It is generally accepted that augmented feedback, provided by a human expert or a technical display, effectively enhances motor learning. However, discussion of the way to most effectively provide augmented feedback has been controversial. Related studies have focused primarily on simple or artificial tasks enhanced by visual feedback. Recently, technical advances have made it possible also to investigate more complex, realistic motor tasks and to implement not only visual, but also auditory, haptic, or multimodal augmented feedback. The aim of this review is to address the potential of augmented unimodal and multimodal feedback in the framework of motor learning theories. The review addresses the reasons for the different impacts of feedback strategies within or between the visual, auditory, and haptic modalities and the challenges that need to be overcome to provide appropriate feedback in these modalities, either in isolation or in combination. Accordingly, the design criteria for successful visual, auditory, haptic, and multimodal feedback are elaborated.

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Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review

The available versions of the Vandenberg and Kuse (1978) Mental Rotations Test (MRT) have physically deteriorated because only copies of copies are available. We report results from a redrawn version of the MRT and for alternate versions of the test. Males perform better than females, and students drawn from the physical sciences perform better than students drawn from the social sciences and humanities, confirming other reports with the original version of the MRT. Subjects find it very hard to perform the MRT when stimuli require rotation along both the top/bottom axis and the left/right axis. The magnitude of effect sizes for sex (which account, on average, for some 20% of the variance) does not increase with increasing difficulty of the task. Minimal strategy effects were observed and females did not perform differently during the menstrual period as opposed to the days between the menstrual periods. Practice effects are dramatic, confirming other reports with the original MRT, and can also be shown to be powerful in a transfer for practice paradigm, where test and retest involve different versions of the MRT. Main effects of handedness on MRT performance were not found.

A redrawn Vandenverg and Kuse mental rotations test : different versions and factors that affect performance

Approaches to learning

As robots and other intelligent agents move from simple environments and problems to more complex, unstructured settings, manually programming their behavior has become increasingly challenging and expensive. Often, it is easier for a teacher to demonstrate a desired behavior rather than attempt to manually engineer it. This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning. This work provides an introduction to imitation learning. It covers the underlying assumptions, approaches, and how they relate; the rich set of algorithms developed to tackle the problem; and advice on effective tools and implementation. We intend this paper to serve two audiences. First, we want to familiarize machine learning experts with the challenges of imitation learning, particularly those arising in robotics, and the interesting theoretical and practical distinctions between it and more familiar frameworks like statistical supervised learning theory and reinforcement learning. Second, we want to give roboticists and experts in applied artificial intelligence a broader appreciation for the frameworks and tools available for imitation learning. We pay particular attention to the intimate connection between imitation learning approaches and those of structured prediction Daume III et al. [2009]. To structure this discussion, we categorize imitation learning techniques based on the following key criteria which drive algorithmic decisions:

1) The structure of the policy space. Is the learned policy a time-index trajectory (trajectory learning), a mapping from observations to actions (so called behavioral cloning [Bain and Sammut, 1996]), or the result of a complex optimization or planning problem at each execution as is common in inverse optimal control methods [Kalman, 1964, Moylan and Anderson, 1973].

2) The information available during training and testing. In particular, is the learning algorithm privy to the full state that the teacher possess? Is the learner able to interact with the teacher and gather corrections or more data? Does the learner have a (typically a priori) model of the system with which it interacts? Does the learner have access to the reward (cost) function that the teacher is attempting to optimize?

