Real-Time 4D Facial Sensing and Modelling

慢性面瘫是由贝尔氏麻痹症和中风等病症引起的，保守估计，英国每年有51640名新患者。根据英国贝尔氏麻痹患者和需要专科治疗的残弱患者（约30%）的发病率数据估计，治疗所有新患者的费用可能在每年2,543,000英镑（仅治疗）至7,948,000英镑（治疗加肉毒杆菌毒素注射）之间。大约30%的面瘫患者患有持续的慢性毁容、焦虑和/或抑郁。研究表明，为这些病人提供的服务是有限的。面部麻痹的治疗是昂贵的。随着NHS要求每年节省数十亿美元（NHS， 2011年），重新思考传统治疗途径势在必行。NHS国家中风物理治疗临床指南建议面瘫患者每天进行45分钟的面部治疗。为了达到指导标准，每位患者需要每天面对面治疗至少12周（广义上定义为急性期），每位患者要花费NHS 2400英镑。每年有26000个新病例，这意味着除了治疗现有患者的费用外，这些新患者每年要花费6240万英镑。然而，开发一种家庭康复技术可以降低成本，让更多的病人接受黄金标准的治疗。所提出的技术将为患者在家中接受治疗时提供实时反馈，从而可以显著减少拜访治疗师进行面对面反馈的时间。该项目将研究4D（动态三维）技术和集成镜像视觉反馈MVF理论的计算模型，用于使用深度传感器的家庭治疗。该框架设想开发易于使用的面瘫治疗技术，该技术可以提供实时反馈，评估患者整合MVF理论的治疗反应。利用MVF治疗幻肢痛和复杂区域疼痛综合征的研究已经取得了可喜的成果。面瘫患者如果多锻炼面部肌肉，恢复得更快。但是，面对镜子时，患者会因为看到不对称和变形的面部特征而感到焦虑，因此会感到痛苦。因此，我们将使用MVF自适应地反映面部健康的一面，并在不健康的一面上进行面部运动，从而使患者在锻炼面部肌肉时可以观察到健康的整张脸。据报道，基于MVF的生物反馈疗法对贝尔面瘫和三叉神经痛的半面痛有效。然而，这些研究要么使用物理镜像盒，要么使用简单的基于图像的映射，这提供了很少的反馈或不准确的面部运动信息。患者对面部异常运动的意识有限，因此如果没有对这些运动的反馈，他们的面部功能可能会恶化，形成永久性的异常运动。因此，迫切需要新的计算4D传感和建模方法来开发能够根据MVF捕获和绘制准确面部肌肉运动的治疗方法。最终目标是将这种方法编程成一个软件包，用于家庭治疗。目前，还没有基于MVF的3D或4D产品或技术可用于治疗。因此，所提出的构建集成MVF的计算4D传感和建模模型的方法将通过在评估患者的治疗反应中提供适当的反馈，并最终提高规模化家庭治疗的能力，从而对患者和NHS大有裨益。它也可以适应远程康复的实际应用，使临床顾问和治疗师可以远程观察病人的家庭治疗。

Chronic Facial Palsy is caused by conditions including Bell's palsy and Stroke, afflicting, at a conservative estimate, 51,640 new people per year in the UK. Estimates based on UK incidence data for Bell's palsy patients and those left with residual weakness requiring specialist management (approximately 30%) suggests that to treat all new patients could cost between £2,543,000 (therapy alone) to £7,948,000 (therapy plus botulinum toxin injections) per annum. Approximately 30% of patients affected by facial palsy suffer ongoing chronic disfigurement, anxiety and/or depression. It has been shown that service provision for these patients is limited. Facial palsy management is expensive. With the NHS requiring to save billions annually (NHS, 2011), it is imperative to rethink conventional treatment pathways. The NHS National Clinical Guidelines for Stroke physiotherapy recommends 45 minutes daily facial therapy for patients with facial paralysis. To meet the guidelines, each patient would need daily face-to-face therapy for a period of at least 12 weeks (broadly defined as the acute phase), costing the NHS £2,400 per patient. With 26,000 new cases annually this would represent a prohibitive cost of £62,400,000 for these new patients per annum on top of the cost of treating existing patients. However, costs could be reduced by developing a home-based rehabilitative technology, allowing greater numbers of patients to receive gold-standard treatment. The proposed technologies will provide patients with real-time feedback when undergoing therapy at home and thus can significantly reduce the time of visiting therapists for face-to-face feedback.This project will investigate 4D (dynamic three-dimension) techniques and a computational model integrating Mirror Visual Feedback MVF theory for home-based therapy using a depth sensor. This framework envisions developing easy to use facial palsy therapy technologies, which can provide real-time feedback assessing treatment responses of patients integrating MVF theory. Research studies using MVF therapy to treat phantom limb pain and complex regional pain syndrome have produced promising results. Facial palsy patients can recover more quickly if they exercise their facial muscles. However, it can be painful for patients to face mirrors due to the anxiety of looking at their asymmetric and deformed facial features. Therefore, we will apply MVF to adaptively mirror the healthy side of the face and facial movement over the unhealthy side and thus allow patients to observe healthy whole faces when exercising their facial muscles. It has been reported that biofeedback therapy based on MVF has been effective for Bell's facial palsy and hemi-facial pain of trigeminal neuralgia. However, these studies either use a physical mirror box or simple image-based mapping, which provide little feedback or inaccurate facial movement information. Patients have limited awareness of the abnormal movements their faces display so without feedback about these movements their facial functions may worsen, developing permanently abnormal movements. Therefore, there is a strong need for novel computational 4D sensing and modelling methods to develop therapy which can capture and map accurate facial muscle movements according to MVF. The ultimate goal is to programme this method into a software package for home-based therapy. Currently, there are no 3D or 4D products or technologies based on MVF available for therapy. Thus, the proposed methods for building computational 4D sensing and modelling models integrating MVF will be hugely beneficial to patients and the NHS by providing appropriate feedback in assessing treatment responses of patients and ultimately improve the ability to scale home-based therapy. It can also be adapted to tele-rehabilitation for practical applications so clinical consultants and therapists can remotely observe patients' home-based therapy.