Pareto-based Dynamic Difficulty Adjustment of a Competitive Exergame for Arm Rehabilitation (Preprint)

2021 ◽  
Author(s):  
Mallipeddi Rammohan ◽  
Oladayo Solomon Ajani
Keyword(s):  
Unique Solution ◽  
First Episode ◽  
Trade Off ◽  
Rehabilitation Outcomes ◽  
Pareto Optimal Set ◽  
Arm Rehabilitation ◽  
Conflicting Objectives ◽  
Single Solution ◽  
And Performance ◽  
Feasible Solutions

BACKGROUND Lack of motivation is a major hindrance to frequent and intense exercise which is critical to rehabilitating people with arm disabilities due to old age, neurological disorders or stroke. Recently, the use of interpersonal exergames has been associated with increased motivation and exercise intensity in arm rehabilitation and is becoming a common trend. However, the Dynamic Difficulty Adjustment (DDA) of such games is still an open issue because unlike the traditional DDA frameworks where game intensity is simply adapted to suit the players' performance, the aim of DDA for exergames is to optimize the conflicting objectives namely of intensity and performance. Objective: To design a dedicated DDA for rehabilitation exergames that optimize the conflicting objectives of intensity and performance by generating a set of feasible trade-off solutions. Based on the rehabilitative needs, the tradeoff worth information of each solution is to be used to select a unique solution. OBJECTIVE To design a dedicated DDA for rehabilitation exergames that optimizes the conflicting objectives of intensity and performance by generating a set of feasible trade-off solutions. Based on the rehabilitative needs, the tradeoff worth information of each solution is to be used to select a unique solution. METHODS We designed a Pareto-based DDA for a competitive exergame that optimizes the two conflicting objectives. Using a set of feasible solutions generated during the first episode of the game, the proposed Pareto-based DDA is used to modify the parameters of the game. Optimizing conflicting objectives generally results in a set of trade-off solutions called Pareto optimal set instead of a single solution. Therefore, the DDA is capable of selecting a single solution from the optimal Pareto based on the trade-off worth information of each solution in the optimal Pareto set. RESULTS Results: Experimental results with 12 unimpaired participants show the capability of the proposed Pareto-based DDA to online adjust the game parameters effectively based on a trade-off between the intensity and motivation. CONCLUSIONS Since rehabilitation outcomes rely on both intensity and motivation, unlike traditional DDA approaches, the capability of Pareto-based DDA to provide trade-off solutions between conflicting objectives of intensity and motivation is very promising to rehabilitation outcomes. However, multi-session investigation over a period of time needs to be carried out to examine if they influence rehabilitation outcomes positively. CLINICALTRIAL This work is not a clinical trial. Although humans participated in this study, they participate in the evaluation of a single-session of a rehabilitation exergame rather than a comprehensive rehabilitation intervention with no health outcomes investigated.

scholarly journals Optimized Deep Learning for Enhanced Trade-off in Differentially Private Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6745-6751
Author(s):  
P. Geetha ◽  
C. Naikodi ◽  
L. Suresh
Keyword(s):  
Deep Learning ◽  
Data Analytics ◽  
Optimal Solution ◽  
Trade Off ◽  
Learning Mechanism ◽  
Good Balance ◽  
Private Data ◽  
Conflicting Objectives ◽  
And Performance ◽  
Two Sides

Privacy and data analytics are two conflicting domains that have gained interest due to the advancements of technology in the big data era. Organizations in sectors such as finance, healthcare, and e-commerce take advantage of the data collected, to help them enable innovative decision making and analysis. What is sidelined is the fact that the collected data have associated private data of the individuals involved, and may be exploited and used for unjustified purposes. Defending privacy and performing useful analytics are two sides of the same coin, and hence achieving a good balance between these is a challenging scenario. This paper proposes an optimized differentially private deep learning mechanism that enhances the trade-off between the conflicting objectives of privacy, accuracy, and performance. The goal of this paper is to provide an optimal solution that gives a quantifiable trade-off between these contradictory objectives.

scholarly journals GRAM

2021 ◽  
Vol 18 (2) ◽  
pp. 1-24
Author(s):  
Nhut-Minh Ho ◽  
Himeshi De silva ◽  
Weng-Fai Wong
Keyword(s):  
Performance Improvement ◽  
Trade Off ◽  
Accuracy Requirement ◽  
Output Error ◽  
Fine Grain ◽  
Mixed Precision ◽  
And Performance ◽  
Effective Use

This article presents GRAM (<underline>G</underline>PU-based <underline>R</underline>untime <underline>A</underline>daption for <underline>M</underline>ixed-precision) a framework for the effective use of mixed precision arithmetic for CUDA programs. Our method provides a fine-grain tradeoff between output error and performance. It can create many variants that satisfy different accuracy requirements by assigning different groups of threads to different precision levels adaptively at runtime . To widen the range of applications that can benefit from its approximation, GRAM comes with an optional half-precision approximate math library. Using GRAM, we can trade off precision for any performance improvement of up to 540%, depending on the application and accuracy requirement.

