Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation

Xu, Guoqing; Gao, Xiang; Pan, Lizheng; Chen, Sheng; Wang, Qiang; Zhu, Bin; Li, Jinfei

doi:10.1177/1729881418806433

Cited by 15 publications

(16 citation statements)

References 28 publications

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“…For instance, Koenig et al [37] and Novak et al [38] used inputs from ANS such as skin-conductance level, heart rate and skin temperature to estimate the engagement and arousal level of stroke patients while performing rehabilitation movements, and proposed their use in developing a closedloop auto-adaptive rehabilitation program. Such systems have been shown to promote the engagement level and participation, and improve the overall user experience [9,10,39], which can increase the rate and amount of recovery from stroke disabilities [8,22,24]. However, stroke patients often show long-lasting abnormalities in ANS, which can alter the functioning of ANS-based auto-adaptive rehabilitation programs [40].…”

Section: Discussionmentioning

confidence: 99%

“…The amount of time spent in actively performing the rehabilitation exercises is positively linked with the rate and amount of recovery from stroke disabilities [ 8 ]. As adaptive rehabilitation programs have shown to promote prolonged training sessions and engagement in the rehabilitation programs [ 9 , 10 ], a training-ErrP-led human-in-the-loop AAN robotic stroke rehabilitation program can provide a higher rate of recovery to stroke patients from their motor impairments in comparison to the existing state-of-the-art AAN robotic stroke rehabilitation programs and standard stroke rehabilitation care [ 4 , 7 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

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The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements

2021

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Background Recently, error-related negativity (ERN) signals are proposed to develop an assist-as-needed robotic stroke rehabilitation program. Stroke patients’ state-of-mind, such as motivation to participate and active involvement in the rehabilitation program, affects their rate of recovery from motor disability. If the characteristics of the robotic stroke rehabilitation program can be altered based on the state-of-mind of the patients, such that the patients remain engaged in the program, the rate of recovery from their motor disability can be improved. However, before that, it is imperative to understand how the states-of-mind of a participant affect their ERN signal. Methods This study aimed to determine the association between the ERN signal and the psychological and cognitive states of the participants. Experiments were conducted on stroke patients, which involved performing a physical rehabilitation exercise and a questionnaire to measure participants' subjective experience on four factors: motivation in participating in the experiment, perceived effort, perceived pressure, awareness of uncompleted exercise trials while performing the rehabilitation exercise. Statistical correlation analysis, EEG time-series and topographical analysis were used to assess the association between the ERN signals and the psychological and cognitive states of the participants. Results A strong correlation between the amplitude of the ERN signal and the psychological and cognitive states of the participants was observed, which indicate the possibility of estimating the said states using the amplitudes of the novel ERN signal. Conclusions The findings pave the way for the development of an ERN based dynamically adaptive assist-as-needed robotic stroke rehabilitation program of which characteristics can be altered to keep the participants’ motivation, effort, engagement in the rehabilitation program high. In future, the single-trial prediction ability of the novel ERN signals to predict the state-of-mind of stroke patients will be evaluated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements

2021

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“…Affective exergames are most prominent in motor rehabilitation, where physiological measurements have been used to estimate psychological states and adjust the difficulty of exercises for both the upper [5,[12][13][14][15][16] and lower limbs [17,18]; they have also been used for general exercise enhancement in several studies [19,20]. However, as affective exergames are more complex than simple performance-based DDA, the question arises: does the additional cost and complexity result in a better user experience?…”

Section: Affective Exergamesmentioning

confidence: 99%

User Experience With Dynamic Difficulty Adjustment Methods for an Affective Exergame: Comparative Laboratory-Based Study

