56 research outputs found
Dataset for paper "Technology for monitoring everyday prosthesis use: a systematic review"
No full textThis dataset supports the publication:
A Chadwell, L Diment, M E Mico-Amigo, D Z Morgado Ramirez, A Dickinson, M Granat, L Kenney, S Kheng, M Sobuh, R Ssekitoleko, P Worsley. (2020) Technology for monitoring everyday prosthesis use: a systematic review. Journal of NeuroEngineering and Rehabilitation. DOI: https://doi.org/10.1186/s12984-020-00711</span
A machine learning classification model for monitoring the daily physical behaviour of lower-limb amputees
No full textThere are currently limited data on how prosthetic devices are used to support lower-limb prosthesis users in their free-living environment. Possessing the ability to monitor a patient's physical behaviour while using these devices would enhance our understanding of the impact of different prosthetic products. The current approaches for monitoring human physical behaviour use a single thigh or wrist-worn accelerometer, but in a lower-limb amputee population, we have the unique opportunity to embed a device within the prosthesis, eliminating compliance issues. This study aimed to develop a model capable of accurately classifying postures (sitting, standing, stepping, and lying) by using data from a single shank-worn accelerometer. Free-living posture data were collected from 14 anatomically intact participants and one amputee over three days. A thigh worn activity monitor collected labelled posture data, while a shank worn accelerometer collected 3-axis acceleration data. Postures and the corresponding shank accelerations were extracted in window lengths of 5-180 s and used to train several machine learning classifiers which were assessed by using stratified cross-validation. A random forest classifier with a 15 s window length provided the highest classification accuracy of 93% weighted average F-score and between 88 and 98% classification accuracy across all four posture classes, which is the best performance achieved to date with a shank-worn device. The results of this study show that data from a single shank-worn accelerometer with a machine learning classification model can be used to accurately identify postures that make up an individual's daily physical behaviour. This opens up the possibility of embedding an accelerometer-based activity monitor into the shank component of a prosthesis to capture physical behaviour information in both above and below-knee amputees. The models and software used in this study have been made open source in order to overcome the current restrictions of applying activity monitoring methods to lower-limb prosthesis users.</p
Characterising residual limb morphology and prosthetic socket design based on expert clinician practice
No full textFunctional, comfortable prosthetic limbs depend on personalised sockets, currently designed using an iterative, expert-led process, which can be expensive and inconvenient. Computer aided design and manufacturing (CAD/CAM) offers enhanced repeatability, but far more use could be made from clinicians’ extensive digital design records. Knowledge-based socket design using smart templates could collate successful design features and tailor them to a new patient. Based on 67 residual limb scans and corresponding sockets, this paper develops a method of objectively analysing personalised design approaches by expert prosthetists, using machine learning: prin-cipal component analysis (PCA) to extract key categories in anatomic and surgical variation, and K-Means Clustering to identify local ‘rectification’ design features. Rectification patterns repre-senting Total Surface Bearing and Patella Tendon Bearing design philosophies are identified au-tomatically by PCA, which reveals trends in socket design choice for different limb shapes that match clinical guidelines. Expert design practice is quantified by measuring the size of local rec-tifications identified by k-means clustering. Implementing smart templates based on these trends requires clinical assessment by prosthetists and does not substitute training. This study provides methods for population-based socket design analysis, and example data, which will support de-velopments in CAD/CAM clinical practice and accuracy of biomechanics research
How do activity, prosthetic fit, comfort and community participation correlate in lower limb prosthesis users? A Cambodian pilot study
No full textDataset for paper "Characterising Residual Limb Morphology and Prosthetic Socket Design based on Expert Clinician Practice"
No full textDataset for article in "Prosthesis" titled "Characterising Residual Limb Morphology and Prosthetic Socket Design based on Expert Clinician Practice" in Prothesis, https://doi.org/10.31224/osf.io/nxwh4</span
An evaluation of technology adoption: using 3D scanners and accelerometers in a Cambodian prosthetics clinic
No full textDigital technologies can provide information to support the conventional methods used to develop prosthetics. However,there is little research into which technologies are useful for prosthetists and prosthesis-users, particularly in low resourced countries
Dataset for publication 'Activity, socket fit, comfort and community participation in lower limb prosthesis-users: A Cambodian cohort study'
No full textThis dataset contains: An Excel spreadsheet containing the raw data behind the figures in the linked journal paper. Activity, socket fit, comfort and community participation in lower limb prosthesis-users: A Cambodian cohort study' in Journal of NeuroEngineering and Rehabilitation
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Dataset for publication "Insights into the spectrum of transtibial prosthetic socket design from expert clinicians and their digital records"
No full textThis dataset contains: A simple .csv file containing the raw data behind the figures in the linked journal paper.
