scholarly journals Development of Surface EMG Game Control Interface for Persons with Upper Limb Functional Impairments

Signals ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 834-851
Author(s):  
Joseph K. Muguro ◽  
Pringgo Widyo Laksono ◽  
Wahyu Rahmaniar ◽  
Waweru Njeri ◽  
Yuta Sasatake ◽  
...  
Keyword(s):  
Machine Learning ◽  
Upper Limb ◽  
People With Disabilities ◽  
Surface Emg ◽  
Functional Impairments ◽  
Semg Signal ◽  
Control Interface ◽  
Directional Control ◽  
Custom Made ◽  
Significant Difference

In recent years, surface Electromyography (sEMG) signals have been effectively applied in various fields such as control interfaces, prosthetics, and rehabilitation. We propose a neck rotation estimation from EMG and apply the signal estimate as a game control interface that can be used by people with disabilities or patients with functional impairment of the upper limb. This paper utilizes an equation estimation and a machine learning model to translate the signals into corresponding neck rotations. For testing, we designed two custom-made game scenes, a dynamic 1D object interception and a 2D maze scenery, in Unity 3D to be controlled by sEMG signal in real-time. Twenty-two (22) test subjects (mean age 27.95, std 13.24) participated in the experiment to verify the usability of the interface. From object interception, subjects reported stable control inferred from intercepted objects more than 73% accurately. In a 2D maze, a comparison of male and female subjects reported a completion time of 98.84 s. ± 50.2 and 112.75 s. ± 44.2, respectively, without a significant difference in the mean of the one-way ANOVA (p = 0.519). The results confirmed the usefulness of neck sEMG of sternocleidomastoid (SCM) as a control interface with little or no calibration required. Control models using equations indicate intuitive direction and speed control, while machine learning schemes offer a more stable directional control. Control interfaces can be applied in several areas that involve neck activities, e.g., robot control and rehabilitation, as well as game interfaces, to enable entertainment for people with disabilities.

scholarly journals Low-cost electromyography: validity against a commercial system depends on exercise type and intensity

2021 ◽  
Author(s):  
Paolo Tecchio ◽  
Andrea Monte ◽  
Paola Zamparo
Keyword(s):  
Vastus Lateralis ◽  
Low Cost ◽  
Surface Emg ◽  
Knee Extensors ◽  
Experimental Conditions ◽  
Semg Signal ◽  
Time To Peak ◽  
Custom Made ◽  
Significant Difference ◽  
Correlation Parameters

The aim of this study was to assess the validity of a custom-made low cost (LC) and a commercial surface EMG apparatus in controlled experimental conditions and different exercise types: maximal voluntary contractions (MVC) at 105, 90, 75, 60, 45 and 30° knee angle and explosive fix-end contractions of the knee extensors (75°) at an isometric dynamometer. sEMG of vastus lateralis was recorded from the same electrodes simultaneously, then analyzed in the same way; sEMG were finally expressed in percentage of those collected at 75°MVC. LC underestimated the sEMG signal at the more extended knee angles (30-60°), significant difference was observed only at 30°. In the explosive contractions no differences between devices were observed in average and peak sEMG, as well as in the time to peak and the activation time. Bland-Altman tests and correlation parameters indicate the LC device is not sensible enough to detect the time to peak and the peak values of the sEMG signal properly. Results suggest low-cost systems might be a valid alternative to commercial ones, but attention must be paid when analyzing rapid events.

Wavelet Based Machine Learning Technique to Classify the Different Shoulder Movement of Upper Limb Amputee

2017 ◽  
Vol 31 ◽  
pp. 32-43 ◽  
Author(s):  
Amanpreet Kaur ◽  
Amod Kumar ◽  
Ravinder Agarwal
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Random Forest ◽  
Upper Limb ◽  
Learning Algorithm ◽  
Discrete Wavelet ◽  
Semg Signal ◽  
Shoulder Movement ◽  
Fundamental Information

The wavelet transform is an accurate, efficient and efficacious method to improve the quality of the myoelectric signal. Classification of the signal from upper limb using Surface Electromyogram (SEMG) signal has been the matter of extensive research. Number of methods and algorithms have been described by researchers to classify biomedical signals. The main aim of this paper to extract the different coefficient values from the given SEMG data by using Discrete Wavelet Transform (DWT). Afterward, random forest machine learning algorithm was used to identify the different shoulder movement of an upper limb amputee. The combination of wavelet coefficients and random forest exhibited the best performance with 99.2% accuracy for the classification of different shoulder motions. It was found that the different motion can be identified accurately and provide the fundamental information to develop an efficient prosthetic device.

Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition

2021 ◽  
Vol 68 ◽  
pp. 102577
Author(s):  
Yang Zhou ◽  
Chaoyang Chen ◽  
Mark Cheng ◽  
Yousef Alshahrani ◽  
Sreten Franovic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Learning Methods ◽  
Semg Signal ◽  
Motion Recognition ◽  
Machine Learning Methods ◽  
Shoulder Motion

Computed tomography angiography and microsurgical flaps for traumatic wounds: What is the added value?

