scholarly journals Control Strategies and Artificial Intelligence in Rehabilitation Robotics

AI Magazine ◽  
2015 ◽  
Vol 36 (4) ◽  
pp. 23-33 ◽  
Author(s):  
Domen Novak ◽  
Robert Riener
Keyword(s):  
Artificial Intelligence ◽  
Control Strategies ◽  
Rehabilitation Robotics ◽  
Control Algorithms ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
Rehabilitation Robots ◽  
Exercise Adaptation ◽  
Dominant Paradigm ◽  
Do So

Rehabilitation robots physically support and guide a patient's limb during motor therapy, but require sophisticated control algorithms and artificial intelligence to do so. This article provides an overview of the state of the art in this area. It begins with the dominant paradigm of assistive control, from impedance-based cooperative controller through electromyography and intention estimation. It then covers challenge-based algorithms, which provide more difficult and complex tasks for the patient to perform through resistive control and error augmentation. Furthermore, it describes exercise adaptation algorithms that change the overall exercise intensity based on the patient's performance or physiological responses, as well as socially assistive robots that provide only verbal and visual guidance. The article concludes with a discussion of the current challenges in rehabilitation robot software: evaluating existing control strategies in a clinical setting as well as increasing the robot's autonomy using entirely new artificial intelligence techniques.

scholarly journals Crowd of Oz: A Crowd-Powered Social Robotics System for Stress Management

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 569 ◽  
Author(s):  
Tahir Abbas ◽  
Vassilis-Javed Khan ◽  
Ujwal Gadiraju ◽  
Emilia Barakova ◽  
Panos Markopoulos
Keyword(s):  
Artificial Intelligence ◽  
Open Source ◽  
Real Time ◽  
Healthy Lifestyle ◽  
Social Robotics ◽  
Coping With Stress ◽  
Assistive Robots ◽  
Socially Assistive Robots ◽  
The Past ◽  
And Training

Coping with stress is crucial for a healthy lifestyle. In the past, a great deal of research has been conducted to use socially assistive robots as a therapy to alleviate stress and anxiety related problems. However, building a fully autonomous social robot which can deliver psycho-therapeutic solutions is a very challenging endeavor due to limitations in artificial intelligence (AI). To overcome AI’s limitations, researchers have previously introduced crowdsourcing-based teleoperation methods, which summon the crowd’s input to control a robot’s functions. However, in the context of robotics, such methods have only been used to support the object manipulation, navigational, and training tasks. It is not yet known how to leverage real-time crowdsourcing (RTC) to process complex therapeutic conversational tasks for social robotics. To fill this gap, we developed Crowd of Oz (CoZ), an open-source system that allows Softbank’s Pepper robot to support such conversational tasks. To demonstrate the potential implications of this crowd-powered approach, we investigated how effectively, crowd workers recruited in real-time can teleoperate the robot’s speech, in situations when the robot needs to act as a life coach. We systematically varied the number of workers who simultaneously handle the speech of the robot (N = 1, 2, 4, 8) and investigated the concomitant effects for enabling RTC for social robotics. Additionally, we present Pavilion, a novel and open-source algorithm for managing the workers’ queue so that a required number of workers are engaged or waiting. Based on our findings, we discuss salient parameters that such crowd-powered systems must adhere to, so as to enhance their performance in response latency and dialogue quality.

Robot Teaching, Pedagogy, and Policy

2020 ◽  
pp. 787-803
Author(s):  
Elana Zeide
Keyword(s):  
Artificial Intelligence ◽  
Analytical Methods ◽  
Personalized Learning ◽  
Educational Process ◽  
Learning Systems ◽  
Policy Decisions ◽  
Public Discussion ◽  
Teaching Pedagogy ◽  
Do So

This chapter looks at the use of artificial intelligence (AI) in education, which immediately conjures the fantasy of robot teachers, as well as fears that robot teachers will replace their human counterparts. However, AI tools impact much more than instructional choices. Personalized learning systems take on a whole host of other educational roles as well, fundamentally reconfiguring education in the process. They not only perform the functions of robot teachers but also make pedagogical and policy decisions typically left to teachers and policymakers. Their design, affordances, analytical methods, and visualization dashboards construct a technological, computational, and statistical infrastructure that literally codifies what students learn, how they are assessed, and what standards they must meet. However, school procurement and implementation of these systems are rarely part of public discussion. If they are to remain relevant to the educational process itself, as opposed to just its packaging and context, schools and their stakeholders must be more proactive in demanding information from technology providers and setting internal protocols to ensure effective and consistent implementation. Those who choose to outsource instructional functions should do so with sufficient transparency mechanisms in place to ensure professional oversight guided by well-informed debate.

scholarly journals Indirect Feedback Measurement of Flow in a Water Pumping Network Employing Artificial Intelligence

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 75
Author(s):  
Thommas Kevin Sales Flores ◽  
Juan Moises Mauricio Villanueva ◽  
Heber P. Gomes ◽  
Sebastian Y. C. Catunda
Keyword(s):  
Artificial Intelligence ◽  
Nonlinear Models ◽  
Control Strategies ◽  
Experimental Tests ◽  
Feedback System ◽  
Indirect Measurement ◽  
High Price ◽  
Maximum Relative Error ◽  
Pumping System ◽  
Water Pumping

