bone segmentation from upper-body CT.

Validation of Non-Restrictive Inertial Gait Analysis of Individuals with Incomplete Spinal Cord Injury in Clinical Settings.

Inertial Measurement Units (IMUs) have gained popularity in gait analysis and human motion tracking, and they provide certain advantages over stationary line-of-sight-dependent Optical Motion Capture (OMC) systems. IMUs appear as an appropriate alternative solution to reduce dependency on bulky, room-based hardware and facilitate the analysis of walking patterns in clinical settings and daily life activities. However, most inertial gait analysis methods are unpractical in clinical settings due to the necessity of precise sensor placement, the need for well-performed calibration movements and poses, and due to distorted magnetometer data in indoor environments as well as nearby ferromagnetic material and electronic devices. To address these limitations, recent literature has proposed methods for self-calibrating magnetometer-free inertial motion tracking, and acceptable performance has been achieved in mechanical joints and in individuals without neurological disorders. However, the performance of such methods has not been validated in clinical settings for individuals with neurological disorders, specifically individuals with incomplete Spinal Cord Injury (iSCI). In the present study, we used recently proposed inertial motion-tracking methods, which avoid magnetometer data and leverage kinematic constraints for anatomical calibration. We used these methods to determine the range of motion of the Flexion/Extension (F/E) hip and Abduction/Adduction (A/A) angles, the F/E knee angles, and the Dorsi/Plantar (D/P) flexion ankle joint angles during walking. Data (IMU and OMC) of five individuals with no neurological disorders (control group) and five participants with iSCI walking for two minutes on a treadmill in a self-paced mode were analyzed. For validation purposes, the OMC system was considered as a reference. The mean absolute difference (MAD) between calculated range of motion of joint angles was 5.00°, 5.02°, 5.26°, and 3.72° for hip F/E, hip A/A, knee F/E, and ankle D/P flexion angles, respectively. In addition, relative stance, swing, double support phases, and cadence were calculated and validated. The MAD for the relative gait phases (stance, swing, and double support) was 1.7%, and the average cadence error was 0.09 steps/min. The MAD values for RoM and relative gait phases can be considered as clinically acceptable. Therefore, we conclude that the proposed methodology is promising, enabling non-restrictive inertial gait analysis in clinical settings.

Improved distinct bone segmentation from upper-body CT using binary-prediction-enhanced multi-class inference.

PURPOSE: Automated distinct bone segmentation has many applications in planning and navigation tasks. 3D U-Nets have previously been used to segment distinct bones in the upper body, but their performance is not yet optimal. Their most substantial source of error lies not in confusing one bone for another, but in confusing background with bone-tissue. METHODS: In this work, we propose binary-prediction-enhanced multi-class (BEM) inference, which takes into account an additional binary background/bone-tissue prediction, to improve the multi-class distinct bone segmentation. We evaluate the method using different ways of obtaining the binary prediction, contrasting a two-stage approach to four networks with two segmentation heads. We perform our experiments on two datasets: An in-house dataset comprising 16 upper-body CT scans with voxelwise labelling into 126 distinct classes, and a public dataset containing 50 synthetic CT scans, with 41 different classes. RESULTS: The most successful network with two segmentation heads achieves a class-median Dice coefficient of 0.85 on cross-validation with the upper-body CT dataset. These results outperform both our previously published 3D U-Net baseline with standard inference, and previously reported results from other groups. On the synthetic dataset, we also obtain improved results when using BEM-inference. CONCLUSION: Using a binary bone-tissue/background prediction as guidance during inference improves distinct bone segmentation from upper-body CT scans and from the synthetic dataset. The results are robust to multiple ways of obtaining the bone-tissue segmentation and hold for the two-stage approach as well as for networks with two segmentation heads.

Fabrication and Characterization of a Flexible FBG-Based Shape Sensor Using Single-Mode Fibers.