3) The notion of success. Different algorithmic approaches provide varying guarantees on the resulting learned behavior. These guarantees range from weaker (e.g., measuring disagreement with the agent’s decision) to stronger (e.g., providing guarantees on the performance of the learner with respect to a true cost function, either known or unknown). We organize our work by paying particular attention to distinction (1): dividing imitation learning into directly replicating desired behavior (sometimes called behavioral cloning) and learning the hidden objectives of the desired behavior from demonstrations (called inverse optimal control or inverse reinforcement learning [Russell, 1998]). In the latter case, behavior arises as the result of an optimization problem solved for each new instance that the learner faces. In addition to method analysis, we discuss the design decisions a practitioner must make when selecting an imitation learning approach. Moreover, application examples—such as robots that play table tennis [Kober and Peters, 2009], programs that play the game of Go [Silver et al., 2016], and systems that understand natural language [Wen et al., 2015]— illustrate the properties and motivations behind different forms of imitation learning. We conclude by presenting a set of open questions and point towards possible future research directions for machine learning.

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An Algorithmic Perspective on Imitation Learning

In this paper we present a review of the most current avenues of research into Kinect-based elderly care and stroke rehabilitation systems to provide an overview of the state of the art, limitations, and issues of concern as well as suggestions for future work in this direction. The central purpose of this review was to collect all relevant study information into one place in order to support and guide current research as well as inform researchers planning to embark on similar studies or applications. The paper is structured into three main sections, each one presenting a review of the literature for a specific topic. Elderly Care section is comprised of two subsections: Fall detection and Fall risk reduction. Stroke Rehabilitation section contains studies grouped under Evaluation of Kinect’s spatial accuracy, and Kinect-based rehabilitation methods. The third section, Serious and exercise games, contains studies that are indirectly related to the first two sections and present a complete system for elderly care or stroke rehabilitation in a Kinect-based game format. Each of the three main sections conclude with a discussion of limitations of Kinect in its respective applications. The paper concludes with overall remarks regarding use of Kinect in elderly care and stroke rehabilitation applications and suggestions for future work. A concise summary with significant findings and subject demographics (when applicable) of each study included in the review is also provided in table format.

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Systematic review of Kinect applications in elderly care and stroke rehabilitation

Digital games cannot only be used for fun and entertainment. The term “serious games” (SG) denotes digital games serving serious purposes like education, training, advertising, research and health. Recently, a new generation of games has emerged involving whole-body movements. Compared to traditional interventions, these games may help elderly people to improve their health by enhancing physical fitness and coordinative abilities by combining increased motivation, game experience like fun and game flow and training. Serious games, particularly adventure and shooter games, already play an important role in health education, prevention and rehabilitation, e.g. to enhance health-related physical activity, improve sensory–motor coordination, prevent asthma, change nutrition behaviour and alleviate diabetes and prevent smoking or HIV. In this paper, the impact of SG on prevention and rehabilitation is discussed. Three criteria are applied. Beyond effectivity and efficiency, the additional benefits of serious games can be described and explained by different models including social, psychological, physiological and sensory–motor factors. The quality of study serves as a third criterion. Despite first promising results, there are only few high-quality studies. Adequate content, game interfaces, sustainability and appropriate settings are critical factors for the success of SG. In this regard, (sport) science can help to develop and evaluate SG and test appropriate settings that ensure sustainable use of serious games.

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Serious games in prevention and rehabilitation—a new panacea for elderly people?

This textbook provides an introduction to the fundamentals of serious games, which differ considerably from computer games that are meant for pure entertainment. Undergraduate and graduate students from various disciplines who want to learn about serious games are one target group of this book. Prospective developers of serious games are another, as they can use the book for self-study in order to learn about the distinctive features of serious game design and development. And ultimately, the book also addresses prospective users of serious game technologies by providing them with a solid basis for judging the advantages and limitations of serious games in different application areas such as game-based learning, training and simulation or games for health. To cater to this heterogeneous readership and wide range of interests, every effort was made to make the book flexible to use. All readers are expected to study Chapter 1, as it provides the necessary basics and terminology that will be used in all subsequent chapters. The eleven chapters that follow cover the creation of serious games (design, authoring processes and tools, content production), the runtime context of serious games (game engines, adaptation mechanisms, game balancing, game mastering, multi-player serious games), the effects of serious games and their evaluation (player experience, assessment techniques, performance indicators), and serious games in practice (economic aspects, cost-benefit analysis, serious game distribution). To familiarize the readers with best practice in this field, the final chapter presents more than 30 selected examples of serious games illustrating their characteristics and showcasing their practical use. Lecturers can select chapters in a sequence that is most suitable for their specific course or seminar. The book includes specific suggestions for courses such as Introduction to Serious Games, Entertainment Technology, Serious Game Design, Game-based Learning, and Applications of Serious Games.

