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

Max Ortiz-Catalan, Nichlas Sander, Morten B Kristoffersen, Bo Håkansson, Rickard Brånemark, Max Ortiz-Catalan, Nichlas Sander, Morten B Kristoffersen, Bo Håkansson, Rickard Brånemark

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.

Keywords: augmented reality; electromyography; myoelectric control; neurorehabilitation; pattern recognition; phantom limb pain; virtual reality.

Figures

Figure 1
Figure 1
Setup for the myoelectrically controlled augmented reality environment (MCARE). (A) Surface electrodes and a fiduciary marker placed at the stump. (B) Environment captured by the webcam and displayed on a computer screen, with the addition of the virtual limb superimposed on the fiduciary marker. (C) Patient playing a racing game in which he drives the car by phantom motions (Trackmania Nations Forever, free version). (D) Patient using the Target Achievement Control (TAC) test as a rehabilitation tool.
Figure 2
Figure 2
Evolution of pain intensity over time. (A) The distribution of pain intensity over time shows that at the beginning of the treatment, the patient had a sustained level of pain (~30%) during more than half of the time, and periods with higher levels of pain the rest of the time. Over the course of the treatment, a reduction of time at higher pain intensity levels was reported, as well as the appearance of periods of lower or absent pain. (B) The sustained level of pain was also the lowest pain perceived by the patient, and it gradually decreased to around 10% over the course of the interventions. Episodes of reduced pain started occurring after 4 weeks of treatment and gradually became pain-free periods. In week 11, a problem with his socket prosthesis caused him to use an old, tighter socket that had previously been shown to induce pain.
Figure 3
Figure 3
Offline accuracy. The offline discrimination accuracy over time is presented in box plots where the central mark represents the median value; the edges of the box are the 25th and 75th percentiles; the whiskers give the range of data values; “*” represent average values.

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