Selecting packaging material for dry food products by trade‐off of sustainability and performance: A case study on cookies and milk powder

2021 ◽  
Vol 34 (5) ◽  
pp. 303-318
Author(s):  
Maarten Baele ◽  
An Vermeulen ◽  
Dimitri Adons ◽  
Roos Peeters ◽  
Angelique Vandemoortele ◽  
...  
Keyword(s):  
Milk Powder ◽  
Food Products ◽  
Packaging Material ◽  
Trade Off ◽  
Dry Food ◽  
And Performance

Age and Performance Under Pressure

2012 ◽  
pp. 134-152 ◽  
Author(s):  
Harold O. Fried ◽  
Loren W. Tauer
Keyword(s):  
Correlation Coefficient ◽  
High Performance ◽  
Age Distribution ◽  
Mental Ability ◽  
Trade Off ◽  
Athletic Ability ◽  
Front End ◽  
Ladies Professional Golf Association ◽  
And Performance ◽  
Performance Under Pressure

This article explores how well an individual manages his or her own talent to achieve high performance in an individual sport. Its setting is the Ladies Professional Golf Association (LPGA). The order-m approach is explained. Additionally, the data and the empirical findings are presented. The inputs measure fundamental golfing athletic ability. The output measures success on the LPGA tour. The correlation coefficient between earnings per event and the ability to perform under pressure is 0.48. The careers of golfers occur on the front end of the age distribution. There is a classic trade-off between the inevitable deterioration in the mental ability to handle the pressure and experience gained with time. The ability to perform under pressure peaks at age 37.

scholarly journals Single- versus Multiobjective Optimization for Evolution of Neural Controllers in Ms. Pac-Man

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Tse Guan Tan ◽  
Jason Teo ◽  
Kim On Chin
Keyword(s):  
Neural Network ◽  
Multiobjective Optimization ◽  
Dynamic Environment ◽  
Automatic Generation ◽  
Fixed Number ◽  
Evolution Strategy ◽  
Pareto Optimal Set ◽  
Conflicting Objectives ◽  
Hidden Neurons ◽  
Single Versus

The objective of this study is to focus on the automatic generation of game artificial intelligence (AI) controllers for Ms. Pac-Man agent by using artificial neural network (ANN) and multiobjective artificial evolution. The Pareto Archived Evolution Strategy (PAES) is used to generate a Pareto optimal set of ANNs that optimize the conflicting objectives of maximizing Ms. Pac-Man scores (screen-capture mode) and minimizing neural network complexity. This proposed algorithm is called Pareto Archived Evolution Strategy Neural Network or PAESNet. Three different architectures of PAESNet were investigated, namely, PAESNet with fixed number of hidden neurons (PAESNet_F), PAESNet with varied number of hidden neurons (PAESNet_V), and the PAESNet with multiobjective techniques (PAESNet_M). A comparison between the single- versus multiobjective optimization is conducted in both training and testing processes. In general, therefore, it seems that PAESNet_F yielded better results in training phase. But the PAESNet_M successfully reduces the runtime operation and complexity of ANN by minimizing the number of hidden neurons needed in hidden layer and also it provides better generalization capability for controlling the game agent in a nondeterministic and dynamic environment.

scholarly journals Principal-principal conflicts leading to activism of a large government-owned investor in Brazil

2021 ◽  
Vol 5 (1) ◽  
pp. 15-21
Author(s):  
Bruno Elmôr Duarte ◽  
Ricardo Pereira Câmara Leal
Keyword(s):  
Corporate Governance ◽  
Government Policy ◽  
Poor Performance ◽  
Ownership Concentration ◽  
Minority Shareholders ◽  
Trade Off ◽  
Legal Proceedings ◽  
Brazilian Government ◽  
And Performance

This article analyzes conflicts between principals that led to activism by one large Brazilian government-owned investor as a minority shareholder and verifies the antecedents, means employed, apparent motivations, and effectiveness of its reactions (Goranova & Ryan, 2014). It examines the cases of three large high ownership concentration listed companies using solely public sources. Poor performance was a frequent conflict antecedent. No evident trade-off between activism and corporate governance (CG) practices emerged. High ownership concentration influenced the way the investor reacted and its success because opposition through internal CG mechanisms was usually not successful and led to legal proceedings. The limitations of the regulatory framework became evident from the mixed outcomes of these proceedings. The investor was not exclusively financially motivated and it occasionally opposed the interests of other minority shareholders to follow government policy. These findings illustrated how high ownership concentration rendered difficult the mitigation of principal-principal conflicts even for a large government-owned investor and help explain the failure of previous econometric studies to relate activism, quality of CG practices and performance (Young, Peng, Ahlstrom, Bruton, & Jiang, 2008)

scholarly journals Fidelity scales and performance measures to support implementation and quality assurance for first episode psychosis services

2018 ◽  
Vol 12 (6) ◽  
pp. 1235-1242 ◽  
Author(s):  
Donald Addington ◽  
Maximillian Birchwood ◽  
Peter Jones ◽  
Eoin Killackey ◽  
David McDaid ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Performance Measures ◽  
First Episode Psychosis ◽  
First Episode ◽  
Episode Psychosis ◽  
And Performance
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