Development and Testing of a Soft Exoskeleton Robotic Hand Training Device.

Hand-function recovery is often a goal for stroke survivors undergoing therapy. This work aimed to design, build, and verify a pneumatic hand training device for its eventual use in post-stroke rehabilitation. The system was built considering prior research in the field of robotic hand rehabilitation as well as specifications and design constraints developed with physiotherapists. The system contained pneumatic airbag actuators for the fingers and thumb of the hand, a set of flex, pressure, and flow sensors, and software and hardware controls. An experiment with the system was carried out on 30 healthy individuals. The sensor readings were analyzed for repeatability and reliability. Position sensors and an approximate biomechanical model of the index finger were used to estimate joint angles during operation. A survey was also issued to the users to evaluate their comfort levels with the device. It was found that the system was safe and comfortable when moving the fingers of the hand into an extension.

Initial Testing of Robotic Exoskeleton Hand Device for Stroke Rehabilitation.

The preliminary test results of a novel robotic hand rehabilitation device aimed at treatment for the loss of motor abilities in the fingers and thumb due to stroke are presented. This device has been developed in collaboration with physiotherapists who regularly treat individuals who have suffered from a stroke. The device was tested on healthy adults to ensure comfort, user accessibility, and repeatability for various hand sizes in preparation for obtaining permission from regulatory bodies and implementing the design in a full clinical trial. Trials were conducted with 52 healthy individuals ranging in age from 19 to 93 with an average age of 58. A comfort survey and force data ANOVA were performed to measure hand motions and ensure the repeatability and accessibility of the system. Readings from the force sensor (p < 0.05) showed no significant difference between repetitions for each participant. All subjects considered the device comfortable. The device scored a mean comfort value of 8.5/10 on all comfort surveys and received the approval of all physiotherapists involved. The device has satisfied all design specifications, and the positive results of the participants suggest that it can be considered safe and reliable. It can therefore be moved forward for clinical trials with post-stroke users.

Modeling contact forces during human-robot interactions for performing activities of daily living.

This study aims to characterize contact forces between humans and tools during activities of daily living (ADL) to provide information to a personal care robot. The study was conducted on non-impaired subjects to capture various static and dynamic force levels when making contact with three different robotic tools, each designed to perform an ADL task: hair brushing, face wiping, and face shaving. The static trial of the study involved 21 participants. Forces were collected at fixed locations for each task and used to develop models for each participant. Extraction of the maximum force levels was performed for both the maximum and desired levels of force. The dynamic trial involved 24 individuals. Participants were asked to maintain a comfortable level of force for the duration of their contact with the tool as the robot moved along its path to perform the ADL task. For the static and dynamic trials, higher forces were observed during hair brushing compared to the other two tasks. It was observed that the hair brushing task force at a specific contact point has an overall maximum of 55.66 N, while the maximum forces detected in the face wiping and face shaving tasks were 36.40 and 11.11 N, respectively. The forces collected were analyzed, and no trends were found relating the contact forces to the gender, height, or weight of the subjects. Based on the analysis of the results, recommendations have been made to enhance the force safety limits for the personal care robot working environment.

A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal.

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesion from five Macaca fasicularis monkeys. The proposed classifier is based on a CNN using filtered segmented EMG signals from the pre- and post-lesion periods as inputs, while the kNN is designed using four hand-crafted EMG features. The results suggest that the CNN provides a promising classification technique for TSCI, compared to conventional machine learning classification. The kNN with hand-crafted EMG features classified the pre- and post-lesion EMG data with an F-measure of 89.7% and 92.7% for the left- and right-side muscles, respectively, while the CNN with the EMG segments classified the data with an F-measure of 89.8% and 96.9% for the left- and right-side muscles, respectively. Finally, the proposed deep learning classification model (CNN), with its learning ability of high-level features using EMG segments as inputs, shows high potential and promising results for use as a TSCI classification system. Future studies can confirm this finding by considering more subjects.

