The Role of Serious Games in Robot Exoskeleton-Assisted Rehabilitation of Stroke Patients

Cornforth, David; Koenig, Alexander; Riener, Robert; August, Katherine G.; Khandoker, Ahsan H.; Karmakar, Chandan; Palaniswami, Marimuthu; Jelinek, Herbert F.

doi:10.1007/978-3-319-05834-4_10

Cited by 5 publications

(5 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, the assessment of these states could be crucial to quantitatively monitoring the level of engagement and motivation of the patients. This can be done using external devices to measure heart rate variability, breathing frequency, skin conductance response, and skin temperature and correlating these measures with kinematic and dynamic information coming from the robot [197].…”

Section: Quantitative Assessment Of the Sensorimotor Performancementioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

View full text Add to dashboard Cite

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots' capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients' recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. The term "robotic technology" in this application domain refers to any mechatronic device with a certain degree of intelligence that can physically intervene on the behavior of the patient, optimizing and speeding up his/her sensorimotor recovery. The two key capabilities of these robots are: (1) Assessing the human sensorimotor function; and (2) re-training the human brain in order to improve the patient's quality of life. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient. Usually, most of the rehabilitation devices implement a passive training modality (robot-driven, position control strategy), where the robot imposes the trajectories, and an active training modality (patient-driven), where the robot modulates its trajectory in response to the subject's intention to move [7,8]. However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walki...

show abstract

Section: Quantitative Assessment Of the Sensorimotor Performancementioning

confidence: 99%

Perspectives and Challenges in Robotic Neurorehabilitation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…To the left of the performance jump are the six Quitters (Player 9, 15, 10, 18. 8, and 17), and to the right are the remainder players (Fulfillers plus Explorers, or non-Quitters: Player 7,19,4,16,5,22,13,20,6,2,and 14). The highest MSI scored by the non-Quitter group is 0.959highly similar to that of the Expert baseline of 1.…”

Section: B Visualizing Performance As Serious Games Analyticsmentioning

confidence: 87%

“…Serious games designed with training and learning in mind can be suitable for acquiring skills not easily taught in classrooms, including cognitive processes such as strategic and analytical thinking, planning and execution, problem solving, decision-making, and adapting to rapid change [5]. This model of serious games is also suitable for extension beyond the traditional classroom for rehabilitation [6], patient choice education [7], and expertise training [8].…”

Section: Introductionmentioning

confidence: 99%

Using players’ Gameplay Action-Decision Profiles to prescribe training: Reducing training costs with Serious Games Analytics

Loh¹,

2018

EasyChair Preprints

View full text Add to dashboard Cite

Player data from Serious Games can be used to produce Serious Games Analytics, which can in turn, be used to measure, assess, and improvement performance. These insights can also be used to support decision-making by Chief Learning Officers in 'prescribing' training – e.g., diagnosing who should receive training, how to design serious games for optimal training, and what content should be included or withheld. Data-driven training prescription can help learning organizations save money by mitigating unnecessary training sessions and identifying what kind of training fits which individual to eventually reduce training costs.We traced player data and calculated the similarity between their course of action (COA) in situ serious games training environments compared to that of the expert’s. We found the combined metrics of Cosine similarity and Maximum Similarity Index to be useful in identifying players’ Gameplay Action-Decision (GAD) profiles. Insights to using GAD profiles as a diagnostic to measure training performance and prescribe training are offered.

show abstract

“…In other words, stroke survivors should only be recommended activities that they are physically able to perform. Ideally, these activities should be challenging to the patients, yet not too challenging, as this can lead to increased stress, despair, or even injury [12]. As the negative impacts of a stroke on the survivors' mobility and the improvements from rehabilitation vary strongly from person to person, the activities have to reflect individual physical capabilities of the stroke survivors.…”

Section: R1: the Design Artifact Has To Communicate Medical Advice Inmentioning

confidence: 99%

“…In order to achieve this, it is necessary for a healthcare professional to select a set of adequate rehabilitation activities (R2), then measure progress against those goals. For instance, feedback from users, consisting of physiological measures such as heart rate and skin conductance, has been shown to permit the adjustment of difficulty in real-time during stroke rehabilitation in a study by Jelinek and colleagues [12]. Such measures can be used to assess the mood of the user (see p. xiv, [13] for some detailed discussion on content validity and construct validity in the context of physiological measurements), and then adjust the difficulty of a video game used in the context of stroke rehabilitation.…”

Section: R4mentioning

confidence: 99%

Empathic Avatars in Stroke Rehabilitation: A Co-designed mHealth Artifact for Stroke Survivors

Aljaroodi

Adam

Chiong

et al. 2017

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Stroke is the second highest cause of death and disability worldwide. While rehabilitation programs are intended to support stroke survivors, and promote recovery after they leave the hospital, current rehabilitation programs typically provide only static written instructions and lack the ability to keep them engaged with the program. In this design science research paper, we present an mHealth artifact that builds on behavior change theory to increase stroke survivors' engagement in rehabilitation programs. We employed a co-design methodology to identify design requirements for the stroke rehabilitation mHealth artifact, addressing stroke survivors' needs and incorporating expertise of healthcare providers. Guided by these requirements, we developed design principles for the artifact pertaining to visual assets that are essential in immersing users in the design. We carried out a two-stage development process by having workshops and interviews with experts. Following this, a prototype was developed and evaluated in a series of workshops with multiple stakeholders.

show abstract

The Role of Serious Games in Robot Exoskeleton-Assisted Rehabilitation of Stroke Patients

Cited by 5 publications

References 56 publications

Perspectives and Challenges in Robotic Neurorehabilitation

Perspectives and Challenges in Robotic Neurorehabilitation

Using players’ Gameplay Action-Decision Profiles to prescribe training: Reducing training costs with Serious Games Analytics

Empathic Avatars in Stroke Rehabilitation: A Co-designed mHealth Artifact for Stroke Survivors

Contact Info

Product

Resources

About