2021 ◽  
pp. 1-9
Author(s):  
Lucas Sousa Macedo ◽  
Renato Polese Rusig ◽  
Gustavo Bersani Silva ◽  
Alvaro Baik Cho ◽  
Teng Hsiang Wei ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Upper Limb ◽  
Computed Tomography Angiography ◽  
Lower Limb ◽  
High Energy ◽  
Lower Limbs ◽  
Added Value ◽  
Vascular Lesions ◽  
Limb Injuries ◽  
Significant Difference

BACKGROUND: Microsurgical flaps are widely used to treat complex traumatic wounds of upper and lower limbs. Few studies have evaluated whether the vascular changes in preoperative computed tomography angiography (CTA) influence the selection of recipient vessel and type of anastomosis and the microsurgical flaps outcomes including complications. OBJECTIVE: The aim of this study was to evaluate if preoperative CTA reduces the occurrence of major complications (revision of the anastomosis, partial or total flap failure, and amputation) of the flaps in upper and lower limb trauma, and to describe and analyze the vascular lesions of the group with CTA and its relationship with complications. METHODS: A retrospective cohort study was undertaken with all 121 consecutive patients submitted to microsurgical flaps for traumatic lower and upper limb, from 2014 to 2020. Patients were divided into two groups: patients with preoperative CTA (CTA+) and patients not submitted to CTA (CTA–). The presence of postoperative complications was assessed and, within CTA+, we also analyzed the number of patent arteries on CTA and described the arterial lesions. RESULTS: Of the 121 flaps evaluated (84 in the lower limb and 37 in the upper limb), 64 patients underwent preoperative CTA. In the CTA+ group, 56% of patients with free flaps for lower limb had complete occlusion of one artery. CTA+ patients had a higher rate of complications (p = 0.031), which may represent a selection bias as the most complex limb injuries and may have CTA indicated more frequently. The highest rate of complications was observed in chronic cases (p = 0.034). There was no statistically significant difference in complications in patients with preoperative vascular injury or the number of patent arteries. CONCLUSIONS: CTA should not be performed routinely, however, CTA may help in surgical planning, especially in complex cases of high-energy and chronic cases, since it provides information on the best recipient artery and the adequate level to perform the microanastomosis, outside the lesion area.

scholarly journals A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chinmay P. Swami ◽  
Nicholas Lenhard ◽  
Jiyeon Kang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Upper Limb ◽  
Daily Living ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Random Forest Regression ◽  
Prosthetic Devices ◽  
Upper Limb Function ◽  
The Neural Network

AbstractProsthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.

scholarly journals Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2469
Author(s):  
Chen-Yi Xie ◽  
Chun-Lap Pang ◽  
Benjamin Chan ◽  
Emily Yuen-Yuen Wong ◽  
Qi Dou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantitative Imaging ◽  
Risk Groups ◽  
Review Of Literature ◽  
Primary Tumors ◽  
Non Invasive ◽  
Significant Difference ◽  
Recent Developments ◽  
Health Significance ◽  
Imaging Markers

Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

CONTROL OF FOREARM MODULE IN UPPER-LIMB REHABILITATION ROBOT FOR REDUCTION AND BIOMECHANICAL ASSESSMENT OF PRONATOR HYPERTONIA OF STROKE PATIENTS

2016 ◽  
Vol 16 (02) ◽  
pp. 1650008 ◽  
Author(s):  
PIN-CHENG KUNG ◽  
CHOU-CHING K. LIN ◽  
SHU-MIN CHEN ◽  
MING-SHAUNG JU
Keyword(s):  
Upper Limb ◽  
Position Control ◽  
Biomechanical Properties ◽  
Torque Control ◽  
Rehabilitation Robot ◽  
Stroke Patients ◽  
Upper Limb Rehabilitation ◽  
Biomechanical Assessment ◽  
Custom Made ◽  
Limb Rehabilitation

Spastic hypertonia causes loss of range of motion (ROM) and contractures in patients with post-stroke hemiparesis. The pronation/supination of the forearm is an essential functional movement in daily activities. We developed a special module for a shoulder-elbow rehabilitation robot for the reduction and biomechanical assessment of pronator/supinator hypertonia of the forearm. The module consisted of a rotational drum driven by an AC servo motor and equipped with an encoder and a custom-made torque sensor. By properly switching the control algorithm between position control and torque control, a hybrid controller able to mimic a therapist’s manual stretching movements was designed. Nine stroke patients were recruited to validate the functions of the module. The results showed that the affected forearms had significant increases in the ROM after five cycles of stretching. Both the passive ROM and the average stiffness were highly correlated to the spasticity of the forearm flexor muscles as measured using the Modified Ashworth Scale (MAS). With the custom-made module and controller, this upper-limb rehabilitation robot may be able to aid physical therapists to reduce hypertonia and quantify biomechanical properties of the muscles for forearm rotation in stroke patients.