Minimally invasive surgical procedures have become the preferable option, as the recovery period and the risk of infections are significantly lower than traditional surgeries. However, the main challenge in using flexible tools for minimal surgical interventions is the lack of precise feedback on their shape and tip position inside the patient's body. Shape sensors based on fiber Bragg gratings (FBGs) can provide accurate shape information depending on their design. One of the most common configurations in FBG-based shape sensors is to attach three single-mode optical fibers with arrays of FBGs in a triangular fashion around a substrate. Usually, the selected substrates dominate the bending stiffness of the sensor probe, as they have a larger diameter and show less flexibility compared to the optical fibers. Although sensors with this configuration can accurately estimate the shape, they cannot be implemented in flexible endoscopes where large deflections are expected. This paper investigates the shape sensor's performance when using a superelastic substrate with a small diameter instead of a substrate with dominating bending stiffness. A generalized model is also designed for characterizing this type of flexible FBG-based shape sensor. Moreover, we evaluated the sensor in single and multi-bend deformations using two shape reconstruction methods.

Real-time motion onset recognition for robot-assisted gait rehabilitation.

BACKGROUND: Many patients with neurological movement disorders fear to fall while performing postural transitions without assistance, which prevents them from participating in daily life. To overcome this limitation, multi-directional Body Weight Support (BWS) systems have been developed allowing them to perform training in a safe environment. In addition to overground walking, these innovative/novel systems can assist patients to train many more gait-related tasks needed for daily life under very realistic conditions. The necessary assistance during the users' movements can be provided via task-dependent support designs. One remaining challenge is the manual switching between task-dependent supports. It is error-prone, cumbersome, distracts therapists and patients, and interrupts the training workflow. Hence, we propose a real-time motion onset recognition model that performs automatic support switching between standing-up and sitting-down transitions and other gait-related tasks (8 classes in total). METHODS: To predict the onsets of the gait-related tasks, three Inertial Measurement Units (IMUs) were attached to the sternum and middle of outer thighs of 19 controls without neurological movement disorders and two individuals with incomplete Spinal Cord Injury (iSCI). The data of IMUs obtained from different gait tasks was sent synchronously to a real-time data acquisition system through a custom-made Bluetooth-EtherCAT gateway. In the first step, data was applied offline for training five different classifiers. The best classifier was chosen based on F1-score results of a Leave-One-Participant-Out Cross-Validation (LOPOCV), which is an unbiased way of testing. In a final step, the chosen classifier was tested in real time with an additional control participant to demonstrate feasibility for real-time classification. RESULTS: Testing five different classifiers, the best performance was obtained in a single-layer neural network with 25 neurons. The F1-score of [Formula: see text] and [Formula: see text] are achieved on testing using LOPOCV and test data ([Formula: see text], participants = 20), respectively. Furthermore, the results from the implemented real-time classifier were compared with the offline classifier and revealed nearly identical performance (difference = [Formula: see text]). CONCLUSIONS: A neural network classifier was trained for identifying the onset of gait-related tasks in real time. Test data showed convincing performance for offline and real-time classification. This demonstrates the feasibility and potential for implementing real-time onset recognition in rehabilitation devices in future.

Quantitative Evaluation of the Thickness of the Available Manipulation Volume Inside the Knee Joint Capsule for Minimally Invasive Robotic Unicondylar Knee Arthroplasty.

OBJECTIVE: Developing robotic tools that introduce substantial changes in the surgical workflow is challenging because quantitative requirements are missing. Experiments on cadavers can provide valuable information to derive workspace requirements, tool size, and surgical workflow. This work aimed to quantify the volume inside the knee joint available for manipulation of minimally invasive robotic surgical tools. In particular, we aim to develop a novel procedure for minimally invasive unicompartmental knee arthroplasty (UKA) using a robotic laser-cutting tool. METHODS: Contrast solution was injected into nine cadaveric knees and computed tomography scans were performed to evaluate the tool manipulation volume inside the knee joints. The volume and distribution of the contrast solution inside the knee joints were analyzed with respect to the femur, tibia, and the anatomical locations that need to be reached by a laser-cutting tool to perform bone resection for a standard UKA implant. RESULTS: Quantitative information was determined about the tool manipulation volume inside these nine knee joints and its distribution around the cutting lines required for a standard implant. CONCLUSION: Based on the volume distribution, we could suggest a possible workflow for minimally invasive UKA, which provides a large manipulation volume, and deducted that for the proposed workflow, an instrument with a thickness of 5-8 mm should be feasible. SIGNIFICANCE: We present quantitative information on the three-dimensional distribution of the maximally available volume inside the knee joint. Such quantitative information lays the basis for developing surgical tools that introduce substantial changes in the surgical workflow.