Upper limb robotic assessment: Pilot study comparing velocity dependent resistance in individuals with acquired brain injury to healthy controls.

INTRODUCTION: Assessment of velocity dependent resistance (VDR) can provide insights into spasticity in individuals with upper motor neuron syndrome. This study investigates the relationship between Modified Ashworth scores and a biomechanical based representation of VDR using a rehabilitation robot. Comparisons in VDR are made for the upper limb (UL) between individuals with acquired brain injury and healthy controls for the para-sagittal plane. METHODS: The system manipulates the individual's limb through five flexion and extension motions at increasing speeds to obtain force profiles at different velocities. An approximation of VDR is calculated and analyzed statistically against clinical scales and tested for interactions. RESULTS: All individuals (aged 18-65), including healthy controls exhibited VDR greater than 0 (P < 0.05). MAS scores were found to be related to VDR (P < 0.05) with an interaction found between MAS Bicep and Tricep scores (P < 0.01). Considering this interaction, evidence of differences in VDR were found between several neighboring assessment score combinations. CONCLUSION: The robot can detect and quantify VDR that captures information relevant to UL spasticity. Results suggests a better categorization of VDR is possible and supports further development of rehabilitation robotics for assisting spasticity assessment.

Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury.

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental stages (pre-and post-lesion) using electromyography signals. Eight time-domain features were extracted from the collected electromyography data. To overcome the imbalanced dataset issue, synthetic minority oversampling technique was applied. Different ML classification techniques were applied including multilayer perceptron, support vector machine, K-nearest neighbors, and radial basis function network; then their performances were compared. A confusion matrix and five other statistical metrics (sensitivity, specificity, precision, accuracy, and F-measure) were used to evaluate the performance of the generated classifiers. The results showed that the best classifier for the left- and right-side data is the multilayer perceptron with a total F-measure of 79.5% and 86.0% for the left and right sides, respectively. This work will help to build a reliable classifier that can differentiate between these two phases by utilizing some extracted time-domain electromyography features.

Transcatheter closure of sinus venosus atrial septal defect with anomalous pulmonary venous drainage: Innovative technique with long-term follow-up.

INTRODUCTION: Surgical closure of sinus venosus atrial septal defect (SVASD) is the standard management. A safe and effective transcatheter approach will be an attractive option. OBJECTIVES: To assess the feasibility and long-term safety of transcatheter closure of SVASD with anomalous pulmonary venous drainage. PATIENTS AND METHODS: From July 2011 to October 2013, four patients with large SVASD and anomalous right upper pulmonary venous (RUPV) drainage underwent transcatheter closure of their defects at Ibn-Albitar Center for Cardiac Surgery, Baghdad, Iraq. Two patients with superior vena cava (SVC)-type SVASD underwent closure using covered Cheatham-Platinum (CP) stents with no need for septal occluder. The other two patients had large RA-type SVASD who underwent closure using covered CP stents only in one patient and stents and device in the other one. An angiogram in the RUPV during balloon inflation in the SVC was done to ensure that the RUPV drains back to the left atrium. The covered CP stent was mounted and hand crimped onto Z-Med™ or BIB-balloon catheters and deployed in the desired location under transesophageal echocardiography guidance. RESULTS: The two patients with SVC-type SVASD underwent successful closure using two overlapping covered CP stents implanted in the SVC, thus creating total septation between the SVC and the RUPV. The RA-type SVASD patients underwent closure using two overlapping covered CP stents. One with mild to moderate residual shunt that completely disappeared at 12 months follow-up after implantation of a second 45 mm CP stent. A significant residual shunt in the second patient was closed successfully using a PFO device. CONCLUSION: Transcatheter closure of SVASD through SVC stent insertion with or without subsequent device implantation is feasible and effective.

Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes.

