scholarly journals A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7458
Author(s):  
Benjamin Griffiths ◽  
Laura Diment ◽  
Malcolm H. Granat
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Classification Accuracy ◽  
Activity Monitor ◽  
Classification Model ◽  
Free Living ◽  
Machine Learning Classification ◽  
Physical Behaviour ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis

There 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.

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

scholarly journals Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

2021 ◽  
Vol 13 (11) ◽  
pp. 6376
Author(s):  
Junseo Bae ◽  
Sang-Guk Yum ◽  
Ji-Myong Kim
Keyword(s):  
Machine Learning ◽  
Financial Management ◽  
Construction Projects ◽  
Transportation Infrastructure ◽  
Classification Model ◽  
Insurance Companies ◽  
Economic Damage ◽  
Infrastructure Projects ◽  
Machine Learning Classification ◽  
Financial Losses

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.

Nonlinear Passive Cam-Based Springs for Powered Ankle Prostheses

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Jonathan Realmuto ◽  
Glenn Klute ◽  
Santosh Devasia
Keyword(s):  
Lower Limb ◽  
Nonlinear Response ◽  
Experimental Results ◽  
Passive Element ◽  
Ankle Torque ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis ◽  
Elastic Elements

This article studies the design of passive elastic elements to reduce the actuator requirements for powered ankle prostheses. The challenge is to achieve most of the typically nonlinear ankle response with the passive element so that the active ankle-torque from the actuator can be small. The main contribution of this article is the design of a cam-based lower-limb prosthesis to achieve such a nonlinear ankle response. Results are presented to show that the addition of the cam-based passive element can reduce the peak actuator torque requirement substantially, by ∼74%. Moreover, experimental results are presented to demonstrate that the cam-based design can achieve a desired nonlinear response to within 10%.

Reduced cortical brain activity with the use of microprocessor-controlled prosthetic knees during walking

2018 ◽  
Vol 43 (3) ◽  
pp. 257-265 ◽  
Author(s):  
Saffran Möller ◽  
David Rusaw ◽  
Kerstin Hagberg ◽  
Nerrolyn Ramstrand
Keyword(s):  
Cognitive Processes ◽  
Lower Limb ◽  
Brain Activity ◽  
Control Group ◽  
Healthy Controls ◽  
Prosthetic Knee ◽  
Level Walking ◽  
Lower Limb Prosthesis ◽  
Prosthetic Knees ◽  
Limb Prosthesis

Background: Individuals using a lower-limb prosthesis indicate that they need to concentrate on every step they take. Despite self-reports of increased cognitive demand, there is limited understanding of the link between cognitive processes and walking when using a lower-limb prosthesis. Objective: The objective was to assess cortical brain activity during level walking in individuals using different prosthetic knee components and compare them to healthy controls. It was hypothesized that the least activity would be observed in the healthy control group, followed by individuals using a microprocessor-controlled prosthetic knee and finally individuals using a non-microprocessor-controlled prosthetic knee. Study design: Cross-sectional study. Methods: An optical brain imaging system was used to measure relative changes in concentration of oxygenated and de-oxygenated haemoglobin in the frontal and motor cortices during level walking. The number of steps and time to walk 10 m was also recorded. The 6-min walk test was assessed as a measure of functional capacity. Results: Individuals with a transfemoral or knee-disarticulation amputation, using non-microprocessor-controlled prosthetic knee ( n = 14) or microprocessor-controlled prosthetic knee ( n = 15) joints and healthy controls ( n = 16) participated in the study. A significant increase was observed in cortical brain activity of individuals walking with a non-microprocessor-controlled prosthetic knee when compared to healthy controls ( p < 0.05) and individuals walking with an microprocessor-controlled prosthetic knee joint ( p < 0.05). Conclusion: Individuals walking with a non-microprocessor-controlled prosthetic knee demonstrated an increase in cortical brain activity compared to healthy individuals. Use of a microprocessor-controlled prosthetic knee was associated with less cortical brain activity than use of a non-microprocessor-controlled prosthetic knee. Clinical relevance Increased understanding of cognitive processes underlying walking when using different types of prosthetic knees can help to optimize selection of prosthetic components and provide an opportunity to enhance functioning with a prosthesis.

scholarly journals Frequency and Circumstances of Falls Reported by Ambulatory Unilateral Lower Limb Prosthesis Users: A Secondary Analysis

PM&R ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 344-353 ◽  
Author(s):  
Janis Kim ◽  
Matthew J. Major ◽  
Brian Hafner ◽  
Andrew Sawers
Keyword(s):  
Lower Limb ◽  
Secondary Analysis ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis

Poster 1: Quality of Life and Satisfaction are Strongly Related to Mobility for Patients with a Lower Limb Prosthesis

PM&R ◽  
2018 ◽  
Vol 10 ◽  
pp. S1-S1
Author(s):  
Shane R. Wurdeman ◽  
Phillip M. Stevens ◽  
James H. Campbell
Keyword(s):  
Quality Of Life ◽  
Lower Limb ◽  
Lower Limb Prosthesis ◽  
Limb Prosthesis
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