Myoelectric Control of Robotic Leg Prostheses and Exoskeletons: A Review

2021 ◽  
Author(s):  
Ali Nasr ◽  
Brokoslaw Laschowski ◽  
John McPhee
Keyword(s):  
Control Systems ◽  
Myoelectric Control ◽  
Machine Learning Algorithms ◽  
Torque Control ◽  
Future Research ◽  
Reactive Control ◽  
Rhythmic Movements ◽  
Myoelectric Signals ◽  
Joint Torques ◽  
Robotic Leg

Abstract Myoelectric signals from the human motor control system can improve the real-time control and neural-machine interface of robotic leg prostheses and exoskeletons for different locomotor activities (e.g., walking, sitting down, stair ascent, and non-rhythmic movements). Here we review the latest advances in myoelectric control designs and propose future directions for research and innovation. We review the different wearable sensor technologies, actuators, signal processing, and pattern recognition algorithms used for myoelectric locomotor control and intent recognition, with an emphasis on the hierarchical architectures of volitional control systems. Common mechanisms within the control architecture include 1) open-loop proportional control with fixed gains, 2) active-reactive control, 3) joint mechanical impedance control, 4) manual-tuning torque control, 5) adaptive control with varying gains, and 6) closed-loop servo actuator control. Based on our review, we recommend that future research consider using musculoskeletal modeling and machine learning algorithms to map myoelectric signals from surface electromyography (EMG) to actuator joint torques, thereby improving the automation and efficiency of next-generation EMG controllers and neural interfaces for robotic leg prostheses and exoskeletons. We also propose an example model-based adaptive impedance EMG controller including muscle and multibody system dynamics. Ongoing advances in the engineering design of myoelectric control systems have implications for both locomotor assistance and rehabilitation.

Predictive, Deadbeat, and Combined Controls

2018 ◽  
Author(s):  
Sadegh Vaez-Zadeh
Keyword(s):  
Control Systems ◽  
Predictive Control ◽  
Direct Torque Control ◽  
Fast Response ◽  
Torque Control ◽  
Control Methods ◽  
Electric Motors ◽  
Operating Cycle ◽  
Combined Control ◽  
The One

In this chapter, three control methods recently developed for or applied to electric motors in general and to permanent magnet synchronous (PMS) motors, in particular, are presented. The methods include model predictive control (MPC), deadbeat control (DBC), and combined vector and direct torque control (CC). The fundamental principles of the methods are explained, the machine models appropriate to the methods are derived, and the control systems are explained. The PMS motor performances under the control systems are also investigated. It is elaborated that MPC is capable of controlling the motor under an optimal performance according to a defined objective function. DBC, on the other hand, provides a very fast response in a single operating cycle. Finally, combined control produces motor dynamics faster than one under VC, with a smoother performance than the one under DTC.

scholarly journals Algorithmic management in a work context

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110203
Author(s):  
Mohammad Hossein Jarrahi ◽  
Gemma Newlands ◽  
Min Kyung Lee ◽  
Christine T. Wolf ◽  
Eliscia Kinder ◽  
...  
Keyword(s):  
Information Exchange ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Organizational Level ◽  
Future Research ◽  
Power Dynamics ◽  
Work Processes ◽  
Work Settings ◽  
Standard Work ◽  
Management Functions

The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps.

scholarly journals Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration

2019 ◽  
Author(s):  
Brock Laschowski ◽  
Reza Sharif Razavian ◽  
John McPhee
Keyword(s):  
Lower Limb ◽  
Electrical Energy ◽  
Mechanical Power ◽  
Future Research ◽  
Ground Reaction Forces ◽  
Dynamic Simulations ◽  
Inverse Dynamic ◽  
Joint Torques ◽  
Reaction Forces ◽  
Body Segment Parameters

AbstractAlthough regenerative actuators can extend the operating durations of robotic lower-limb exoskeletons and prostheses, these energy-efficient powertrains have been exclusively designed and evaluated for continuous level-ground walking.ObjectiveHere we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for electrical regeneration.MethodsNine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification, whereby differences in ground reaction forces and moments between the experimental measurements and inverse dynamic simulations were minimized. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated over time to determine joint biomechanical energy.ResultsThe hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle joints per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively.Conclusion and SignificanceAssuming an 80-kg person and previously published regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of total electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations are based on healthy young adults, future research should include seniors and/or rehabilitation patients to better estimate the biomechanical energy available for electrical regeneration among individuals with mobility impairments.