This study aims to characterize traumatic spinal cord injury (TSCI) neurophysiologically using an intramuscular fine-wire electromyography (EMG) electrode pair. EMG data were collected from an agonist-antagonist pair of tail muscles of Macaca fasicularis, pre- and post-lesion, and for a treatment and control group. The EMG signals were decomposed into multi-resolution subsets using wavelet transforms (WT), then the relative power (RP) was calculated for each individual reconstructed EMG sub-band. Linear mixed models were developed to test three hypotheses: (i) asymmetrical volitional activity of left and right side tail muscles (ii) the effect of the experimental TSCI on the frequency content of the EMG signal, (iii) and the effect of an experimental treatment. The results from the electrode pair data suggested that there is asymmetry in the EMG response of the left and right side muscles (p-value < 0.001). This is consistent with the construct of limb dominance. The results also suggest that the lesion resulted in clear changes in the EMG frequency distribution in the post-lesion period with a significant increment in the low-frequency sub-bands (D4, D6, and A6) of the left and right side, also a significant reduction in the high-frequency sub-bands (D1 and D2) of the right side (p-value < 0.001). The preliminary results suggest that using the RP of the EMG data, the fine-wire intramuscular EMG electrode pair are a suitable method of monitoring and measuring treatment effects of experimental treatments for spinal cord injury (SCI).

Evaluation of some mechanical properties of a new silicone elastomer for maxillofacial prostheses after addition of intrinsic pigments.

OBJECTIVE: The approximate life span of a silicone maxillofacial prosthesis is as short as 1.5-2 years of clinical service, then a new prosthesis should be fabricated. The most common reason for re-making the prosthesis is silicone mechanical properties degradation. The aim of this study was to assess some mechanical properties of VST-30 silicone for maxillofacial prostheses after addition of intrinsic pigments. METHODS: Two types of intrinsic pigments (rayon flocking and burnt sienna); each of them was incorporated into silicone. One hundred and twenty samples were prepared and split into 4 groups according to the conducted tests (tear strength, hardness, surface roughness, and tensile strength and elongation percentage) with 30 samples for each test. Each group was equally split into three subgroups. Group (A) was without pigment (control group), group (B) was with rayon flocking and group (C) was with burnt sienna. RESULTS: Samples with rayon flocking showed a highly significant decrease in hardness and there was a significant increase in tear strength, while there were non-significant differences in surface roughness, tensile strength and elongation percentage. Samples with burnt sienna showed a highly significant increase in tear strength and a highly significant decrease in hardness, but surface roughness, tensile strength and elongation percentage showed non-significant differences. However, there were non-significant differences between experimental groups in all tests. CONCLUSIONS: The addition of each of rayon flocking and burnt sienna changed the mechanical properties of the VST-30 silicone, while no superior pigment-silicone combination was revealed in all the conducted tests.

Model of Traumatic Spinal Cord Injury for Evaluating Pharmacologic Treatments in Cynomolgus Macaques (Macaca fasicularis).

Here we present the results of experiments involving cynomolgus macaques, in which a model of traumatic spinal cord injury (TSCI) was created by using a balloon catheter inserted into the epidural space. Prior to the creation of the lesion, we inserted an EMG recording device to facilitate measurement of tail movement and muscle activity before and after TSCI. This model is unique in that the impairment is limited to the tail: the subjects do not experience limb weakness, bladder impairment, or bowel dysfunction. In addition, 4 of the 6 subjects received a combination treatment comprising thyrotropin releasing hormone, selenium, and vitamin E after induction of experimental TSCI. The subjects tolerated the implantation of the recording device and did not experience adverse effects due the medications administered. The EMG data were transformed into a metric of volitional tail moment, which appeared to be valid measure of initial impairment and subsequent natural or treatment-related recovery. The histopathologic assessment demonstrated widespread axon loss at the site of injury and areas cephalad and caudad. Histopathology revealed evidence of continuing inflammation, with macrophage activation. The EMG data did not demonstrate evidence of a statistically significant treatment effect.

