Bone anchored amputation prostheses connect a persons amputated limb to an artificial limb. They are surgically implanted as an alternative to a prosthetic socket. This paper reports on the use of computer simulations to analyse how varying two measurements can affect stresses in the implant. These measurements were firstly, thickness of the bone wall into which the implant sits and secondly, the shape of the threads along the screw part of the implant. Varying both measurements had an effect on the implant stress and the conclusion was that this could guide future implant design optimisation.
To promote transparency and proper adherence to the scientific method, we published a protocol for the statistics we planned to conduct for our international clincial trial investigating purposeful control over the phantom limb as a treatment for phantom limb pain. The primary outcome of the study is to examine whether 15 sessions of out treatment can induce greater phantom limb pain relief, compared to a placebo treatment. The statistical analysis plan was written and published prior to reviewing the completed dataset to reduce bias when reporting the overall study results. The degree of phantom limb pain relief, in addition to other study outcomes to be examined, will provide insight into the mechanism behind phantom limb pain and its treatments, which serves to guide future developments of phantom limb pain treatments.
Fretting fatigue is a common problem for modular orthopedic implants which may lead to mechanical failure of the implant or inflammatory tissue responses due to excessive release of wear debris. Compressive residual stresses at the contacting surfaces may alleviate the problem. Here we investigate the potential of a surface enhancement method known as low plasticity burnishing (LPB) to increase the fretting fatigue resistance of bone-anchored implants for skeletal attachment of limb prostheses. Rotation bending fatigue tests performed on LPB treated and untreated test specimens demonstrate that the LPB treatment leads to statistically significantly increased resistance to fretting fatigue (LPB treated test specimens withstood on average 108,780 load cycles as compared with 37,845 load cycles for untreated test specimens, p = 0.004). LPB treated test specimens exhibited less wear at the modular interface as compared with untreated test specimens. This surface treatment may lead to reduced risk of fretting induced component failure and a reduced need for revision of implant system componentry.
Investigation of the potential of using pulsed electrical stimulation as a means to promote osseointegration in an in vitro model. Three different stimulation treatments were applied in a novel in vitro setup. The findings suggested that pulsed electrical stimulation with characteristics similar to peripheral nerve stimulation has the potential to improve cell survival and may provide a promising approach to improve implant-bone healing, particularly to neuromusculoskeletal interfaces in which implanted electrodes are readily available.
In research on lower limb prostheses, safety during testing and training is paramount. Lower limb prosthesis users risk unintentional loss of balance that can result in injury, fear of falling, and overall decreased confidence in their prosthetic leg. Here, we present a protocol for managing the risks during evaluation of active prosthetic legs with modifiable control systems. We propose graded safety levels, each of which must be achieved before advancing to the next one, from laboratory bench testing to independent ambulation in real-world environments.
The development of control algorithms and prosthetic hardware for lower limb prostheses involves an iterative testing process. Here, we present the design and validation of a bypass socket to enable able-bodied researchers to wear a leg prosthesis for evaluation purposes. The bypass socket can be made using a 3D-printer and standard household tools. It has an open-socket design that allows for electromyography recordings. It was designed for people with a height of 160 – 190 cm and extra caution should be observed with users above 80 kg. The use of a safety harness when wearing a prosthesis with the bypass socket is also recommended for additional safety.
The ability to measure functional performance of a prosthesis is hindered by the lack of an equalized mechanical platform to test from. Researchers and designers seeking to increase the pace of development have attempted novel mounts for prostheses so these can be used by able-bodied participants. Termed “bypass sockets”, these can increase the sampling pool during prosthetic evaluations. Here, we present an open-source, 3D printable prosthetic bypass socket for below-elbow (transradial) amputations. Methods to quantify the effectiveness of bypass sockets are limited and therefore we propose the use of a validated and clinically relevant evaluation tool, the Assessment of Capacity for Myoelectric Control (ACMC). We performed the ACMC in six able-bodied subjects with limited experience with myoelectric prostheses and found the participants to be rated from “non-” to “somewhat capable” using the ACMC interpretation scale. In addition, we conducted a secondary evaluation consisting of a subset of tasks of the Cybathlon competition aimed at eliciting fatigue in the participants. All participants completed said tasks, suggesting that the bypass socket is suitable for extended use during prosthesis development.
Phantom Limb Pain (PLP) is a chronic condition frequent among individuals with acquired amputation. PLP has been often investigated with the use of functional MRI focusing on the changes that take place in the sensorimotor cortex after amputation. In the present study, we investigated whether a different type of data, namely electroencephalographic (EEG) recordings, can be used to study the condition. We acquired resting state EEG data from people with and without PLP and then used machine learning for a binary classification task that differentiates the two. Common Spatial Pattern (CSP) decomposition was used as the feature extraction method and two validation schemes were followed for the classification task. Six classifiers (LDA, Log, QDA, LinearSVC, SVC and RF) were optimized through grid search and their performance compared. Two validation approaches, namely all-subjects validation and leave-one-out cross-validation (LOOCV), resulted in high classification accuracy. Most notably, the 93.7% accuracy achieved with SVC in LOOCV holds promise for good diagnostic capabilities using EEG biomarkers. In conclusion, our findings indicate that EEG data is a promising target for future research aiming at elucidating the neural mechanisms underlying PLP and its diagnosis.
Integrating tactile and kinesthetic feedback in a bionic arm results in performance closer to able-bodied individuals.
Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200–300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.