The Functional Rotational Workspace of a Human-Robot System can be Influenced by Adjusting the Telemanipulator Handle Orientation.

The handle design of telemanipulation master devices has not been extensively studied so far. However, the master device handle is an integral part of the robotic system through which the user interacts with the system. Previous work showed that the size and shape of the functional rotational workspace of the human-robot system and its usability are influenced by the design of the master device handle. Still, in certain situations, e.g., due to user preference, a specific grasp type handle might be desired. Therefore, in this article, we provide a systematic approach on how to assess and adjust the functional rotational workspace of a human-robot system. We investigated the functional rotational workspace with two exemplary grasp type handles and two different mounting orientations for each handle. The results showed that by adapting the handle orientation in the home configuration of the telemanipulator, the functional rotational workspace of the human-robot system can be adjusted systematically to cover more of the mechanical workspace of the master device. Finally, we deduct recommendations on how to choose and adjust a telemanipulator handle.

Force quantification and simulation of pedicle screw tract palpation using direct visuo-haptic volume rendering.

PURPOSE: We present a feasibility study for the visuo-haptic simulation of pedicle screw tract palpation in virtual reality, using an approach that requires no manual processing or segmentation of the volumetric medical data set. METHODS: In a first experiment, we quantified the forces and torques present during the palpation of a pedicle screw tract in a real boar vertebra. We equipped a ball-tipped pedicle probe with a 6-axis force/torque sensor and a motion capture marker cluster. We simultaneously recorded the pose of the probe relative to the vertebra and measured the generated forces and torques during palpation. This allowed us replaying the recorded palpation movements in our simulator and to fine-tune the haptic rendering to approximate the measured forces and torques. In a second experiment, we asked two neurosurgeons to palpate a virtual version of the same vertebra in our simulator, while we logged the forces and torques sent to the haptic device. RESULTS: In the experiments with the real vertebra, the maximum measured force along the longitudinal axis of the probe was 7.78 N and the maximum measured bending torque was 0.13 Nm. In an offline simulation of the motion of the pedicle probe recorded during the palpation of a real pedicle screw tract, our approach generated forces and torques that were similar in magnitude and progression to the measured ones. When surgeons tested our simulator, the distributions of the computed forces and torques were similar to the measured ones; however, higher forces and torques occurred more frequently. CONCLUSIONS: We demonstrated the suitability of direct visual and haptic volume rendering to simulate a specific surgical procedure. Our approach of fine-tuning the simulation by measuring the forces and torques that are prevalent while palpating a real vertebra produced promising results.

Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods.