Transverse forces versus modified ashworth scale for upper limb flexion/extension in para-sagittal plane.

Spasticity is a common impairment following an upper motor neuron lesion in conditions such as stroke and brain injury. A clinical issue is how to best quantify and measure spasticity. Recently, research has been performed to develop new methods of spasticity quantification using various systems. This paper follows up on previous work taking a closer look at the role of transversal forces obtained via rehabilitation robot for motions in the para-sagittal plane. Results from 45 healthy individuals and 40 individuals with acquired brain injury demonstrate that although the passive upper motions are vertical, horizontal forces into and away from the individual's body demonstrate a relationship with the Modified Ashworth Scale. This finding leads the way to new avenues of spasticity quantification and monitoring.

Robotic pilot study for analysing spasticity: clinical data versus healthy controls.

BACKGROUND: Spasticity is a motor disorder that causes significant disability and impairs function. There are no definitive parameters that assess spasticity and there is no universally accepted definition. Spasticity evaluation is important in determining stages of recovery. It can determine treatment effectiveness as well as how treatment should proceed. This paper presents a novel cross sectional robotic pilot study for the primary purpose of assessment. The system collects force and position data to quantify spasticity through similar motions of the Modified Ashworth Scale (MAS) assessment in the Sagittal plane. Validity of the system is determined based on its ability to measure velocity dependent resistance. METHODS: Forty individuals with Acquired Brain Injury (ABI) and 45 healthy individuals participated in a robotic pilot study. A linear regression model was applied to determine the effect an ABI has on force data obtained through the robotic system in an effort to validate it. Parameters from the model were compared for both groups. Two techniques were performed in an attempt to classify between healthy and patients. Dynamic Time Warping (DTW) with k-nearest neighbour (KNN) classification is compared to a time-series algorithm using position and force data in a linear discriminant analysis (LDA). RESULTS: The system is capable of detecting a velocity dependent resistance (p<0.05). Differences were found between healthy individuals and those with MAS 0 who are considered to be healthy. DTW with KNN is shown to improve classification between healthy and patients by approximately 20 % compared to that of an LDA. CONCLUSIONS: Quantitative methods of spasticity evaluation demonstrate that differences can be observed between healthy individuals and those with MAS of 0 who are often clinically considered to be healthy. Exploiting the time-series nature of the collected data demonstrates that position and force together are an accurate predictor of patient health.

Results of clinicians using a therapeutic robotic system in an inpatient stroke rehabilitation unit.

BACKGROUND: Physical rehabilitation is an area where robotics could contribute significantly to improved motor return for individuals following a stroke. This paper presents the results of a preliminary randomized controlled trial (RCT) of a robot system used in the rehabilitation of the paretic arm following a stroke. METHODS: The study's objectives were to explore the efficacy of this new type of robotic therapy as compared to standard physiotherapy treatment in treating the post-stroke arm; to evaluate client satisfaction with the proposed robotic system; and to provide data for sample size calculations for a proposed larger multicenter RCT. Twenty clients admitted to an inpatient stroke rehabilitation unit were randomly allocated to one of two groups, an experimental (robotic arm therapy) group or a control group (conventional therapy). An occupational therapist blinded to patient allocation administered two reliable measures, the Chedoke Arm and Hand Activity Inventory (CAHAI-7) and the Chedoke McMaster Stroke Assessment of the Arm and Hand (CMSA) at admission and discharge. For both groups, at admission, the CMSA motor impairment stage of the affected arm was between 1 and 3. RESULTS: Data were compared to determine the effectiveness of robot-assisted versus conventional therapy treatments. At the functional level, both groups performed well, with improvement in scores on the CAHAI-7 showing clinical and statistical significance. The CAHAI-7 (range7-49) is a measure of motor performance using functional items. Individuals in the robotic therapy group, on average, improved by 62% (95% CI: 26% to 107%) while those in the conventional therapy group changed by 30% (95% CI: 4% to 61%). Although performance on this measure is influenced by hand recovery, our results showed that both groups had similar stages of motor impairment in the hand. Furthermore, the degree of shoulder pain, as measured by the CMSA pain inventory scale, did not worsen for either group over the course of treatment. CONCLUSION: Our findings indicated that robotic arm therapy alone, without additional physical therapy interventions tailored to the paretic arm, was as effective as standard physiotherapy treatment for all responses and more effective than conventional treatment for the CMSA Arm (p = 0.04) and Hand (p = 0.04). At the functional level, both groups performed equally well.

