Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control

Abstract

Myoelectric control of a prosthetic hand with more than one degree of freedom (DoF) is challenging, and clinically available techniques require a sequential actuation of the DoFs. Simultaneous and proportional control of multiple DoFs is possible with regression-based approaches allowing for fluent and natural movements. Conventionally, the regressor is calibrated in an open-loop with training based on recorded data and the performance is evaluated subsequently. For individuals with amputation or congenital limb-deficiency who need to (re)learn how to generate suitable muscle contractions, this open-loop process may not be effective. We present a closed-loop real-time learning scheme in which both the user and the machine learn simultaneously to follow a common target. Experiments with ten able-bodied individuals show that this co-adaptive closed-loop learning strategy leads to significant performance improvements compared to a conventional open-loop training paradigm. Importantly, co-adaptive learning allowed two individuals with congenital deficiencies to perform simultaneous 2-D proportional control at levels comparable to the able-bodied individuals, despite having to a learn completely new and unfamiliar mapping from muscle activity to movement trajectories. To our knowledge, this is the first study which investigates man-machine co-adaptation for regression-based myoelectric control. The proposed training strategy has the potential to improve myographic prosthetic control in clinically relevant settings.

Two degrees of freedom quasi-static EMG-force at the wrist using a minimum number of electrodes

“…Each of our subjects was new to lab-based EMG-force experimentation (thus, had no prior experience with 2-DoF experimental contractions), limited practice was made available in our single-session study and two of our subjects had congenital limb absence (a condition in which phantom limb sensation may be limited, thus impairing the mirrored-contraction protocol). Hence, significant subject training may need to be included in future work with limb-absent subjects (Hahne et al, 2015; Powell, Kaliki, 2014). …”

“…First, the successful results were primarily observed in able-bodied subjects, with much poorer results in limb-absent subjects. Multi-session training could lead to lower errors in limb-absent subjects (Hahne, Dahne, 2015; Powell, Kaliki, 2014). Second, low EMG-force (Jiang, Dosen, 2012a) or EMG-classification (Ortiz-Catalan, Rouhani, 2015) errors in the laboratory do not necessarily transfer to improved prosthesis usability.…”

A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition

“…After analyzing both industrial and academic demands, Farina et al [7] divided the demand for reliability of upper limb prosthesis control system into two parts: (a) the robustness to instantaneous changes such as the electrode shifting when donning and doffing, and arm posture variation; and (b) the adaptability to slow changes such as muscular fatigue and skin impedance variation. The robustness is usually related to research on advanced signal recording methods, such as optimizing the size and the layout of EMG electrodes [8,9], employing high-density electrodes to get more information [10,11], and finding out invariant characteristics in EMG signals [12], whereas the adaptability is usually related to adaptive learning methods [13,14,15,16,17]. The objective of this paper is to employ the adaptive learning method based on the theory of concept drift to endow the PR classifier with adaptability to slow changes of EMG signals.…”

A Novel Percutaneous Electrode Implant for Improving Robustness in Advanced Myoelectric Control

“…Our findings indicate that the problems related to both factors may be solved by the proposed implants. We have recently shown that with appropriate training strategies prosthetic users can achieve a similar high performance as able-bodied subjects in regression based simultaneous and proportional myoelectric control (Hahne et al, 2015). Once a high performance is reached, we expect a similar robustness to the investigated factors also for prosthetic users with the proposed implants.…”

“…This is largely due to the repositioning of the electrodes when donning/doffing the prosthesis (Young et al, 2012). A myoelectric control test that involved simultaneous control of two DoF of the wrist was designed based on the linear regression approach described in Hahne et al (2015). The wrist angles of flexion/extension and radial/ulnar deviation were estimated from the RMS of the EMG in overlapping time intervals of 200 ms, with 40 ms increments.…”