Voluntary phantom hand and finger movements in transhumerai amputees could be used to naturally control polydigital prostheses

Abstract

An arm amputation is extremely invalidating since many of our daily tasks require bi-manual and precise control of hand movements. Perfect hand prostheses should therefore offer a natural, intuitive and cognitively simple control over their numerous biomimetic active degrees of freedom. While efficient polydigital prostheses are commercially available, their control remains complex to master and offers limited possibilities, especially for high amputation levels. In this pilot study, we demonstrate the possibility for upper-arm amputees to intuitively control a polydigital hand prosthesis by using surface myoelectric activities of residual limb muscles (sEMG) associated with phantom limb movements, even if these residual arm muscles on which the phantom activity is measured were not naturally associated with hand movements before amputation. Using pattern recognition methods, three arm amputees were able, without training, to initiate 5-8 movements of a robotic hand (including individual finger movements) by simply mobilizing their phantom limb while the robotic hand was mimicking the action in real time. This innovative control approach could offer to numerous upper-limb amputees an access to recent biomimetic prostheses with multiple controllable joints, without requiring surgery or complex training; and might deeply change the way the phantom limb is apprehended by both patients and clinicians.

Phantom Movement Training Without Classifier Performance Feedback Improves Mobilization Ability While Maintaining EMG Pattern Classification

“…The present study confirmed our previous results ( [12], [13], [21]) that phantom mobility is classifiable, even before training, with overall classification success rates that are comparable to values found in the literature, using other methods in a clinical setting where amputees trained the PR controller using static muscle contractions, sometimes with additional instructions such as varying EMG amplitudes [31], [35]. If so, is phantom training at home useful?…”

“…The kinematics of the mimicked phantom finger movements were recorded on the intact hand using a Cyberglove® II, at a frequency of 100 Hz. Details on the calibration of the Cyberglove have been reported previously [12], [22]. Global hand movements around the wrist were recorded with an inertial measurement unit (IMU, nine degree-of-freedom Sensor Stick from Sparkfun ®) that was attached to the dorsal surface of the glove.…”

“…Voluntary phantom mobility is used in research as a promising way to control prostheses via PR [10]- [14]. This phenomenon is defined as the ability to send a motor command to one's phantom limb that will in turn elicit the somatosensory sensation of a movement via systematically associated contractions in residual muscles [12]- [17]. Phantom mobility, and more specifically phantom hand movement (PHM), is a widespread phenomenon irrespective of the level of amputation.…”

Phantom-Mobility-Based Prosthesis Control in Transhumeral Amputees Without Surgical Reinnervation: A Preliminary Study

“…This approach has been quite extensively studied for below-elbow amputees whose residual limb usually still contains the muscles that mobilized the fingers before the amputation, and, therefore, provide an adapted measurement site together with relatively strong myoelectric signals. While first attempts of adaptation of these approaches to above-elbow amputees date back to the 70s with pioneering work like (Wirta et al, 1978 ), several studies recently tried to revive such approach using updated classification techniques, specifically of phantom-limb-mobility-related EMG signals (Jarrasse et al, 2017b ; Gaudet et al, 2018 ), even extended to individual finger movement decoding (Jarrasse et al, 2017a ). Also, recent work on the treatment of phantom limb pain with help of virtual reality (e.g., Ortiz-Catalan et al, 2016 ) illustrates the possibility of decoding these PLM-associated EMG patterns measured on the residual limb of transhumeral amputees.…”

Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals

“…Nevertheless, these characteristics are only a related subcategory of the factors that instigate inconsistencies in interpreting the time and occurrence-based statistical features of the sEMG signals. The evaluation of these factors was conducted by researchers who have obtained significant results from their analysis (Jarrasse et al, 2017 ). Techniques for monitoring muscle activity are employed to measure and analyze the performance of machines and equipment, as well as to evaluate the physical condition of their operators (Hyodo et al, 2012 ; Hong et al, 2018 ).…”