Digitalization of Training Tasks and Specification of the Behaviour of a Social Humanoid Robot as Coach

Forbrig, Peter; Bundea, Alexandru; Pedersen, Ann Louise; Platz, Thomas

doi:10.1007/978-3-030-64266-2_3

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…We have been worked on a domain-domain specific language that allows the generation of the Python scripts (see e.g. [6]). A visualization tool for hierarchical state diagrams allows the identification of errors like missing transitions or wrong guards.…”

Section: Related Workmentioning

confidence: 99%

Using a Humanoid Robot to Assist Post-stroke Patients with Standardized Neurorehabilitation Therapy

Forbrig

Bundea

Pedersen

et al. 2021

Lecture Notes in Networks and Systems

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Worldwide the number of people living with stroke-related disability is increasing. Neurorehabilitation, e.g. training therapy provided by occupational and physiotherapists helps to reduce impairment and activity limitations effectively. Yet, to cope with the increasing demands, the number of physiotherapists and occupational therapists is not sufficient. The paper proposes the hypothesis that a social humanoid robot might serve as therapeutic assistant for patients during standardized training sessions, after therapists have evaluated a patient's needs, decided on and have the patient made acquainted with an individualized training program. It is first described what kind of training tasks is intended to be supported by a social humanoid robot as training assistant. Second, digitalization of those tasks is presented. Third, the process of building user models for patients and helping persons using the repertory grid approach is discussed. Finally, opportunities for motivating interactions based on these models are mentioned.

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Section: Related Workmentioning

confidence: 99%

Using a Humanoid Robot to Assist Post-stroke Patients with Standardized Neurorehabilitation Therapy

Forbrig

Bundea

Pedersen

et al. 2021

Lecture Notes in Networks and Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Digital transformation has opened avenues for improving services, products, and interventions through the increased use of digital technologies [ 10 , 11 ]. In the project evidence-based robotic assistance in neurorehabilitation (E-BRAiN), a robot was developed to support daily therapy practice [ 12 ]. Researchers investigated the use of the humanoid robot “Pepper” by SoftBank Robotics owing to interaction [ 13 ], motivation, software and hardware capabilities, and acceptance.…”

Section: Introductionmentioning

confidence: 99%

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment

Fischer

Mühlbacher

2023

JMIR Res Protoc

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Background Strokes pose a particular challenge to the health care system. Although stroke-related mortality has declined in recent decades, the absolute number of new strokes (incidence), stroke deaths, and survivors of stroke has increased. With the increasing need of neurorehabilitation and the decreasing number of professionals, innovations are needed to ensure adequate care. Digital technologies are increasingly used to meet patients’ unfilled needs during their patient journey. Patients must adhere to unfamiliar digital technologies to engage in health interventions. Therefore, the acceptance of the benefits and burdens of digital technologies in health interventions is a key factor in implementing these innovations. Objective This study aims to describe the development of a discrete choice experiment (DCE) to weigh criteria that impact patient and public acceptance. Secondary study objectives are a benefit-burden assessment (estimation of the maximum acceptable burden of technical features and therapy-related characteristics for the patient or individual, eg, no human contact), overall comparison (assessment of the relative importance of attributes for comparing digital technologies), and adherence (identification of key attributes that influence patient adherence). The exploratory objectives include heterogeneity assessment and subgroup analysis. The methodological aims are to investigate the use of DCE. Methods To obtain information on the criteria impacting acceptance, a DCE will be conducted including 7 attributes based on formative qualitative research. Patients with stroke (experimental group) and the general population (control group) are surveyed. The final instrument includes 6 best-best choice tasks in partial design. The experimental design is a fractional-factorial efficient Bayesian design (D-error). A conditional logit regression model and mixed logistic regression models will be used for analysis. To consider the heterogeneity of subgroups, a latent class analysis and an analysis of heteroscedasticity will be performed. Results The literature review, qualitative preliminary study, survey development, and pretesting were completed. Data collection and analysis will be completed in the last quarter of 2023. Conclusions Our results will inform decision makers about patients’ and publics’ acceptance of digital technologies used in innovative interventions. The patient preference information will improve decisions regarding the development, adoption, and pricing of innovative interventions. The behavioral changes in the choice of digital intervention alternatives are observable and can therefore be statistically analyzed. They can be translated into preferences, which define the value. This study will investigate the influences on the acceptance of digital interventions and thus support decisions and future research. International Registered Report Identifier (IRRID) DERR1-10.2196/46056

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MUMR-MIODMIT: A Generic Architecture Extending Standard Interactive Systems Architecture to Address Engineering Issues for Rehabilitation

Carayon,

Martinie,

Palanque

2024

Lecture Notes in Computer Science

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Digitalization of Training Tasks and Specification of the Behaviour of a Social Humanoid Robot as Coach

Cited by 6 publications

References 15 publications

Using a Humanoid Robot to Assist Post-stroke Patients with Standardized Neurorehabilitation Therapy

Using a Humanoid Robot to Assist Post-stroke Patients with Standardized Neurorehabilitation Therapy

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment

MUMR-MIODMIT: A Generic Architecture Extending Standard Interactive Systems Architecture to Address Engineering Issues for Rehabilitation

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