BACKGROUND: Arm weight compensation with rehabilitation robots for stroke patients has been successfully used to increase the active range of motion and reduce the effects of pathological muscle synergies. However, the differences in structure, performance, and control algorithms among the existing robotic platforms make it hard to effectively assess and compare human arm weight relief. In this paper, we introduce criteria for ideal arm weight compensation, and furthermore, we propose and analyze three distinct arm weight compensation methods (Average, Full, Equilibrium) in the arm rehabilitation exoskeleton 'ARMin'. The effect of the best performing method was validated in chronic stroke subjects to increase the active range of motion in three dimensional space. METHODS: All three methods are based on arm models that are generalizable for use in different robotic devices and allow individualized adaptation to the subject by model parameters. The first method Average uses anthropometric tables to determine subject-specific parameters. The parameters for the second method Full are estimated based on force sensor data in predefined resting poses. The third method Equilibrium estimates parameters by optimizing an equilibrium of force/torque equations in a predefined resting pose. The parameters for all three methods were first determined and optimized for temporal and spatial estimation sensitivity. Then, the three methods were compared in a randomized single-center study with respect to the remaining electromyography (EMG) activity of 31 healthy participants who performed five arm poses covering the full range of motion with the exoskeleton robot. The best method was chosen for feasibility tests with three stroke patients. In detail, the influence of arm weight compensation on the three dimensional workspace was assessed by measuring of the horizontal workspace at three different height levels in stroke patients. RESULTS: All three arm weight compensation methods reduced the mean EMG activity of healthy subjects to at least 49% compared with the no compensation reference. The Equilibrium method outperformed the Average and the Full methods with a highly significant reduction in mean EMG activity by 19% and 28% respectively. However, upon direct comparison, each method has its own individual advantages such as in set-up time, cost, or required technology. The horizontal workspace assessment in poststroke patients with the Equilibrium method revealed potential workspace size-dependence of arm height, while weight compensation helped maximize the workspace as much as possible. CONCLUSION: Different arm weight compensation methods were developed according to initially defined criteria. The methods were then analyzed with respect to their sensitivity and required technology. In general, weight compensation performance improved with the level of technology, but increased cost and calibration efforts. This study reports a systematic way to analyze the efficacy of different weight compensation methods using EMG. Additionally, the feasibility of the best method, Equilibrium, was shown by testing with three stroke patients. In this test, a height dependence of the workspace size also seemed to be present, which further highlights the importance of patient-specific weight compensation, particularly for training at different arm heights. TRIAL REGISTRATION: ClinicalTrials.gov,NCT02720341. Registered 25 March 2016.

Do we need complex rehabilitation robots for training complex tasks?

One key question in motor learning is how the complex tasks in daily life - those that require coordinated movements of multiple joints - should be trained. Often, complex tasks are directly taught as a whole, even though training of simple movement components before training the entire movement has been shown to be more effective for particularly complex tasks ("part-whole transfer paradigm"). The important implication of the part-whole transfer paradigm, e.g. on the field of rehabilitation robotics, is that training of most complex tasks could be simplified and, subsequently, devices used to train can become simpler and more affordable. In this way, robot-assisted rehabilitation could become more accessible. However, often the last step in the training process is forgotten: the recomposition of several simple movement components to a complete complex movement. Therefore, at least for the last training step, a complex rehabilitation device may be required.In a pilot study, we wanted to investigate if a complex robotic device (e.g. an exoskeleton robot with many degrees of freedom), such as the ARMin rehabilitation robot, is really beneficial for training the coordination between several simpler movement components or if training using visual feedback would lead to equal benefits. In a study, involving 16 healthy participants, who were instructed in a complex rugby motion, we could show first trends on the following two aspects: i) the part-whole transfer paradigm seems to hold true and therefore, simple robots might be used for training movement primitives. ii) Visual feedback does not seem to have the same potential, at least in healthy humans, to replace visuo-haptic guidance for movement recomposition of complex tasks. Therefore, complex rehabilitation robots seem to be beneficial for training complex real-life tasks.

Optoacoustic Tissue Differentiation Using a Mach-Zehnder Interferometer.

Laser osteotomy offers a way to make precise and less traumatic cuts smaller than conventional mechanical bone surgery tools. To fully exploit the advantages of laser osteotomy over conventional osteotomy, real-time feedback to differentiate the hard bone from the surrounding soft tissues is desired. In this study, we differentiated various tissue types-hard and soft bone, fat, muscle, and skin tissues from five proximal and distal fresh porcine femurs-based on cutting sounds. For laser ablation, an Nd:YAG laser was used to create ten craters on the surface of each proximal and distal femurs. For sound recording, the probing beam of a Mach-Zehnder interferometer was placed 5 cm away from each ablation site. For offline tissue differentiation, we investigated the measurements by looking at the amplitude frequency band between 0.83 and 1.25 MHz, which provides the least average classification error. Then, we used principal component analysis to reduce the dimensionality and the 95% confidence ellipsoid (Mahalanobis distance) method to differentiate between tissues based on the acoustic shock wave. A set of 14 400 data points, measured from ten craters in four proximal and distal femurs, was used as "training data," while a set of 3600 data points from ten craters in the remaining proximal and distal femurs was considered as "testing data." As is seen in the confusion matrix, the experimental-based scores of hard and soft bones, fat, muscles, and skin yielded average classification errors (with leave-one-out cross validation) of 0.11%, 57.69%, 0.06%, 0.14%, and 2.92%, respectively. The results of this study demonstrate a promising technique for differentiating tissues during laser osteotomy.

Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions.

Smart laser technologies are desired that can accurately cut and characterize tissues, such as bone and muscle, with minimal thermal damage and fast healing. Using a long-pulsed laser with a 0.5-10  ms pulse width at a wavelength of 1.07  µm, we investigated the optimum laser parameters for producing craters with minimal thermal damage under both wet and dry conditions. In different tissues (bone and muscle), we analyzed craters of various morphologies, depths, and volumes. We used a two-way Analysis of Variance (ANOVA) test to investigate whether there are significant differences in the ablation efficiency in wet versus dry conditions at each level of the pulse energy. We found that bone and muscle tissue ablated under wet conditions produced fewer cracks and less thermal damage around the craters than under dry conditions. In contrast to muscle, the ablation efficiency of bone under wet conditions was not higher than under dry conditions. Tissue differentiation was carried out based on measured acoustic waves. A Principal Component Analysis of the measured acoustic waves and Mahalanobis distances were used to differentiate bone and muscle under wet conditions. Bone and muscle ablated in wet conditions demonstrated a classification error of less than 6.66 % and 3.33 %, when measured by a microphone and a fiber Bragg grating, respectively.

Rowing Simulator Modulates Water Density to Foster Motor Learning.

Although robot-assisted training is present in various fields such as sports engineering and rehabilitation, provision of training strategies that optimally support individual motor learning remains as a challenge. Literature has shown that guidance strategies are useful for beginners, while skilled trainees should benefit from challenging conditions. The Challenge Point Theory also supports this in a way that learning is dependent on the available information, which serves as a challenge to the learner. So, learning can be fostered when the optimal amount of information is given according to the trainee's skill. Even though the framework explains the importance of difficulty modulation, there are no practical guidelines for complex dynamic tasks on how to match the difficulty to the trainee's skill progress. Therefore, the goal of this study was to determine the impact on learning of a complex motor task by a modulated task difficulty scheme during the training sessions, without distorting the nature of task. In this 3-day protocol study, we compared two groups of naïve participants for learning a sweep rowing task in a highly sophisticated rowing simulator. During trainings, groups received concurrent visual feedback displaying the requested oar movement. Control group performed the task under constant difficulty in the training sessions. Experimental group's task difficulty was modulated by changing the virtual water density that generated different heaviness of the simulated water-oar interaction, which yielded practice variability. Learning was assessed in terms of spatial and velocity magnitude errors and the variability for these metrics. Results of final day tests revealed that both groups reduced their error and variability for the chosen metrics. Notably, in addition to the provision of a very well established visual feedback and knowledge of results, experimental group's variable training protocol with modulated difficulty showed a potential to be advantageous for the spatial consistency and velocity accuracy. The outcomes of training and test runs indicate that we could successfully alter the performance of the trainees by changing the density value of the virtual water. Therefore, a follow-up study is necessary to investigate how to match different density values to the skill and performance improvement of the participants.

When a robot teaches humans: Automated feedback selection accelerates motor learning.

A multitude of robotic systems have been developed to foster motor learning. Some of these robotic systems featured augmented visual or haptic feedback, which was automatically adjusted to the trainee's performance. However, selecting the type of feedback to achieve the training goal usually remained up to a human trainer. We automated this feedback selection within a robotic rowing simulator: Four spatial errors and one velocity error were considered, all related to trunk-arm sweep rowing set as the training goal to be learned. In an alternating sequence of assessments without augmented feedback and training sessions with augmented, concurrent feedback, the experimental group received feedback, thus addressing the main shortcoming of the previous assessment. With this approach, each participant of the experimental group received an individual sequence of 10 training sessions with feedback. The training sequences from participants in the experimental group were consecutively applied for participants in the control group. Both groups were able to reduce spatial and velocity errors due to training. The learning rate of the requested velocity profile was significantly higher for the experimental group compared with the control group. Thus, our robotic rowing simulator accelerated motor learning by automated feedback selection. This demonstration of a working, closed-loop selection of types of feedback, i.e., training conditions, could serve as the basis for other robotic trainers incorporating further human expertise and artificial intelligence.

