Suitable lower-limb prosthetic sockets must provide an adequate distribution of the pressures created from standing and ambulation. A systematic search for articles reporting socket pressure changes in response to device alignment perturbation was carried out, identifying 11 studies. These were then evaluated using the American Academy of Orthotists and Prosthetists guidelines for a state-of-the-science review. Each study used a design where participants acted as their own controls. Results were available for 52 individuals and five forms of alignment perturbation. Four studies were rated as having moderate internal and external validity, the remainder were considered to have low validity. Significant limitations in study design, reporting quality and in representation of results and the suitability of calculations of statistical significance were evident across articles. Despite the high inhomogeneity of study designs, moderate evidence supports repeatable changes in pressure distribution for specific induced changes in component alignment. However, there also appears to be a significant individual component to alignment responses. Future studies should aim to include greater detail in the presentation of results to better support later meta-analyses.
Traditional shoulder range of movement (ROM) measurement tools suffer from inaccuracy or from long experimental setup times. Recently, it has been demonstrated that relatively low-cost wearable inertial measurement unit (IMU) sensors can overcome many of the limitations of traditional motion tracking systems. The aim of this study is to develop and evaluate a single IMU combined with an electromyography (EMG) sensor to monitor the 3D reachable workspace with simultaneous measurement of deltoid muscle activity across the shoulder ROM. Six volunteer subjects with healthy shoulders and one participant with a ‘frozen’ shoulder were recruited to the study. Arm movement in 3D space was plotted in spherical coordinates while the relative EMG intensity of any arm position is presented graphically. The results showed that there was an average ROM surface area of 27291 ± 538 deg2 among all six healthy individuals and a ROM surface area of 13571 ± 308 deg2 for the subject with frozen shoulder. All three sections of the deltoid show greater EMG activity at higher elevation angles. Using such tools enables individuals, surgeons and physiotherapists to measure the maximum envelope of motion in conjunction with muscle activity in order to provide an objective assessment of shoulder performance in the voluntary 3D workspace. Graphical abstractThe aim of this study is to develop and evaluate a single IMU combined with an electromyography (EMG) sensor to monitor the 3D reachable workspace with simultaneous measurement of deltoid muscle activity across the shoulder ROM. The assessment tool consists of an IMU sensor, an EMG sensor, a microcontroller and a Bluetooth module. The assessment tool was attached to subjects arm. Individuals were instructed to move their arms with the elbow fully extended. They were then asked to provide the maximal voluntary elevation envelope of the arm in 3D space in multiple attempts starting from a small movement envelope going to the biggest possible in four consecutive circuits. The results showed that there was an average ROM surface area of 27291 ± 538 deg2 among all six healthy individuals and a ROM surface area of 13571 ± 308 deg2 for the subject with frozen shoulder. All three sections of the deltoid show greater EMG activity at higher elevation angles. Using such tools enables individuals, surgeons and physiotherapists to measure the maximum envelope of motion in conjunction with muscle activity in order to provide an objective assessment of shoulder performance in the voluntary 3D workspace.
Human motion tracking is widely used for assessment of movement dysfunction in orthopaedic patients. Currently, most clinical motion analysis centres use marker based three-dimensional (3D) systems as they are deemed to be the most accurate method. However, due to space, costs and logistics they are not available in many clinical settings. This study compared joint angles measured in functional tests using the novel low-cost Microsoft Kinect Perfect Phorm system with the established marker based Nexus VICON system. When measuring right and left knee flexion, the average difference between the VICON and Kinect Perfect Phorm measurement was 13.2%, with a SD of 19.6. Both overestimation and underestimation of the joint angle was recorded in different participants. Although the average percentage difference during hip abduction tests was lower at-3.9%, the range of error was far greater (SD=75). From this, it can be concluded that the level of accuracy presented in the new low cost Kinect Perfect Phorm system is not yet suitable for clinical assessments. However, for general tests of performance, and for tracking cases where absolute accuracy is less critical, future versions of this software may have a place.
Abstract-Ensemble neural networks are a commonly used as a method to boost performance of artificial intelligence applications. By collating the response of multiple networks with differences in composition or training and hence a range of estimation error, an overall improvement in the appraisal of new problem data can be made. In this work, artificial neural networks are used as an inverse-problem solver to calculate the internal distribution of pressures on a lower limb prosthetic socket using information on the deformation of the external surface of the device. Investigation into the impact of noise injection was studied by changing the maximum noise alteration parameter and the differences in network composition by altering the variance around this maximum noise value. Results indicate that use of ensembles of networks provides a meaningful improvement in overall performance. RMS error expressed as a percentage of the total applied load was 3.86% for the best performing ensemble, compared to 5.32% for the mean performance of the networks making up that ensemble. Although noise injection resulted in an improvement in typical network estimates of load distribution, ensembles performed better with low noise and low variance between network training patterns. These results mean that ensembles have been implemented in the research tool under development
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