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Fathima Aliyar Vellameeran ◽  
Thomas Brindha
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Research Papers ◽  
Using Data ◽  
Intelligent Methods

Abstract Objectives To make a clear literature review on state-of-the-art heart disease prediction models. Methods It reviews 61 research papers and states the significant analysis. Initially, the analysis addresses the contributions of each literature works and observes the simulation environment. Here, different types of machine learning algorithms deployed in each contribution. In addition, the utilized dataset for existing heart disease prediction models was observed. Results The performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure, etc., are learned. Further, the best performance is also checked to confirm the effectiveness of entire contributions. Conclusions The comprehensive research challenges and the gap are portrayed based on the development of intelligent methods concerning the unresolved challenges in heart disease prediction using data mining techniques.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

In the field of tension between creativity and efficiency: a systematic literature review of management control systems for innovation activities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Philipp Lill ◽  
Andreas Wald ◽  
Jan Christoph Munck
Keyword(s):  
Systematic Review ◽  
Control Systems ◽  
Systems Design ◽  
Management Control ◽  
Management Control Systems ◽  
Future Research ◽  
Content Type ◽  
Innovation Activities ◽  
Body Of Knowledge ◽  
Control Systems Design

PurposeThe number of theoretical and empirical research on management control of innovation activities has significantly increased. Existing studies in this field are characterized by a wide dispersion and a multitude of different definitions. The purpose of this article is to provide a systematic review of the literature on management control of innovation activities and to synthesize the current body of knowledge.Design/methodology/approachFollowing a systematic review approach, this article reviews 79 articles on management control for innovation activities from 1959 to 2019 and inductively derives a multi-dimensional framework.FindingsThe review of existing studies advances the debate about the detrimental versus beneficial character of management control systems for innovation, showing that the repressing character of control is not inherent to control itself, but emanates from the design of the respective management control system.Research limitations/implicationsThe multi-dimensional framework connects and combines existing research and thus synthesizes the current state of knowledge in this field. Additionally, the framework can guide practitioners to systematically assess context factors and consequences of their management control systems design, and it shows avenues for future research.Originality/valueThe scientific and practical value of this paper is the convergence of the current body of knowledge consisting of various definitions and conceptualizations and the identification of avenues for future research.

scholarly journals Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4289 ◽  
Author(s):  
Jin ◽  
Yin ◽  
Chen
Keyword(s):  
Control Systems ◽  
Steering System ◽  
System Dynamic ◽  
Dynamic State ◽  
Future Research ◽  
Active Safety ◽  
Automated Driving ◽  
Estimation Techniques ◽  
Stability Performance ◽  
Vehicle System

In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

Bridging the Abstraction Gap for the Design of Embedded Reactive Systems Using Esterel: A Case Study

2007 ◽  
Author(s):  
Martin Zauner ◽  
Michael Kramer ◽  
Peter Balog
Keyword(s):  
Embedded Systems ◽  
Control Systems ◽  
Reactive Systems ◽  
Design Methods ◽  
Design Approach ◽  
Reactive Control ◽  
Systems Implementation ◽  
Traditional Design ◽  
Abstraction Levels

New design methodologies at higher abstraction levels are necessary to deal with the increasing complexity of modern embedded systems. As a consequence, new design paradigms must supersede traditional design methods to bridge the abstraction gap which often exists between specification and implementation. This paper examines several examples which evaluate the applicability of Esterel, a language with well-defined semantics for specification and verification of reactive control systems. Implementation size, performance and design effort were selected as measures to assess the benefits of this design approach in comparison to a traditional one.

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