scholarly journals Multi-grip classification-based prosthesis control with two EMG-IMU sensors

2019 ◽  
Author(s):  
Agamemnon Krasoulis ◽  
Sethu Vijayakumar ◽  
Kianoush Nazarpour
Keyword(s):  
Machine Learning ◽  
Upper Limb ◽  
Prosthetic Hand ◽  
Myoelectric Prosthesis ◽  
Inertial Measurement ◽  
Machine Learning Methods ◽  
Pick And Place ◽  
Measurement Units ◽  
Prosthesis Control ◽  
Emg Electrodes

AbstractIn the field of upper-limb myoelectric prosthesis control, the use of statistical and machine learning methods has been long proposed as a means of enabling intuitive grip selection and actuation. Recently, this paradigm has found its way toward commercial adoption. Machine learning-based prosthesis control typically relies on the use of a large number of electrodes. Here, we propose an end-to-end strategy for multi-grip, classification-based prosthesis control using only two sensors, comprising electromyography (EMG) electrodes and inertial measurement units (IMUs). We emphasize the importance of accurately estimating posterior class probabilities and rejecting predictions made with low confidence, so as to minimize the rate of unintended prosthesis activations. To that end, we propose a confidence-based error rejection strategy using grip-specific thresholds. We evaluate the efficacy of the proposed system with real-time pick and place experiments using a commercial multi-articulated prosthetic hand and involving 12 able-bodied and two transradial (i.e., below-elbow) amputee participants. Results promise the potential for deploying intuitive, classification-based multi-grip control in existing upper-limb prosthetic systems subject to small modifications.

scholarly journals Analysis of ergonomic and unergonomic human lifting behaviors by using Inertial Measurement Units

2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Angular Velocity ◽  
Musculoskeletal System ◽  
Occupational Diseases ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Main Components

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.

A Novel Approach and Design of Embedded Controlled Prosthetic Upper Limb to Assist the above Elbow Amputees

2011 ◽  
Vol 403-408 ◽  
pp. 2039-2045
Author(s):  
D. Jothi Lakshmi ◽  
G. Illakiya ◽  
R. Rajkamal
Keyword(s):  
Magnetic Field ◽  
Upper Limb ◽  
Proprioceptive Feedback ◽  
Skeletal Structure ◽  
Prosthetic Hand ◽  
Field Variation ◽  
Myoelectric Prosthesis ◽  
Magnetic Field Sensors ◽  
Novel Approach ◽  
Major Disadvantage

The existing prosthetic upper limb design and control is divided into two broad categories. One is the myoelectric prosthesis where electromechanical active joints actuate the arm segments and is directly activated by acquiring Electromyogram (EMG) signals from the amputee which is sensed by myoelectric electrodes. Acquiring of the EMG signals is a tedious process as it involves adequate amplification and proper filtering. Also isolation of noise from EMG signals poses difficulty. The other category falls under intelligent prosthetic hand where neural networks (NN) are involved. It requires adequate training for NN operation that leads to the complexity in implementing electronic circuits. The major disadvantage of the above mentioned technologies is lack of proprioceptive feedback from the amputee. The drawbacks of the existing technologies motivates us to design a prototype with proprioceptive feedback to control the Above Elbow (AE) prosthesis with a permanent magnet implanted at the distal end of the residual humerus of the amputee. The proprioception remains intact to the residual limb skeletal structure. In this work, the proposed approach involves in processing the magnetic field variation due to residual arm bone movement which is sensed by magnetic field sensors. The embedded controller controls the movements of the prosthetic hand by processing the signals received from the sensors to assist the AE amputee.

Defect patterns study of pick-and-place machine using automated optical inspection data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuqiao Cen ◽  
Jingxi He ◽  
Daehan Won
Keyword(s):  
Machine Learning ◽  
Content Type ◽  
Component Placement ◽  
Root Cause ◽  
Machine Learning Methods ◽  
Optical Inspection ◽  
Pick And Place ◽  
Automated Optical Inspection ◽  
Inspection Data ◽  
Root Cause Identification

Purpose This paper aims to study the component pick-and-place (P&P) defect patterns for different root causes based on automated optical inspection data and develop a root cause identification model using machine learning. Design/methodology/approach This study conducts experiments to simulate the P&P machine errors including nozzle size and nozzle pick-up position. The component placement qualities with different errors are inspected. This study uses various machine learning methods to develop a root cause identification model based on the inspection result. Findings The experimental results revealed that the wrong nozzle size could increase the mean and the standard deviation of component placement offset and the probability of component drop during the transfer process. Moreover, nozzle pick-up position can affect the rotated component placement offset. These root causes of defects can be traced back using machine learning methods. Practical implications This study provides operators in surface mount technology assembly lines to understand the P&P machine error symptoms. The developed model can trace back the root causes of defects automatically in real line production. Originality/value The findings are expected to lead the regular preventive maintenance to data-driven predictive and reactive maintenance.

A machine learning approach to estimate Minimum Toe Clearance using Inertial Measurement Units

2015 ◽  
Vol 48 (16) ◽  
pp. 4309-4316 ◽  
Author(s):  
Braveena K. Santhiranayagam ◽  
Daniel T.H. Lai ◽  
W.A. Sparrow ◽  
Rezaul K. Begg
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Machine Learning Approach ◽  
Measurement Units

scholarly journals Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke

2020 ◽  
Vol 34 (5) ◽  
pp. 428-439 ◽  
Author(s):  
Ceren Tozlu ◽  
Dylan Edwards ◽  
Aaron Boes ◽  
Douglas Labar ◽  
K. Zoe Tsagaris ◽  
...  
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Upper Limb ◽  
Motor Impairment ◽  
Chronic Stroke ◽  
Response To Therapy ◽  
Stroke Patients ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The Difference

Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2. Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median [Formula: see text] P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient’s postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients’ response to therapy and, therefore, could be included in prospective studies.

scholarly journals Pedestrian navigation: how can inertial measurment units assist smartphones?

2021 ◽  
Vol 9 (2) ◽  
pp. 22-34
Author(s):  
I. A. Chistyakov ◽  
I. V. Grishov ◽  
A. A. Nikulin ◽  
M. V. Pikhletsky ◽  
I. B. Gartseev
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Time Synchronization ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Pedestrian Navigation ◽  
Navigation Algorithms ◽  
Modified Kalman Filter ◽  
Measurement Units ◽  
Combined Measurements

This paper is devoted to construction of reference walking trajectories for developing pedestrian navigation algorithms for smartphones. Such trajectories can be used both for verification of classical algorithms of navigation or for application of machine learning technics. Reconstruction of closed trajectories based on data from foot-mounted inertial measurement units (IMU) is investigated. The advantages of the approach are the use of inexpensive sensors and the simplicity of the presented method. We propose algorithms for reconstruction of smooth 2D pedestrian trajectories based on measurements from a single IMU as well as on combined measurements from two IMU’s. Introduced algorithms are based on application of modified Kalman filter with an assumption of IMU having zero velocity when foot contacts the ground. In case of two measurement units, it is additionally assumed that the positions of the sensors cannot differ significantly from each other. The algorithms were tested on trajectories lasting from 1 to 10 minutes, passing indoors on horizontal surfaces. Obtained results were compared with high precision trajectories acquired with GNSS RTK receivers. Additionally, the process of inter-device time synchronization is investigated and detailed description of the experiments and used equipment is given. The dataset used for verification of proposed algorithms is freely available at: http://gartseev.ru/projects/rtj2021.

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