title: Treatment of phantom limb pain (PLP) based on augmented reality and gaming controlled by myoelectric pattern recognition: a case study of a chronic PLP patient.
abstract: A variety of treatments have been historically used to alleviate phantom limb pain (PLP) with varying efficacy. Recently, virtual reality (VR) has been employed as a more sophisticated mirror therapy. Despite the advantages of VR over a conventional mirror, this approach has retained the use of the contralateral limb and is therefore restricted to unilateral amputees. Moreover, this strategy disregards the actual effort made by the patient to produce phantom motions. In this work, we investigate a treatment in which the virtual limb responds directly to myoelectric activity at the stump, while the illusion of a restored limb is enhanced through augmented reality (AR). Further, phantom motions are facilitated and encouraged through gaming. The proposed set of technologies was administered to a chronic PLP patient who has shown resistance to a variety of treatments (including mirror therapy) for 48 years. Individual and simultaneous phantom movements were predicted using myoelectric pattern recognition and were then used as input for VR and AR environments, as well as for a racing game. The sustained level of pain reported by the patient was gradually reduced to complete pain-free periods. The phantom posture initially reported as a strongly closed fist was gradually relaxed, interestingly resembling the neutral posture displayed by the virtual limb. The patient acquired the ability to freely move his phantom limb, and a telescopic effect was observed where the position of the phantom hand was restored to the anatomically correct distance. More importantly, the effect of the interventions was positively and noticeably perceived by the patient and his relatives. Despite the limitation of a single case study, the successful results of the proposed system in a patient for whom other medical and non-medical treatments have been ineffective justifies and motivates further investigation in a wider study.
authors.affiliation: Biomedical Engineering Division, Department of Signals and Systems, Chalmers University of Technology Gothenburg, Sweden ; Department of Orthopaedics, Centre of Orthopaedic Osseointegration, Sahlgrenska University Hospital Gothenburg, Sweden.
authors.forename: Max
authors.initials: M
authors.lastname: Ortiz-Catalan
authors.affiliation: Biomedical Engineering Division, Department of Signals and Systems, Chalmers University of Technology Gothenburg, Sweden.
authors.forename: Nichlas
authors.initials: N
authors.lastname: Sander
authors.affiliation: Biomedical Engineering Division, Department of Signals and Systems, Chalmers University of Technology Gothenburg, Sweden ; Department of Orthopaedics, Centre of Orthopaedic Osseointegration, Sahlgrenska University Hospital Gothenburg, Sweden.
authors.forename: Morten B
authors.initials: MB
authors.lastname: Kristoffersen
authors.affiliation: Biomedical Engineering Division, Department of Signals and Systems, Chalmers University of Technology Gothenburg, Sweden.
authors.forename: Bo
authors.initials: B
authors.lastname: Håkansson
authors.affiliation: Department of Orthopaedics, Centre of Orthopaedic Osseointegration, Sahlgrenska University Hospital Gothenburg, Sweden.
authors.forename: Rickard
authors.initials: R
authors.lastname: Brånemark
keywords: augmented reality; electromyography; myoelectric control; neurorehabilitation; pattern recognition; phantom limb pain; virtual reality