Weight loss trajectories and related factors in a 16-week mobile obesity intervention: A retrospective observational study (Preprint)

2021 ◽  
Author(s):  
Ho Heon Kim ◽  
Young In Kim ◽  
Andreas Michaelides ◽  
Yu Rang Park
Keyword(s):  
Machine Learning ◽  
Weight Loss ◽  
Effect Size ◽  
Weight Change ◽  
Average Weight ◽  
Learning Approach ◽  
Obesity Management ◽  
Significant Difference ◽  
Machine Learning Approach ◽  
Cluster 2

BACKGROUND In obesity management, whether patients lose 5% or more of their initial weight is a critical factor in their clinical outcome. However, evaluations that only take this approach cannot identify and distinguish between individuals whose weight change varies and those who steadily lose weight. Evaluation of weight loss considering the volatility of weight change through a mobile-based intervention for obesity can facilitate the understanding of individuals’ behavior and weight changes from a longitudinal perspective. OBJECTIVE With machine learning approach, we examined weight loss trajectories and explored the factors related to behavioral and app usage characteristics that induce weight loss. METHODS We used the lifelog data of 19,784 individuals who enrolled in a 16-week obesity management program on the healthcare app Noom in the US during August 8, 2013 to August 8, 2019. We performed K-means clustering with dynamic time warping to cluster the weight loss time series and inspected the quality of clusters with the total sum of distance within the clusters. To identify the usage factors to determine clustering assignment, we longitudinally compared weekly usage statistics with effect size on a weekly basis. RESULTS Initial Body Mass Index (BMI) of participants was 33.9±5.9 kg/m2, and ultimately reached an average BMI of 32.0±5.7 kg/m2. In their weight log, 5 Clusters were identified: Cluster 1 (sharp decrease) showed a high proportion of weight reduction class between 10% and 15%—the highest among the five clusters (n=2,364/12,796, 18.9%)—followed by Cluster 2 (moderate decrease), Cluster 3 (increase), Cluster 4 (yoyo), Cluster 5 (other). In comparison between cluster 2 and cluster 4, although the effect size of difference in the average meal input adherence and average weight input adherence did not show a significant difference in the first week, it increased continuously for 7 weeks (Cohen’s d=0.408; 0.38). CONCLUSIONS With machine learning approach clustering shape-based timeseries similarity, this study identified 5 weight loss trajectories in mobile weight management app. Overall adherence and early adherence related to self-monitoring emerged as a potential predictor of these trajectories.

scholarly journals Functional impairments for outcomes in a randomized trial of unruptured brain AVMs

Neurology ◽  
2017 ◽  
Vol 89 (14) ◽  
pp. 1499-1506 ◽  
Author(s):  
J.P. Mohr ◽  
Jessica R. Overbey ◽  
Ruediger von Kummer ◽  
Marco A. Stefani ◽  
Richard Libman ◽  
...  
Keyword(s):  
Randomized Trial ◽  
Functional Impairment ◽  
Interventional Therapy ◽  
Functional Impairments ◽  
Interventional Management ◽  
Parallel Group ◽  
Significant Difference ◽  
Martin Grade ◽  
Brain Avms

Objective:To investigate the effects of medical vs interventional management on functional outcome in A Randomized Trial of Unruptured Brain Arteriovenous Malformations (ARUBA).Methods:We used the initial results of a nonblinded, randomized, controlled, parallel-group trial involving adults ≥18 years of age with an unruptured brain arteriovenous malformation (AVM) to compare the effects of medical management (MM) with or without interventional therapy (IT) on functional impairment, defined by a primary outcome of death or symptomatic stroke causing modified Rankin Scale (mRS) score ≥2. ARUBA closed recruitment on April 15, 2013.Results:After a median of 33.3 months of follow-up (interquartile range 16.3–49.8 months), of the 223 enrolled in the trial, those in the MM arm were less likely to experience primary outcomes with an mRS score ≥2 than those who underwent IT. The results applied for both those as randomized (MM n = 109 vs IT n = 114) (hazard ratio [HR] 0.25, 95% confidence interval [CI] 0.11–0.57, p = 0.001) and as treated (MM n = 125 vs IT n = 98) (HR 0.10, 95% CI 0.04–0.28, p < 0.001). Functional impairment for the outcomes showed no significant difference by Spetzler-Martin grade for MM but was more frequent with increasing grades for IT (p < 0.001).Conclusion:Death or stroke with functional impairment in ARUBA after a median follow-up of 33 months was significantly lower for those in the MM arm both as randomized and as treated compared with those with IT. Functional severity of outcomes was lower in the MM arm, regardless of Spetzler-Martin grades.ClinicalTrials.gov identifier:NCT00389181.Classification of evidence:This study provides Class II evidence that for adults with unruptured brain AVMs, interventional management compared to MM increases the risk of disability and death over ≈3 years.

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