Darzi¹,

McCrea²,

Novak³

2021

JMIR Serious Games

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Background In affective exergames, game difficulty is dynamically adjusted to match the user’s physical and psychological state. Such an adjustment is commonly made based on a combination of performance measures (eg, in-game scores) and physiological measurements, which provide insight into the player’s psychological state. However, although many prototypes of affective games have been presented and many studies have shown that physiological measurements allow more accurate classification of the player’s psychological state than performance measures, few studies have examined whether dynamic difficulty adjustment (DDA) based on physiological measurements (which requires additional sensors) results in a better user experience than performance-based DDA or manual difficulty adjustment. Objective This study aims to compare five DDA methods in an affective exergame: manual (player-controlled), random, performance-based, personality-performance–based, and physiology-personality-performance–based (all-data). Methods A total of 50 participants (N=50) were divided into five groups, corresponding to the five DDA methods. They played an exergame version of Pong for 18 minutes, starting at a medium difficulty; every 2 minutes, two game difficulty parameters (ball speed and paddle size) were adjusted using the participant’s assigned DDA method. The DDA rules for the performance-based, personality-performance–based, and all-data groups were developed based on data from a previous open-loop study. Seven physiological responses were recorded throughout the sessions, and participants self-reported their preferred changes to difficulty every 2 minutes. After playing the game, participants reported their in-game experience using two questionnaires: the Intrinsic Motivation Inventory and the Flow Experience Measure. Results Although the all-data method resulted in the most accurate changes to ball speed and paddle size (defined as the percentage match between DDA choice and participants’ preference), no significant differences between DDA methods were found on the Intrinsic Motivation Inventory and Flow Experience Measure. When the data from all four automated DDA methods were pooled together, the accuracy of changes in ball speed was significantly correlated with players’ enjoyment (r=0.38) and pressure (r=0.43). Conclusions Although our study is limited by the use of a between-subjects design and may not generalize to other exergame designs, the results do not currently support the inclusion of physiological measurements in affective exergames, as they did not result in an improved user experience. As the accuracy of difficulty changes is correlated with user experience, the results support the development of more effective DDA methods. However, they show that the inclusion of physiological measurements does not guarantee a better user experience even if it yields promising results in offline cross-validation.

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“…The adaptive adjustment of the training task is often used for improving patient engagement. The challenge level of training tasks, which is one of the main sub-factors that contribute to engagement, can be adjusted to match a patients' motor abilities by use of training task adaptation (Csikszentmihalyi and Csikzentmihaly, 1990;Yannakakis and Hallam, 2009;Xu et al, 2017Xu et al, , 2018Agarwal and Deshpande, 2019). In 2003, Krebs et al proposed a performance-based progressive robotic therapy method (Krebs et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation

Wang

Ren

et al. 2020

Front. Neurorobot.

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Enhancing patients' engagement is of great benefit for neural rehabilitation. However, physiological and neurological differences among individuals can cause divergent responses to the same task, and the responses can further change considerably during training; both of these factors make engagement enhancement a challenge. This challenge can be overcome by training task optimization based on subjects' responses. To this end, an engagement enhancement method based on human-in-the-loop optimization is proposed in this paper. Firstly, an interactive speed-tracking riding game is designed as the training task in which four reference speed curves (RSCs) are designed to construct the reference trajectory in each generation. Each RSC is modeled using a piecewise function, which is determined by the starting velocity, transient time, and end velocity. Based on the parameterized model, the difficulty of the training task, which is a key factor affecting the engagement, can be optimized. Then, the objective function is designed with consideration to the tracking accuracy and the surface electromyogram (sEMG)-based muscle activation, and the physical and physiological responses of the subjects can consequently be evaluated simultaneously. Moreover, a covariance matrix adaption evolution strategy, which is relatively tolerant of both measurement noises and human adaptation, is used to generate the optimal parameters of the RSCs periodically. By optimization of the RSCs persistently, the objective function can be maximized, and the subjects' engagement can be enhanced. Finally, the performance of the proposed method is demonstrated by the validation and comparison experiments. The results show that both subjects' sEMG-based motor engagement and electroencephalography based neural engagement can be improved significantly and maintained at a high level.

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Anxiety detection and training task adaptation in robot-assisted active stroke rehabilitation

Cited by 15 publications

References 28 publications

The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements

The influence of psychological and cognitive states on error-related negativity evoked during post-stroke rehabilitation movements

User Experience With Dynamic Difficulty Adjustment Methods for an Affective Exergame: Comparative Laboratory-Based Study

Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation

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