Research Library

Mathematical models are one possible avenue for studying the working mechanisms of pain. This is a systematic review of mathematical and computational models for pain. 31 articles were identified and sorted based on their classification algorithm, data collection method, or proposal of a mathematical model.

This article discusses several existing hypotheses for the origin of phantom limb pain (PLP) and speculates on their respective implications for treatments. While seemingly contradicting, these neurophysiological models of PLP might not be mutually exclusive. All of them involve mechanisms by which an artificial limb could counteract PLP.

Phantom motor execution (PME) facilitated by augmented/virtual reality (AR/VR) and serious gaming (SG) has been proposed as a treatment for phantom limb pain (PLP). Evidence of the efficacy of this approach was obtained through a clinical trial involving individuals with chronic intractable PLP affecting the upper limb, and further evidence is currently being sought with a multi-sited, international, double blind, randomized, controlled clinical trial in upper and lower limb amputees. All experiments have been conducted in a clinical setting supervised by a therapist. Here, we present a series of case studies (two upper and two lower limb amputees) on the use of PME as a self-treatment. We explore the benefits and the challenges encountered in translation from clinic to home use with a holistic, mixed-methods approach, employing both quantitative and qualitative methods from engineering, medical anthropology, and user interface design. All patients were provided with and trained to use a myoelectric pattern recognition and AR/VR device for PME. Patients took these devices home and used them independently over 12 months. We found that patients were capable of conducting PME as a self-treatment and incorporated the device into their daily life routines. Use patterns and adherence to PME practice were not only driven by the presence of PLP but also influenced by patients’ perceived need and social context. The main barriers to therapy adherence were time and availability of single-use electrodes, both of which could be resolved, or attenuated, by informed design considerations. Our findings suggest that adherence to treatment, and thus related outcomes, could be further improved by considering disparate user types and their utilization patterns. Our study highlights the importance of understanding, from multiple disciplinary angles, the tight coupling and interplay between pain, perceived need, and use of medical devices in patient-initiated therapy.

Textile-based electrodes can be used to measure the electrical signals from muscles which can be used for diagnosis, monitoring and treatment. We reviewed articles describing applications of measuring the electrical signals from muscles using textile electrodes and summarised it. Based on the 41 articles we read, we introduce four textile integration levels to describe the various applications.

The underlying mechanism of phantom limb pain (PLP) is poorly understood. This article discusses PLP in relation to available clinical findings. An alternative hypothesis for PLP is proposed: that the disruption following an amputation result in the pain system becoming coactivated with the sensorimotor system belonging to the missing limb. An implication of this hypothesis is that training of phantom movements through phantom motor execution could disentangle the motor system from pain, resulting in a reduction of PLP.

Phantom limb pain (PLP) is a chronic condition that can greatly diminish quality of life. Control over the phantom limb and exercise of such control have been hypothesised to reverse maladaptive brain changes correlated to PLP. Preliminary investigations have shown that decoding motor volition using myoelectric pattern recognition, while providing real-time feedback via virtual and augmented reality (VR-AR), facilitates phantom motor execution (PME) and reduces PLP. Here we present the study protocol for an international (seven countries), multicentre (nine clinics), double-blind, randomised controlled clinical trial to assess the effectiveness of PME in alleviating PLP. Sixty-seven subjects suffering from PLP in upper or lower limbs are randomly assigned to PME or phantom motor imagery (PMI) interventions. Subjects allocated to either treatment receive 15 interventions and are exposed to the same VR-AR environments using the same device. The only difference between interventions is whether phantom movements are actually performed (PME) or just imagined (PMI). Complete evaluations are conducted at baseline and at intervention completion, as well as 1, 3 and 6 months later using an intention-to-treat (ITT) approach. Changes in PLP measured using the Pain Rating Index between the first and last session are the primary measure of efficacy. Secondary outcomes include: frequency, duration, quality of pain, intrusion of pain in activities of daily living and sleep, disability associated to pain, pain self-efficacy, frequency of depressed mood, presence of catastrophising thinking, health- related quality of life and clinically significant change as patient’s own impression. Follow-up interviews are conducted up to 6 months after the treatment.

Real-time evaluation of novel prosthetic control schemes is critical for translational research on artificial limbs. Recently, two computer-based, real-time evaluation tools, the target achievement control (TAC) test and the Fitts’ law test (FLT), have been proposed to assess real-time controllability. Whereas TAC tests provides an anthropomorphic visual representation of the limb at the cost of confusing visual feedback, FLT clarifies the current and target locations by simplified non-anthropomorphic representations. Here, we investigated these two approaches and quantied differences in common performance metrics that can result from the chosen method of visual feedback. Ten able-bodied and one amputee subject performed target achievement tasks corresponding to the FLT and TAC test with equivalent indices of difficulty. Ablebodied subjects exhibited significantly (p <0.05) better completion rate, path efficiency, and overshoot when performing the FLT, although no significant difference was seen in throughput performance. The amputee subject showed significantly better performance in overshoot at the FLT, but showed no significant difference in completion rate, path efficiency, and throughput. Results from the FLT showed a strong linear relationship between the movement time and the index of difficulty (R2 D 0:96), whereas TAC test results showed no apparent linear relationship (R2 D 0:19). These results suggest that in relatively similar conditions, the confusing location of virtual limb representation used in the TAC test contributed to poorer performance. Establishing an understanding of the biases of various evaluation protocols is critical to the translation of research into clinical practice.

When measuring electric signals from the muscles of the leg to treat phantom limb pain, the placement of the electrodes which measures the signal is important, but it is not known how to place the electrodes for getting the best signals for analysing the electric signals with artificial intelligence algorithms. We tested different electrodes placements, such as putting two electrodes on a specific muscle compared to putting one electrode on several untargeted muscles. The best placement was putting one electrode on several untargeted muscles and we presented a case study who had a 68% reduction in pain after 23 sessions. Removing the need to target the muscles means that electrode placement will be easier which can make it easier to perform phantom limb pain treatments measuring electric signals from the muscles of the leg.

Myoelectric pattern recognition (MPR) can be used for intuitive control of virtual and robotic effectors in clinical applications such as prosthetic limbs and the treatment of phantom limb pain. The conventional approach is to feed classifiers with descriptive electromyographic (EMG) features that represent the aimed movements. The complexity and consequently classification accuracy of MPR is highly affected by the separability of such features. In this study, classification complexity estimating algorithms were investigated as a potential tool to estimate MPR performance. An early prediction of MPR accuracy could inform the user of faulty data acquisition, as well as suggest the repetition or elimination of detrimental movements in the repository of classes. Two such algorithms, Nearest Neighbor Separability (NNS) and Separability Index (SI), were found to be highly correlated with classification accuracy in three commonly used classifiers for MPR (Linear Discriminant Analysis, Multi-Layer Perceptron, and Support Vector Machine). These Classification Complexity Estimating Algorithms (CCEAs) were implemented in the open source software BioPatRec and are available freely online. This work deepens the understanding of the complexity of MPR for the prediction of motor volition.

Phantom limb pain is a condition where pain is felt in a missing limb, for example after amputation. In this study patients received a treatment where machine learning and augmented reality was used to visualize and improve their ability to move their phantom limb. Patients showed significant improvements of their pain, suggesting potential value in phantom motor execution as a treatment for phantom limb pain.