Reducing Compensatory Motions in Motion-Based Video Games for Stroke Rehabilitation

Alankuş, Gazihan; Kelleher, Caitlin

doi:10.1080/07370024.2014.985826

Cited by 23 publications

(25 citation statements)

References 32 publications

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“…The extent of trunk movement was highly variable (inter-patient and inter-session), and there was no significant trend over time that would suggest either an increase or a reduction of compensation with the trunk during reaching movements. Providing auditory instructions when excessive trunk movements are detected, simulating the presence of a virtual therapist [ 38 ], or using negative visual cues within the therapy game [ 40 ], might help to prevent compensatory trunk movements.…”

Section: Discussionmentioning

confidence: 99%

Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system

Wittmann

Held

Lambercy

et al. 2016

J NeuroEngineering Rehabil

124

160

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BackgroundThe effect of rehabilitative training after stroke is dose-dependent. Out-patient rehabilitation training is often limited by transport logistics, financial resources and a lack of motivation/compliance. We studied the feasibility of an unsupervised arm therapy for self-directed rehabilitation therapy in patients’ homes.MethodsAn open-label, single group study involving eleven patients with hemiparesis due to stroke (27 ± 31.5 months post-stroke) was conducted. The patients trained with an inertial measurement unit (IMU)-based virtual reality system (ArmeoSenso) in their homes for six weeks. The self-selected dose of training with ArmeoSenso was the principal outcome measure whereas the Fugl-Meyer Assessment of the upper extremity (FMA-UE), the Wolf Motor Function Test (WMFT) and IMU-derived kinematic metrics were used to assess arm function, training intensity and trunk movement. Repeated measures one-way ANOVAs were used to assess differences in training duration and clinical scores over time.ResultsAll subjects were able to use the system independently in their homes and no safety issues were reported. Patients trained on 26.5 ± 11.5 days out of 42 days for a duration of 137 ± 120 min per week. The weekly training duration did not change over the course of six weeks (p = 0.146). The arm function of these patients improved significantly by 4.1 points (p = 0.003) in the FMA-UE. Changes in the WMFT were not significant (p = 0.552). ArmeoSenso based metrics showed an improvement in arm function, a high number of reaching movements (387 per session), and minimal compensatory movements of the trunk while training.ConclusionsSelf-directed home therapy with an IMU-based home therapy system is safe and can provide a high dose of rehabilitative therapy. The assessments integrated into the system allow daily therapy monitoring, difficulty adaptation and detection of maladaptive motor patterns such as trunk movements during reaching.Trial registrationUnique identifier: NCT02098135.

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Section: Discussionmentioning

confidence: 99%

Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system

Wittmann

Held

Lambercy

et al. 2016

J NeuroEngineering Rehabil

124

160

View full text Add to dashboard Cite

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“…Gamification in healthcare is expanding: gamification elements help treat eating disorders in children (Chow, et al, 2020) and adults (Forman, et al, 2019). Gamification is used in stroke rehabilitation therapy to guide rehabilitation exercises (Alankus and Kelleher, 2015), to manage cardiovascular disease patients (Derboven, Voorend and Slegers, 2020), smoking prevention strategies (Luna-Perejon, et al, 2019), and wellbeing programs (Lin and Windasari, 2019).…”

Section: Gamificationmentioning

confidence: 99%

Innovation in project management education - let’s get serious!

Tews¹,

Skulmoski²,

Langston³

et al. 2020

CEB

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More educators use serious games (e.g., games where the primary objective is learning rather than enjoyment) to enhance learning due to benefits such as improved understanding and engagement. However, using serious games within project management education is not well understood. The aim of this research is to investigate project management serious games in higher education: i) determine the extent of gamification in PMI-accredited project management programs, and ii) survey university students about their experiences playing project management serious games. Two separate mixed-method studies reveal insights about serious games in higher education and where innovations may be leveraged. Traditional statistics were used to analyze quantitative data, and coding was used to analyze the qualitative data. The results from a global survey of ten PMI-accredited university programs suggest that serious games are embryonic but promising. A case study at one university reveals that students enjoy learning through games but caution against using games to formally assess students’ learning. The paper concludes with recommendations for further research and development.

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“…After detecting the compensation movement, an appropriate trunk restraint technique should be incorporated to reduce the compensatory trunk movements during the seated reaching tasks. Visual, auditory or haptic feedback was provided to stroke survivors to modify their movement patterns and demonstrate the potential effects of improving upper limb function [53][54][55]. An accurate detection of compensatory movements was achieved using a pressure distribution-based system.…”

Section: Future Workmentioning

confidence: 99%

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

Cai

Zhang

et al. 2019

J NeuroEngineering Rehabil

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Background: Compensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to reduce compensatory trunk movements and improving the motor function of the paretic arm. Sensor-based and camera-based systems have been developed to detect compensatory movements, but they still have some limitations, such as causing object obstructions, requiring complex setups and raising privacy concerns. To overcome these drawbacks, this paper proposes a compensatory movement detection system based on pressure distribution data and is unobtrusive, simple and practical. Machine learning algorithms were applied to classify compensatory movements automatically. Therefore, the purpose of this study was to develop and test a pressure distribution-based system for the automatic detection of compensation movements of stroke survivors using machine learning algorithms. Methods: Eight stroke survivors performed three types of reaching tasks (back-and-forth, side-to-side, and up-anddown reaching tasks) with both the healthy side and the affected side. The pressure distribution data were recorded, and five features were extracted for classification. The k-nearest neighbor (k-NN) and support vector machine (SVM) algorithms were applied to detect and categorize the compensatory movements. The surface electromyography (sEMG) signals of nine trunk muscles were acquired to provide a detailed description and explanation of compensatory movements. Results: Cross-validation yielded high classification accuracies (F1-score>0.95) for both the k-NN and SVM classifiers in detecting compensation movements during all the reaching tasks. In detail, an excellent performance was achieved in discriminating between compensation and noncompensation (NC) movements, with an average F1score of 0.993. For the multiclass classification of compensatory movement patterns, an average F1-score of 0.981 was achieved in recognizing the NC, trunk lean-forward (TLF), trunk rotation (TR) and shoulder elevation (SE) movements. Conclusions: Good classification performance in detecting and categorizing compensatory movements validated the feasibility of the proposed pressure distribution-based system. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to monitor compensation movements automatically by using the pressure distribution-based system when stroke survivors perform seated reaching tasks.

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Reducing Compensatory Motions in Motion-Based Video Games for Stroke Rehabilitation

Cited by 23 publications

References 32 publications

Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system

Self-directed arm therapy at home after stroke with a sensor-based virtual reality training system

Innovation in project management education - let’s get serious!

Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms

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