An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject

Abstract

The functionality of upper limb prostheses can be improved by intuitive control strategies that use bioelectric signals measured at the stump level. One such strategy is the decoding of motor volition via myoelectric pattern recognition (MPR), which has shown promising results in controlled environments and more recently in clinical practice. Moreover, not much has been reported about daily life implementation and real-time accuracy of these decoding algorithms. This paper introduces an alternative approach in which MPR allows intuitive control of four different grips and open/close in a multifunctional prosthetic hand. We conducted a clinical proof-of-concept in activities of daily life by constructing a self-contained, MPR-controlled, transradial prosthetic system provided with a novel user interface meant to log errors during real-time operation. The system was used for five days by a unilateral dysmelia subject whose hand had never developed, and who nevertheless learned to generate patterns of myoelectric activity, reported as intuitive, for multi-functional prosthetic control. The subject was instructed to manually log errors when they occurred via the user interface mounted on the prosthesis. This allowed the collection of information about prosthesis usage and real-time classification accuracy. The assessment of capacity for myoelectric control test was used to compare the proposed approach to the conventional prosthetic control approach, direct control. Regarding the MPR approach, the subject reported a more intuitive control when selecting the different grips, but also a higher uncertainty during proportional continuous movements. This paper represents an alternative to the conventional use of MPR, and this alternative may be particularly suitable for a certain type of amputee patients. Moreover, it represents a further validation of MPR with dysmelia cases.

Multi-Grip Classification-Based Prosthesis Control With Two EMG-IMU Sensors

“…Several studies have also used this paradigm to decode grips and gestures for intuitive prosthetic hand control. In their majority, however, they have been either limited to offline analyses [8]- [10] or only included able-bodied participants [11]- [13], with few exceptions demonstrating real-time control with amputees [14]- [16].…”

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

“…Several solutions have been developed to reduce the interference in the acquired biomedical signals. However, a residual interference of these interferences still presents [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. The signal contamination by motion artifacts causes data irregularities.…”

Stationary Wavelet Processing and Data Imputing in Myoelectric Pattern Recognition on a Low-Cost Embedded System

“…Having an effective implementation of artifact reduction and data imputing on a mobile processing platform allows for investigation into their effects in real-life prosthetic use. We have previously developed an evaluation method using an embedded system in which the subject can report perceived misclassification while operating the prosthesis in daily life and for long periods of time [49]. The Assessment for Capacity of Myoelectric Control [50], the Activities Measure for Upper Limb Amputees [51], and the Southampton Hand Assessment Procedure [52] all provide insight into prosthetic controllability with respect to functional tasks simulating realworld environments, but they are cross-sectional in nature (a single point in time) and are not performed out in the real world.…”