Serious Games: Foundations, Concepts and Practice

The aim of the present study is to compare the efficiency of traditional and video game based balance training programs. 22 customers of a health care centre (age: M = 47.6 yrs; SD = 13.1) volunteered to participate in the experiment. They were randomly assigned to two experimental groups. One group underwent a traditional training program, while the other group trained using the Nintendo Wii Fit TM Balance Board. Between pre and post test procedures, training sessions were performed three times a week for three weeks. In addition to five balance tests (SEBT, ball-handling, two video games, dynamic balance), a questionnaire was applied concerning mood state, self-efficacy, physical activity enjoyment, flow and subjective experience in order to evaluate psychological effects of the interventions. Two-factor analyses of variance showed both general and differential improvements of the two groups for pre and post test measures. Both groups improved their balance performance in 4 of five tests. The traditional group showed a significantly greater improvement in two tests (SEBT: F(1,20) = 8.907, p = .007, η2 = .308; ball-handling: F(1,20) = 13.578, p = .001, η2 = .404), whereas the Wii group showed a significantly greater improvement in one test (Ski Slalom (F(1,20) = 5.101, p = .035, η2 = .203). Psychological questionnaires revealed neither significant pre-post effects nor differences between the groups for pre and post test measurements. The results confirm that the Nintendo Wii Fit TM may be a suitable medium of training balance in prevention and rehabilitation of adults. Specific effects of training are more pronounced than transfer between virtual and

Comparison of a traditional and a Video Game Based Balance Training Program.

Esports as the competitive play of digital games has gained considerable popularity. However, a comprehensive framework for esport training is still missing. In this paper, a performance model integrating insights from game research and sport science is developed. Based on this model, an online questionnaire was designed and applied to investigate training in different esports regarding relevant competencies and training areas. Overall, 1,835 esports players voluntarily participated in the study. Age ranged from 13 to 47 years (M = 20,9; SD = 4,5), and males clearly dominated (95%). Furthermore, the mean weakly playing time was 20.03 hours (SD = 15.8). Training occupied 38.85% (7.75 h) of the playing time on average. On the one hand, the results reveal game-specific competence and training structures in the five esports selected for the study (Starcraft II, League of Legends, Rocket League, FIFA, and Counter Strike). On the other hand, the factor structure of competencies closely resembles the esports performance model. As a conclusion, esports training methods should always consider the specific competence profile of the respective esports game.

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The structure of performance and training in esports

A group discussion of individuals with expertise working in the field of exergaming and rehabilitation focused on the issue of designing exergames for persons with disabilities as well as appropriate interventions using exergames. The purpose of these discussions was to develop recommendations for the design, evaluation, and application of exergames in therapy serving as potential guidelines for researchers, developers, and therapists. The following key issues were addressed: (1) Challenges in exergame design for persons with disabilities, (2) adaptation of exergames for persons with disabilities, (3) exergame interventions, and (4) future research directions. It is the hope of the group that the results of these recommendations will help improve the quality of exergame design and interventions and thereby increase opportunities for persons with disabilities to engage sustainably in exergaming.

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Josef Wiemeyer

Papers

Serious games in prevention and rehabilitation—a new panacea for elderly people?

Serious Games: Foundations, Concepts and Practice

Comparison of a traditional and a Video Game Based Balance Training Program.

The structure of performance and training in esports

Recommendations for the Optimal Design of Exergame Interventions for Persons with Disabilities: Challenges, Best Practices, and Future Research