Experimental evaluation of a model-based assistance-as-needed paradigm using an assistive robot

In robotic rehabilitation a promising paradigm is assistance-as-needed. This is because it promotes patient active participation which is essential for neuro-rehabilitation. A model-based assistance-as-needed paradigm has been developed which utilizes a musculoskeletal model representing the subject to calculate their assistance needs. In this paper we experimentally evaluate this model-based paradigm to control an assistive robot and provide a subject with assistance-as-needed at the muscular level. A subject with impairments defined in specific muscle groups performs a number of upper limb tasks, whilst receiving assistance from a robotic exoskeleton. The paradigm is evaluated on its ability to provide assistance only as the subject needs, depending on the tasks being performed and the impairments defined. Results show that the model-based assistance-as-needed paradigm was relatively successful in providing assistance when it was needed. © 2013 IEEE

Buffer or amplifier? Longitudinal effects of social support for functional autonomy/dependence on older adults’ chronic-pain experiences

Objective: This longitudinal study aimed to investigate (a) the moderating role of formal social support for functional autonomy versus dependence on the relationship between pain intensity and pain-related disability among older adults with chronic pain and (b) the mediating role of pain-related self-efficacy and pain-related fear in this moderation. Method: One hundred and seventy older adults (Mage = 78.0; SD = 8.7) with chronic musculoskeletal pain participated in a 3-month prospective study, with 3 measurement moments. Participants filled out the Formal Social Support for Autonomy and Dependence in Pain Inventory, the Portuguese versions of the Brief Pain Inventory, the Pain Self-Efficacy Questionnaire, and the Tampa Scale of Kinesiophobia. Results: Using structural equation modeling, it was found that perceived promotion of autonomy, at Time 1, moderated the relationship between pain intensity (T1) and pain-related disability (T2); this moderation was fully mediated by pain-related self-efficacy (T2). Perceived promotion of dependence was not a significant moderator. Conclusions: These findings highlight the importance of social support for functional autonomy in buffering the impact of pain intensity on older adults’ pain-related disability. Also, they clarify the role of pain-related self-efficacy in this effect. Implications for the development of intervention programs, with formal caregivers, to reduce the impact of chronic pain on older adults’ healthy ageing process, are discussed.info:eu-repo/semantics/acceptedVersio

A subject-specific EMG-driven musculoskeletal model for applications in lower-limb rehabilitation robotics

Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator’s motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject’s joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model’s ability to predict knee joint torque with the coefficient of determination (R2) value of 0.934±0.0130.934±0.013 and the normalized root-mean-square error (RMSE) of 11.58%±1.44%11.58%±1.44%

Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings

Articular human joint modelling

Copyright @ Cambridge University Press 2009.The work reported in this paper encapsulates the theories and algorithms developed to drive the core analysis modules of the software which has been developed to model a musculoskeletal structure of anatomic joints. Due to local bone surface and contact geometry based joint kinematics, newly developed algorithms make the proposed modeller different from currently available modellers. There are many modellers that are capable of modelling gross human body motion. Nevertheless, none of the available modellers offer complete elements of joint modelling. It appears that joint modelling is an extension of their core analysis capability, which, in every case, appears to be musculoskeletal motion dynamics. It is felt that an analysis framework that is focused on human joints would have significant benefit and potential to be used in many orthopaedic applications. The local mobility of joints has a significant influence in human motion analysis, in understanding of joint loading, tissue behaviour and contact forces. However, in order to develop a bone surface based joint modeller, there are a number of major problems, from tissue idealizations to surface geometry discretization and non-linear motion analysis. This paper presents the following: (a) The physical deformation of biological tissues as linear or non-linear viscoelastic deformation, based on spring-dashpot elements. (b) The linear dynamic multibody modelling, where the linear formulation is established for small motions and is particularly useful for calculating the equilibrium position of the joint. This model can also be used for finding small motion behaviour or loading under static conditions. It also has the potential of quantifying the joint laxity. (c) The non-linear dynamic multibody modelling, where a non-matrix and algorithmic formulation is presented. The approach allows handling complex material and geometrical nonlinearity easily. (d) Shortest path algorithms for calculating soft tissue line of action geometries. The developed algorithms are based on calculating minimum ‘surface mass’ and ‘surface covariance’. An improved version of the ‘surface covariance’ algorithm is described as ‘residual covariance’. The resulting path is used to establish the direction of forces and moments acting on joints. This information is needed for linear or non-linear treatment of the joint motion. (e) The final contribution of the paper is the treatment of the collision. In the virtual world, the difficulty in analysing bodies in motion arises due to body interpenetrations. The collision algorithm proposed in the paper involves finding the shortest projected ray from one body to the other. The projection of the body is determined by the resultant forces acting on it due to soft tissue connections under tension. This enables the calculation of collision condition of non-convex objects accurately. After the initial collision detection, the analysis involves attaching special springs (stiffness only normal to the surfaces) at the ‘potentially colliding points’ and motion of bodies is recalculated. The collision algorithm incorporates the rotation as well as translation. The algorithm continues until the joint equilibrium is achieved. Finally, the results obtained based on the software are compared with experimental results obtained using cadaveric joints

Evidence-based decision support for pediatric rheumatology reduces diagnostic errors.

BACKGROUND: The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS: Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ( unaided ), versus after use of the DDSS ( aided ). RESULTS: The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p \u3c 0.0001). Improvement was greatest for emergency medicine physicians (p = 0.013) and clinicians in practice for less than 10 years (p = 0.012). This error reduction occurred despite the fact that testers employed an open book approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. CONCLUSIONS: These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists\u27 ability to evaluate and diagnose patients presenting with musculoskeletal complaints. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02205086

Adaptive Assistance With An Active And Soft Back-Support Exosuit To Unknown External Loads Via Model-Based Estimates Of Internal Lumbosacral Moments

State of the art controllers for back exoskeletons largely rely on body kinematics. This results in control strategies which cannot provide adaptive support under unknown external loads. We developed a neuromechanical model-based controller (NMBC) for a soft back exosuit, wherein assistive forces were proportional to the active component of lumbosacral joint moments, derived from real-time electromyography-driven models. The exosuit provided adaptive assistance forces with no a priori information on the external loading conditions. Across 10 participants, who stoop-lifted 5 and 15 kg boxes, our NMBC was compared to a non-adaptive virtual spring-based control(VSBC), in which exosuit forces were proportional to trunk inclination. Peak cable assistive forces were modulated across weight conditions for NMBC (5kg: 2.13 N/kg; 15kg: 2.82 N/kg) but not for VSBC (5kg: 1.92 N/kg; 15kg: 2.00 N/kg). The proposed NMBC strategy resulted in larger reduction of cumulative compression forces for 5 kg (NMBC: 18.2%; VSBC: 10.7%) and 15 kg conditions (NMBC: 21.3%; VSBC: 10.2%). Our proposed methodology may facilitate the adoption of non-hindering wearable robotics in real-life scenarios