Dynamic biomechanical model for assessing and monitoring robot-assisted upper-limb therapy.

This article describes the design, validation, and application of a dynamic biomechanical model that assesses and monitors trajectory, position, orientation, force, and torque generated by upper-limb (UL) movement during robot-assisted therapy. The model consists of two links that represent the upper arm and forearm, with 5 degrees of freedom (DOF) for the shoulder and elbow joints. The model is a useful tool for enhancing the functionality of poststroke robot-assisted UL therapy. The individualized inertial segment parameters were based on anthropometric measurements. The model performed inverse dynamic analysis of UL movements to calculate reaction forces and moments acting about the 3-DOF shoulder and 2-DOF elbow joints. Real-time fused biofeedback of a 6-DOF force sensor and three-dimensional (3-D) pose sensors supported the model validation and application. The force sensor was mounted between the robot manipulator and the subject's wrist, while the 3-D pose sensors were fixed at specific positions on the subject's UL segments. The model input and output parameters were stored in the subject's database, which is part of the rehabilitation information system. We assigned 20 nondisabled subjects three different therapy exercises to test and validate the biomechanical model. We found that when the biomechanical model is taught an exercise, it can accurately predict a subject's actual UL joint angles and torques and confirm that the exercise is isolating the desired movement.

Mathematical modeling of laser based potato cutting and peeling.

A mathematical model is developed and validated to predict the depth of cut in potato tuber slabs as a function of laser power and travel speed. The model considers laser processing parameters such as input power, spot size and exposure time as well as the properties of the material being cut such as specific heat, thermal conductivity, surface reflectance, etc. The model also considers the phase change of water in potato and the ignition temperature of the solid portion. The composition of the potato tuber is assumed to be of water and solid. The model also assumes that the ablation process is accomplished through ejection of liquid water, debris and water vapour, and combustion of solid. A CO(2) laser operating in c.w. mode was chosen for the experimental work because water absorbs laser energy highly at 10.6 microm, and CO(2) laser units with relatively high output power are available. Slabs of potato tuber were chosen to be laser processed since potato contains high moisture and large amounts of relatively homogeneous tissue. The results of the preliminary calculations and experiments concluded that the model is able to predict the depth of cut in potato tuber parenchyma when subjected to a CO(2) laser beam.

A neural network model for reconstructing EMG signals from eight shoulder muscles: consequences for rehabilitation robotics and biofeedback.

This paper demonstrates the ability of a fully connected feed forward neural network (FFNN) using the backpropagation training algorithm to predict the electromyography (EMG) signal from eight muscles of the shoulder for both exercises not used for training and EMG signals from subjects not used for training. The network showed a good predictive ability for subjects not used for training (r(2) between 0.33 and 0.84) and for activities not used for training (r(2) between 0.56 and 0.89). This may have applications for patients, physical therapists and doctors to monitor patient performance by reviewing the level of agreement between the patient EMG and the predicted EMG. Coupled with traditional methods of evaluation, EMG can provide an excellent measure of how well a patient has responded or is responding to treatment. Incorporating robotic technology could facilitate the use of EMG as an input to an intelligent decision making algorithm used to increase or decrease the level of difficulty according to patient performance.