Model-based control of individual finger movements for prosthetic hand function

Abstract

Prosthetic devices for hand difference have advanced considerably in recent years, to the point where the mechanical dexterity of a state-of-the-art prosthetic hand approaches that of the natural hand. Control options for users, however, have not kept pace, meaning that the new devices are not used to their full potential. Promising developments in control technology reported in the literature have met with limited commercial and clinical success. We have previously described a biomechanical model of the hand that could be used for prosthesis control. In this study, we report on three key elements of the biomechanical simulations relevant to prosthesis control: we show the performance of the model in replicating recorded hand kinematics and find average correlations of 0.89 between modelled and recorded motions; we show that the computational performance of the simulations is fast enough to achieve real-time control with a robotic hand in the loop; and we describe the use of the model for controlling object gripping. Despite some limitations in accessing sufficient driving signals, the model performance shows promise as a controller for prosthetic hands when driven with recorded EMG signals. We identify areas for future work to address these limitations.

Quantifying Soft Tissue Artefacts and Imaging Variability in Motion Capture of the Fingers

“…Nevertheless, we provide data that could be used to support future researchers in calculating their required sample size to ensure statistical power and evaluating potential clinical effect sizes, and offer insights to support previous work undertaken in cadaveric or single case studies. These results would complement advanced soft tissue MSK models, 3,17,23,26,29,30 now finding use in such diverse applications as studying human-device interactions 22 and optimising tenodesis surgery. 9 These results also quantified the error associated with STA affecting and advocating a single surface marker placement protocol on the fingers as an alternative approach to technical marker sets or marker clusters, which has been an ongoing topic of debate in kinematic analysis studies.…”

“…16 Marker-based kinematic measurement 11,27,28,32,34 and MSK modelling techniques have been reported for the hand joints. 3,19,29,37 The cited studies demonstrate that these methods are capable of reliable analysis of functional hand and wrist joint movements in health and disease, and integration into a MSK model can be used to predict kinetics such as joint moments, 5,31 muscle forces in static tasks, 29 and give insights into hand movements from extrinsic muscles with applications in prosthetic hand control. 3 They are often limited by idealising anatomy and its variability and by available input data, requiring a compromise between the detailed measurements available from cadaver models and the representativeness of living human subjects.…”

“…3,19,29,37 The cited studies demonstrate that these methods are capable of reliable analysis of functional hand and wrist joint movements in health and disease, and integration into a MSK model can be used to predict kinetics such as joint moments, 5,31 muscle forces in static tasks, 29 and give insights into hand movements from extrinsic muscles with applications in prosthetic hand control. 3 They are often limited by idealising anatomy and its variability and by available input data, requiring a compromise between the detailed measurements available from cadaver models and the representativeness of living human subjects. Moreover, where MSK models are driven by kinematic data, it is acknowledged that uncertainty arises from soft tissue artefacts (STAs), where motion capture markers display displacement relative to the underlying bony landmarks they are intended to follow.…”

EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges

“…After that, with proper mapping strategies, these models can be driven by EMG signals and, therefore, intuitively simulate the movement of human extremities, providing crucial information for continuous control of HMI systems. For example, a model-based control strategy is proposed in [ 134 ], using the EMG signal as the input for the carefully tuned and calibrated hand-finger biomechanic model, and managed to continuously control a prosthesis hand to finish the task of gripping in the response time of 16.2 ms per loop. The control schemes and multisensory strategy is demonstrated in Figure 14 .…”

W:Ti Flexible Transversal Electrode Array for Peripheral Nerve Stimulation: A Feasibility Study

“…tingling, as reported by the users [2]- [4], [6]- [11]. However, different groups approach this problem from different angles with marked differences in the site in the nervous system where the stimulation is delivered: central [12]- [14] vs. peripheral nervous system [2], [15]- [17]; invasive [2], [7], [8], [10], [18] vs. non-invasive [5], [17], [19]; and the way the neural stimulation is modulated to convey sensory precepts: biomimetic [8]- [10], [20]- [23] vs. abstract [13], [17], [21], [24]. We believe that this diversity is due to three main reasons: 1. uncertainty on how to best sense and convert sensory information from the environment to electrical patterns to stimulate the nervous system and evoke naturalistic sensation; 2. lack of technologies for targeted delivery of this information to the nervous system; and 3. research still ongoing on the development of biocompatible neural interfaces that allow direct communication with the peripheral nervous system without causing physical damage and pain.…”