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

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

doi:10.1007/978-981-16-6369-7_3

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Through medical and adequate neurorehabilitative treatment, the disabilities can be sustainably reduced, and patients can regain more independence in their daily lives. This requires more capacities of physiotherapists, occupational therapists, or neuropsychologists, which are currently not available in the healthcare system [9].…”

Section: Clinical Backgroundmentioning

confidence: 99%

Which body functions and activities matter for stroke patients? Study protocol for best–Worst scalings to value core elements of the International Classification of Functioning, Disability and Health

Juhnke,

Mühlbacher

2023

PLoS ONE

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Background Stroke is a common, serious, and disabling healthcare problem with increasing incidence and prevalence. Following a stroke, identifying the factors associated with decisions about rehabilitation interventions is important to assess rehabilitation after stroke. The aim is to guide clinical staff to make patient-centered decisions. Fundamentally, decision makers cannot draw on evidence to consider the relevance of distinct functions and activities from the patient’s perspective. Until now, outcomes of rehabilitation are generally categorized using the International Classification of Functioning, Disability and Health (ICF). This can be seen as a conceptual basis for the assessment of health and disability. Since the ICF does not distinguish importance between these aspects there is a need to value the most important clinical factors as well as related activities from a patients and public perspective to help guide therapists in effectively designing post-acute rehabilitation care for individuals following stroke. The research question is which ICF body functions and activities are of value to stroke patients? Which trade-offs are patients willing to make within the core elements? Health preference research (HPR) answers the need to develop additional preference weights for certain ICF dimensions. Patient preference information (PPI) values health conditions based on the ICF from a patient perspective. Methods In this study we conduct three best–worst scaling (BWS) experiments to value body function and activities from patients’ and public perspective. Out of all ICF dimensions this research covers health conditions relevant to stroke patients in terms of body function, perception, and activities of daily living. Stroke patients as well as members of the general population will be recruited to participate in the online BWS surveys. Fractional, efficient designs are applied regarding the survey design. Conditional and multinominal logit analyses will be used as the main analysis method, with the best–worst count analysis as a secondary analysis. The survey is being piloted prior to commencing the process of data collection. Results are expected by the autumn of 2023. Discussion The research will add to the current literature on clinical decision-making in stroke rehabilitation and the value of certain body functions as well as related activities in neurorehabilitation. Moreover, the study will show whether body functions and activities that are currently equally weighted in international guidelines are also equally important from the point of view of those affected, or whether there are disconcordances in terms of differences between public judgements and patients’ preferences.

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Section: Clinical Backgroundmentioning

confidence: 99%

Which body functions and activities matter for stroke patients? Study protocol for best–Worst scalings to value core elements of the International Classification of Functioning, Disability and Health

Juhnke,

Mühlbacher

2023

PLoS ONE

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“…In addition, HRs were used to improve social skills for children with autism spectrum disorders (Mengoni et al, 2017) or as coaches for physical exercises to promote arm function in children with cerebral palsy (Martin et al, 2020) or stretching exercises for low back pain relief (Blanchard et al, 2022). Furthermore, HRs have been designed and used to assist poststroke patients in performing exercises during their rehabilitation process, at times for over extended periods of time (Koren et al, 2022), or with applications that were designed to provide human-like comprehensive guidance and interactions during therapeutic sessions (Forbrig et al, 2022). For such training-based therapy, therapeutic interactions, now provided by an HR, is a critical element.…”

Section: Introductionmentioning

confidence: 99%

Feasibility, coverage, and inter-rater reliability of the assessment of therapeutic interaction by a humanoid robot providing arm rehabilitation to stroke survivors using the instrument THER-I-ACT