Indirect measurement can be used as an alternative to obtain a desired quantity, whose physical positioning or use of a direct sensor in the plant is expensive or not possible. This procedure can been improved by means of feedback control strategies of a secondary variable, which can be measured and controlled. Its main advantage is a new form of dynamic response, with improvements in the response time of the measurement of the quantity of interest. In water pumping networks, this methodology can be employed for measuring the flow indirectly, which can be advantageous due to the high price of flow sensors and the operational complexity to install them in pipelines. In this work, we present the use of artificial intelligence techniques in the implementation of the feedback system for indirect flow measurement. Among the contributions of this new technique is the design of the pressure controller using the Fuzzy logic theory, which rules out the need for knowing the plant model, as well as the use of an artificial neural network for the construction of nonlinear models with the purpose of indirectly estimating the flow. The validation of the proposed approach was carried out through experimental tests in a water pumping system, fully automated and installed at the Laboratory of Hydraulic and Energy Efficiency in Sanitation at the Federal University of Paraiba (LENHS/UFPB). The results were compared with an electromagnetic flow sensor present in the system, obtaining a maximum relative error of 10%.

scholarly journals A Survey of Encoding Techniques for Signal Processing in Spiking Neural Networks

2021 ◽  
Author(s):  
Daniel Auge ◽  
Julian Hille ◽  
Etienne Mueller ◽  
Alois Knoll
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Industrial Applications ◽  
Spiking Neural Networks ◽  
Biologically Inspired ◽  
Power Efficient ◽  
Signal Encoding ◽  
Uniform Nomenclature ◽  
Theoretical Foundations ◽  
Do So

AbstractBiologically inspired spiking neural networks are increasingly popular in the field of artificial intelligence due to their ability to solve complex problems while being power efficient. They do so by leveraging the timing of discrete spikes as main information carrier. Though, industrial applications are still lacking, partially because the question of how to encode incoming data into discrete spike events cannot be uniformly answered. In this paper, we summarise the signal encoding schemes presented in the literature and propose a uniform nomenclature to prevent the vague usage of ambiguous definitions. Therefore we survey both, the theoretical foundations as well as applications of the encoding schemes. This work provides a foundation in spiking signal encoding and gives an overview over different application-oriented implementations which utilise the schemes.

The Use of Damping Based Semi-Active Control Algorithms in the Mechanical Smart-Spring System

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Wander Gustavo Rocha Vieira ◽  
Fred Nitzsche ◽  
Carlos De Marqui
Keyword(s):  
Active Control ◽  
Control Algorithm ◽  
Control Strategies ◽  
Vibration Reduction ◽  
Flow Analysis ◽  
Coupled Systems ◽  
Control Technique ◽  
Control Algorithms ◽  
Control Techniques ◽  
Spring Mechanism

In recent decades, semi-active control strategies have been investigated for vibration reduction. In general, these techniques provide enhanced control performance when compared to traditional passive techniques and lower energy consumption if compared to active control techniques. In semi-active concepts, vibration attenuation is achieved by modulating inertial, stiffness, or damping properties of a dynamic system. The smart spring is a mechanical device originally employed for the effective modulation of its stiffness through the use of semi-active control strategies. This device has been successfully tested to damp aeroelastic oscillations of fixed and rotary wings. In this paper, the modeling of the smart spring mechanism is presented and two semi-active control algorithms are employed to promote vibration reduction through enhanced damping effects. The first control technique is the smart-spring resetting (SSR), which resembles resetting control techniques developed for vibration reduction of civil structures as well as the piezoelectric synchronized switch damping on short (SSDS) technique. The second control algorithm is referred to as the smart-spring inversion (SSI), which presents some similarities with the synchronized switch damping (SSD) on inductor technique previously presented in the literature of electromechanically coupled systems. The effects of the SSR and SSI control algorithms on the free and forced responses of the smart-spring are investigated in time and frequency domains. An energy flow analysis is also presented in order to explain the enhanced damping behavior when the SSI control algorithm is employed.

On Integration of Additive Manufacturing During the Design and Development of a Rehabilitation Robot: A Case Study

2015 ◽  
Vol 137 (11) ◽  
Author(s):  
Kaci E. Madden ◽  
Ashish D. Deshpande
Keyword(s):  
Additive Manufacturing ◽  
Neurological Disorders ◽  
Development Process ◽  
Mechanical Design ◽  
Rehabilitation Robotics ◽  
Critical Design ◽  
Rehabilitation Robots ◽  
Time Optimization ◽  
Hand Exoskeleton

The field of rehabilitation robotics has emerged to address the growing desire to improve therapy modalities after neurological disorders, such as a stroke. For rehabilitation robots to be successful as clinical devices, a number of mechanical design challenges must be addressed, including ergonomic interactions, weight and size minimization, and cost–time optimization. We present additive manufacturing (AM) as a compelling solution to these challenges by demonstrating how the integration of AM into the development process of a hand exoskeleton leads to critical design improvements and substantially reduces prototyping cost and time.

scholarly journals Modeling and Controlling the Cooling System of an IC Vehicle

2021 ◽  
pp. 80-85
Author(s):  
C. Mureșan ◽  
◽  
G. Harja
Keyword(s):  
Mathematical Modeling ◽  
Heat Exchangers ◽  
High Performance ◽  
Cooling System ◽  
Control Strategies ◽  
Positive Influence ◽  
Control Algorithms ◽  
Performance Control ◽  
Ic Engines ◽  
Physical Phenomena

The performance and efficiency of internal combustion (IC) engines can be greatly improved by using a high-performance cooling system. This can be achieved by implementing robust control strategies and, also by building the cooling system with high-performance elements. The mechanical execution elements can be replaced with electrically controllable elements such as the pump and the thermostat valve. This will have a positive influence on the degree of controllability of the system. In order to develop high-performance control algorithms, it is necessary to have a model that best reflects the behaviors of the physical system. Thus, this paper presents a mathematical modeling approach for the cooling system using the principles of heat exchangers and the physical phenomena present in them.

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