Real-Time 4D Facial Sensing and Modelling

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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.