2023

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Objective: The instrument THERapy-related InterACTion (THER-I-ACT) was developed to document therapeutic interactions comprehensively in the human therapist–patient setting. Here, we investigate whether the instrument can also reliably be used to characterise therapeutic interactions when a digital system with a humanoid robot as a therapeutic assistant is used.Methods:Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis [n = 9] or arm ability training (AAT) for mild arm paresis [n = 8]) using the digital therapy system E-BRAiN over a course of nine sessions. Analysis of the therapeutic interaction: A total of 34 therapy sessions were videotaped. All therapeutic interactions provided by the humanoid robot during the first and the last (9th) session of daily training were documented both in terms of their frequency and time used for that type of interaction using THER-I-ACT. Any additional therapeutic interaction spontaneously given by the supervising staff or a human helper providing physical assistance (ABT only) was also documented. All ratings were performed by two trained independent raters.Statistical analyses: Intraclass correlation coefficients (ICCs) were calculated for the frequency of occurrence and time used for each category of interaction observed.Results: Therapeutic interactions could comprehensively be documented and were observed across the dimensions provision of information, feedback, and bond-related interactions. ICCs for therapeutic interaction category assessments from 34 therapy sessions by two independent raters were high (ICC ≥0.90) for almost all categories of the therapeutic interaction observed, both for the occurrence frequency and time used for categories of therapeutic interactions, and both for the therapeutic interaction performed by the robot and, even though much less frequently observed, additional spontaneous therapeutic interactions by the supervisory staff and a helper being present. The ICC was similarly high for an overall subjective rating of the concentration and engagement of patients (0.87).Conclusion: Therapeutic interactions can comprehensively and reliably be documented by trained raters using the instrument THER-I-ACT not only in the traditional patient–therapist setting, as previously shown, but also in a digital therapy setting with a humanoid robot as the therapeutic agent and for more complex therapeutic settings with more than one therapeutic agent being present.

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“…The digital therapy system Evidence-Based Robot-Assistant in Neurorehabilitation (E-BRAiN) ( https://www.ebrain-science.de/en/home/ ) that was used in this research project allows a humanoid robot to lead stroke survivors receiving rehabilitation treatment through therapeutic sessions, to give instructions for carefully selected training exercises, provide feedback, and support their motivation. In this context, the robot’s task is not to take therapeutic decisions, but to autonomously continue a repetitive training schedule once it has been decided on, which was individually adapted and introduced to a stroke survivor by a human therapist ( Forbrig et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the therapeutic interaction provided by a humanoid robot serving stroke survivors as a therapeutic assistant for arm rehabilitation

Platz

Pedersen²,

Deutsch³

et al. 2023

Front. Robot. AI

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Objective: To characterize a socially active humanoid robot’s therapeutic interaction as a therapeutic assistant when providing arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis or arm ability training (AAT) for mild arm paresis) to stroke survivors when using the digital therapeutic system Evidence-Based Robot-Assistant in Neurorehabilitation (E-BRAiN) and to compare it to human therapists’ interaction.Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., ABT [n = 9] or AAT [n = 8]) using E-BRAiN over a course of nine sessions and twenty-one other stroke survivors receiving arm rehabilitation sessions (i.e., ABT [n = 6] or AAT [n = 15]) in a conventional 1:1 therapist–patient setting. Analysis of therapeutic interaction: Therapy sessions were videotaped, and all therapeutic interactions (information provision, feedback, and bond-related interaction) were documented offline both in terms of their frequency of occurrence and time used for the respective type of interaction using the instrument THER-I-ACT. Statistical analyses: The therapeutic interaction of the humanoid robot, supervising staff/therapists, and helpers on day 1 is reported as mean across subjects for each type of therapy (i.e., ABT and AAT) as descriptive statistics. Effects of time (day 1 vs. day 9) on the humanoid robot interaction were analyzed by repeated-measures analysis of variance (rmANOVA) together with the between-subject factor type of therapy (ABT vs. AAT). The between-subject effect of the agent (humanoid robot vs. human therapist; day 1) was analyzed together with the factor therapy (ABT vs. AAT) by ANOVA.Main results and interpretation: The overall pattern of the therapeutic interaction by the humanoid robot was comprehensive and varied considerably with the type of therapy (as clinically indicated and intended), largely comparable to human therapists’ interaction, and adapted according to needs for interaction over time. Even substantially long robot-assisted therapy sessions seemed acceptable to stroke survivors and promoted engaged patients’ training behavior.Conclusion: Humanoid robot interaction as implemented in the digital system E-BRAiN matches the human therapeutic interaction and its modification across therapies well and promotes engaged training behavior by patients. These characteristics support its clinical use as a therapeutic assistant and, hence, its application to support specific and intensive restorative training for stroke survivors.

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

Cited by 5 publications

References 13 publications

Which body functions and activities matter for stroke patients? Study protocol for best–Worst scalings to value core elements of the International Classification of Functioning, Disability and Health

Which body functions and activities matter for stroke patients? Study protocol for best–Worst scalings to value core elements of the International Classification of Functioning, Disability and Health

Feasibility, coverage, and inter-rater reliability of the assessment of therapeutic interaction by a humanoid robot providing arm rehabilitation to stroke survivors using the instrument THER-I-ACT

Analysis of the therapeutic interaction provided by a humanoid robot serving stroke survivors as a therapeutic assistant for arm rehabilitation

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