The keys for the Excel sheets are as follows:
- Figure 2:
Raw data behind histograms showing relative size distributions of local and gross rectifications for prosthetic limbs designed nominally to patellar tendon bearing (PTB) and total surface bearing (TSB) philosophies.
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Flat-pack, rapid production powered air purifying respirators for Low-Resource Settings amid the SARS-CoV-2 pandemic
No full textHealthcare workers are at high risk of catching SARS-CoV-2 because of their regular interaction with patients with the disease. In low-resource settings, the ratio of healthcare workers to the whole population is lower than in high income countries, and there is often limited access to personal protective equipment (PPE). Illness or death of healthcare workers will, therefore, have a disproportionate impact in these settings, so it is particularly important to find ways to protect them. To protect against airborne infection in healthcare settings, PPE recommendations typically include filtering facemask respirators or powered air purifying respirators (PAPR). The former, passively filter inhaled air. They are small, noiseless and do not require a power supply, but they are single-use, presenting manufacturing and supply issues. Fit testing is crucial, and many users find them difficult to tolerate, due to breathing resistance and elevated humidity. There is also the potential for contamination due to the exposed face. PAPRs are re-usable devices that may last for months and provide airflow through a filter from a battery-powered blower unit to a hood or helmet which covers the face. This creates a positive pressure in the hood or helmet that enables the wearer to breathe filtered air easily, without requiring an air-tight fit needed for standard face masks. This is reported to be more comfortable and provides better protection for the face from droplets and splashes, and infection by self-contact with the hands. PAPRs have typically been expensive, bulky and not readily available or easy to ship to low-resource settings. The aim of this paper is to design, develop and provide open-source instructions for building a low-cost, reusable PAPR. The design is novel because it uses readily available materials and manufacturing processes, and it may be shipped flat-packed and easily assembled. This offers an option for manufacturing in low-resource settings and for shipping in bulk. This paper provides the CAD designs that can be fabricated using a laser cutter
A Personal respirator to improve protection for healthcare workers treating COVID-19 (PeRSo)
No full textIntroduction: SARS-CoV-2 infection is a global pandemic. Personal Protective Equipment (PPE) to protect healthcare workers has been a recurrent challenge in terms of global stocks, supply logistics and suitability. Around 20% of healthcare workers treating COVID-19 cases have become infected, which leads to staff absence at peaks of the pandemic, and in some cases mortality.Methods: To address current shortcomings in PPE, we developed a simple powered air purifying respirator, made from inexpensive and widely available components. The prototype was designed to minimise manufacturing complexity so that derivative versions could be developed in low resource settings with minor modification.Results: The “Personal Respirator – Southampton” (PeRSo) delivers High-Efficiency Particulate Air (HEPA) filtered air from a battery powered fan-filter assembly into a lightweight hood with a clear visor that can be comfortably worn for several hours. Validation testing demonstrates that the prototype removes microbes, avoids excessive CO2 build-up in normal use, and passes fit test protocols widely used to evaluate standard N95/FFP2 and N99/FFP3 face masks. Feedback from doctors and nurses indicate the PeRSo prototype was preferred to standard FFP2 and FFP3 masks, being more comfortable and reducing the time and risk of recurrently changing PPE. Patients report better communication and reassurance as the entire face is visible. Conclusion: Rapid upscale of production of cheaply produced powered air purifying respirators, designed to achieve regulatory approval in the country of production, could protect healthcare workers from infection and improve healthcare delivery during the COVID-19 pandemic
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