Overground walking patterns after chronic incomplete spinal cord injury show distinct response patterns to unloading.

BACKGROUND: Body weight support (BWS) is often provided to incomplete spinal cord injury (iSCI) patients during rehabilitation to enable gait training before full weight-bearing is recovered. Emerging robotic devices enable BWS during overground walking, increasing task-specificity of the locomotor training. However, in contrast to a treadmill setting, there is little information on how unloading is integrated into overground locomotion. We investigated the effect of a transparent multi-directional BWS system on overground walking patterns at different levels of unloading in individuals with chronic iSCI (CiSCI) compared to controls. METHODS: Kinematics of 12 CiSCI were analyzed at six different BWS levels from 0 to 50% body weight unloading during overground walking at 2kmh- 1 and compared to speed-matched controls. RESULTS: In controls, temporal parameters, single joint trajectories, and intralimb coordination responded proportionally to the level of unloading, while spatial parameters remained unaffected. In CiSCI, unloading induced similar changes in temporal parameters. CiSCI, however, did not adapt their intralimb coordination or single joint trajectories to the level of unloading. CONCLUSIONS: The findings revealed that continuous, dynamic unloading during overground walking results in subtle and proportional gait adjustments corresponding to changes in body load. CiSCI demonstrated diminished responses in specific domains of gait, indicating that their altered neural processing impeded the adjustment to environmental constraints. CiSCI retain their movement patterns under overground unloading, indicating that this is a viable locomotor therapy tool that may also offer a potential window on the diminished neural control of intralimb coordination.

Laser-induced breakdown spectroscopy as a potential tool for autocarbonization detection in laserosteotomy.

In laserosteotomy, it is vital to avoid thermal damage of the surrounding tissue, such as carbonization, since carbonization does not only deteriorate the ablation efficiency but also prolongs the healing process. The state-of-the-art method to avoid carbonization is irrigation systems; however, it is difficult to determine the desired flow rate of the air and cooling water based on previous experiments without online monitoring of the bone surface. Lack of such feedback during the ablation process can cause carbonization in case of a possible error in the irrigation system or slow down the cutting process when irrigating with too much cooling water. The aim of this paper is to examine laser-induced breakdown spectroscopy as a potential tool for autocarbonization detection in laserosteotomy. By monitoring the laser-driven plasma generated during nanosecond pulse ablation of porcine bone samples, carbonization is hypothesized to be detectable. For this, the collected spectra were analyzed based on variation of a specific pair of emission line ratios in both groups of samples: normal and carbonized bone. The results confirmed a high accuracy of over 95% in classifying normal and carbonized bone.

Feedforward model based arm weight compensation with the rehabilitation robot ARMin.

Highly impaired stroke patients at early stages of recovery are unable to generate enough muscle force to lift the weight of their own arm. Accordingly, task-related training is strongly limited or even impossible. However, as soon as partial or full arm weight support is provided, patients are enabled to perform arm rehabilitation training again throughout an increased workspace. In the literature, the current solutions for providing arm weight support are mostly mechanical. These systems have components that restrict the freedom of movement or entail additional disturbances. A scalable weight compensation for upper and lower arm that is online adjustable as well as generalizable to any robotic system is necessary. In this paper, a model-based feedforward weight compensation of upper and lower arm fulfilling these requirements is introduced. The proposed method is tested with the upper extremity rehabilitation robot ARMin V, but can be applied in any other actuated exoskeleton system. Experimental results were verified using EMG measurements. These results revealed that the proposed weight compensation reduces the effort of the subjects to 26% on average and more importantly throughout the entire workspace of the robot.