First for Stroke: using the Microsoft ‘Kinect’ as a facial paralysis stroke rehabilitation tool

Breedon, Philip; Russell, Adam; Logan, Pip; Newell, Ossie; O’Brien, Becca; Edmans, Judi; Baines, Darrin; Hall, Patrick T.

doi:10.5334/ijic.1760

Cited by 8 publications

(7 citation statements)

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“…In a similar way, current facial rehabilitation techniques (targeting people suffering from facial nerve paralysis) focus either on mime therapy (series of massage and exercises where the patient does FEs) or EMG biofeedback therapy (see [22], [23]). For instance, Breedon et al [4] recently developed Face to Face, a therapeutic game where patients try to match a FE displayed on a screen while their own FEs are tracked by a Kinect.…”

Section: B Voluntary Facial Expression and Facial Therapymentioning

confidence: 99%

Evaluation of facial expressions as an interaction mechanism and their impact on affect, workload and usability in an AR game

Transon

Verhulst

Normand

et al. 2017

2017 23rd International Conference on Virtual System &Amp; Multimedia (VSMM)

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With the recent development of Head Mounted Display (HMD) for Virtual Reality (VR) allowing to track and recognize user's Facial Expression (FE)s in real-time, we investigated the impact that the use of FEs as an action-trigger input mechanism (e.g. a FE mapped to a single action) has on our emotional state; as well as their workload and usability compared to the use of a controller button. We developed an Augmented Reality (AR)-based memory card where the users select virtual cards using a wand and flip them using either a FE (smiling; frowning) or a Xbox controller button. The users were separated into three groups: (1) flipping the card with a smile (n = 10); (2) flipping the card with a frown (n = 8) and (3) flipping the cards with the Xbox controller button (n = 11). We did not see any significant differences between our groups in: (i) the positive and negative affect of the participants and (ii) the reported workload and usability, thus highlighting that the FEs could be used inside a HMD in the same way as a controller button.

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Section: B Voluntary Facial Expression and Facial Therapymentioning

confidence: 99%

Evaluation of facial expressions as an interaction mechanism and their impact on affect, workload and usability in an AR game

Transon

Verhulst

Normand

et al. 2017

2017 23rd International Conference on Virtual System &Amp; Multimedia (VSMM)

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“…While the facial analysis capabilities of Kinect have been used successfully in delivering rehabilitation exercise regimens and feedback (see [16] for example), its use to translate digital tracking data into health outcome measures that can track treatment effects has been less well studied. However, the potential to achieve this is well demonstrated by these approaches.…”

Section: Facial Analysismentioning

confidence: 99%

“…In this section, we review a number of published examples of solutions for objective measurement of movement using Microsoft Kinect focusing on the areas of gait and balance [11,[22][23], upper extremity movement [24][25][26], chest wall motion analysis [27] and facial analysis [16] (Table V). Our aim is not to provide a comprehensive review, but to illustrate the growing application and potential of the Kinect platform to enable objective clinical endpoint assessment.…”

Section: Measuring Objective Health Outcomes Using Microsoft Kinectmentioning

confidence: 99%

“…However, the potential to achieve this is well demonstrated by these approaches. For example, the Face to Face solution [16] (Figure 3), funded by the UK NIHR Invention for Innovation Programme, is a rehabilitation system for facial paralysis in stroke patients. It recognizes facial expressions by tracking movement across the face and applying the recognized motion onto an onscreen representation of the user.…”

Section: Facial Analysismentioning

confidence: 99%

“…However, this has not limited the application of this technology in clinical and home health care applications, especially within the rehabilitation sector where platforms such as Neuroforma (Titanis Sp. z o.o., Warsaw, Poland) [14], JINTRONIX (Jintronix, Montreal, Canada) [15], and Face To Face [16] have recently been developed; therefore suggesting that 3D cameras and sensors will continue to be used in clinical platforms for the foreseeable future.…”

Section: E Xtion Pro Livementioning

confidence: 99%

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Enhancing the Measurement of Clinical Outcomes Using Microsoft Kinect

Breedon¹,

Byrom²,

Siena³

et al. 2016

2016 International Conference on Interactive Technologies and Games (ITAG)

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Abstract-There is a growing body of applications leveraging Microsoft Kinect and the associated Windows Software Development Kit in health and wellness. In particular, this platform has been valuable in developing interactive solutions for rehabilitation including creating more engaging exercise regimens and ensuring that exercises are performed correctly for optimal outcomes. Clinical trials rely upon robust and validated methodologies to measure health status and to detect treatment-related changes over time to enable the efficacy and safety of new drug treatments to be assessed and measured. In many therapeutic areas, traditional outcome measures rely on subjective investigator and patient ratings. Subjective ratings are not always sensitive to detecting small improvements, are subject to inter-and intra-rater variability and limited in their ability to record detailed or subtle aspects of movement and mobility. For these reasons, objective measurements may provide greater sensitivity to detect treatment-related changes where they exist.In this review paper, we explore the use of the Kinect platform to develop low-cost approaches to objectively measure aspects of movement. We consider published applications that measure aspects of gait and balance, upper extremity movement, chest wall motion and facial analysis. In each case, we explore the utility of the approach for clinical trials, and the precision and accuracy of estimates derived from the Kinect output.We conclude that the use of games platforms such as Microsoft Kinect to measure clinical outcomes offer a versatile, easy to use and low-cost approach that may add significant value and utility to clinical drug development, in particular in replacing conventional subjective measures and providing richer information about movement than previously possible in large scale clinical trials, especially in the measurement of gross spatial movements. Regulatory acceptance of clinical outcomes collected in this way will be subject to comprehensive assessment of validity and clinical relevance, and this will require good quality peer-reviewed publications of scientific evidence.

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SVM classification of facial functions based on facial landmarks and animation Units

Gaber

Taher

Wahed

et al. 2021

Biomed. Phys. Eng. Express

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Quantitative assessment and classification of facial paralysis (FP) are essential for treatment selection and progress evaluation of the condition. As part of a comprehensive framework towards this goal, this study aims to classify five normal facial functions: smiling, eye closure, raising the eyebrows, blowing cheeks, and whistling as well as the rest state. 3D facial landmarks and facial animation units (FAUs) were obtained using the Kinect V2, a fast and cost-effective depth camera. These were used to compute the features used in a Support Vector Machine (SVM) classifier. A dataset of 1650 records from 50 normal subjects was compiled for this study. The performances of different SVM kernel models were tested with different feature groups. The best performance (Accuracy=96.7%, Sensitivity=90.2%, and Specificity=98%) was found when using the RBF kernel model applied on just nine differences in FAUs. This research will be developed and extended to include FP classification.

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First for Stroke: using the Microsoft ‘Kinect’ as a facial paralysis stroke rehabilitation tool

Cited by 8 publications

References 0 publications

Evaluation of facial expressions as an interaction mechanism and their impact on affect, workload and usability in an AR game

Evaluation of facial expressions as an interaction mechanism and their impact on affect, workload and usability in an AR game

Enhancing the Measurement of Clinical Outcomes Using Microsoft Kinect

SVM classification of facial functions based on facial landmarks and animation Units

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