Control strategies for smart prosthetic hand technology: an overview

Brain-machine interface to control a prosthetic arm with monkey ECoGs during periodic movements

Brain–machine interfaces (BMIs) are promising technologies for rehabilitation of upper limb functions in patients with severe paralysis. We previously developed a BMI prosthetic arm for a monkey implanted with electrocorticography (ECoG) electrodes, and trained it in a reaching task. The stability of the BMI prevented incorrect movements due to misclassification of ECoG patterns. As a trade-off for the stability, however, the latency (the time gap between the monkey's actual motion and the prosthetic arm movement) was about 200 ms. Therefore, in this study, we aimed to improve the response time of the BMI prosthetic arm. We focused on the generation of a trigger event by decoding muscle activity in order to predict integrated electromyograms (iEMGs) from the ECoGs. We verified the achievability of our method by conducting a performance test of the proposed method with actual achieved iEMGs instead of predicted iEMGs. Our results confirmed that the proposed method with predicted iEMGs eliminated the time delay. In addition, we found that motor intention is better reflected by muscle activity estimated from brain activity rather than actual muscle activity. Therefore, we propose that using predicted iEMGs to guide prosthetic arm movement results in minimal delay and excellent performance.

Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)-Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

Exploring teleimpedance and tactile feedback for intuitive control of the Pisa/IIT SoftHand

This paper proposes a teleimpedance controller with tactile feedback for more intuitive control of the Pisa/IIT SoftHand. With the aim to realize a robust, efficient and low-cost hand prosthesis design, the SoftHand is developed based on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. Due to the built-in flexibility of the hand joints, as the SoftHand grasps, it follows a synergistic path while allowing grasping of objects of various shapes using only a single motor. The DC motor of the hand incorporates a novel teleimpedance control in which the user's postural and stiffness synergy references are tracked in real-time. In addition, for intuitive control of the hand, two tactile interfaces are developed. The first interface (mechanotactile) exploits a disturbance observer which estimates the interaction forces in contact with the grasped object. Estimated interaction forces are then converted and applied to the upper arm of the user via a custom made pressure cuff. The second interface employs vibrotactile feedback based on surface irregularities and acceleration signals and is used to provide the user with information about the surface properties of the object as well as detection of object slippage while grasping. Grasp robustness and intuitiveness of hand control were evaluated in two sets of experiments. Results suggest that incorporating the aforementioned haptic feedback strategies, together with user-driven compliance of the hand, facilitate execution of safe and stable grasps, while suggesting that a low-cost, robust hand employing hardware-based synergies might be a good alternative to traditional myoelectric prostheses.

A synergy-driven approach to a myoelectric hand

In this paper, we present the Pisa/IIT SoftHand with myoelectric control as a synergy-driven approach for a prosthetic hand. Commercially available myoelectric hands are more expensive, heavier, and less robust than their body-powered counterparts; however, they can offer greater freedom of motion and a more aesthetically pleasing appearance. The Pisa/IIT SoftHand is built on the motor control principle of synergies through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of a wide variety of objects with a single motor. Here we test, as a proof-of-concept, 4 myoelectric controllers: a standard controller in which the EMG signal is used only as a position reference, an impedance controller that determines both position and stiffness references from the EMG input, a standard controller with vibrotactile force feedback, and finally a combined vibrotactile-impedance (VI) controller. Four healthy subjects tested the control algorithms by grasping various objects. All controllers were sufficient for basic grasping, however the impedance and vibrotactile controllers reduced the physical and cognitive load on the user, while the combined VI mode was the easiest to use of the four. While these results need to be validated with amputees, they suggest a low-cost, robust hand employing hardware-based synergies is a viable alternative to traditional myoelectric prostheses.

Cognitive vision system for control of dexterous prosthetic hands: experimental evaluation

BACKGROUND

Dexterous prosthetic hands that were developed recently, such as SmartHand and i-LIMB, are highly sophisticated; they have individually controllable fingers and the thumb that is able to abduct/adduct. This flexibility allows implementation of many different grasping strategies, but also requires new control algorithms that can exploit the many degrees of freedom available. The current study presents and tests the operation of a new control method for dexterous prosthetic hands.

METHODS

The central component of the proposed method is an autonomous controller comprising a vision system with rule-based reasoning mounted on a dexterous hand (CyberHand). The controller, termed cognitive vision system (CVS), mimics biological control and generates commands for prehension. The CVS was integrated into a hierarchical control structure: 1) the user triggers the system and controls the orientation of the hand; 2) a high-level controller automatically selects the grasp type and size; and 3) an embedded hand controller implements the selected grasp using closed-loop position/force control. The operation of the control system was tested in 13 healthy subjects who used Cyberhand, attached to the forearm, to grasp and transport 18 objects placed at two different distances.

RESULTS

The system correctly estimated grasp type and size (nine commands in total) in about 84% of the trials. In an additional 6% of the trials, the grasp type and/or size were different from the optimal ones, but they were still good enough for the grasp to be successful. If the control task was simplified by decreasing the number of possible commands, the classification accuracy increased (e.g., 93% for guessing the grasp type only).

CONCLUSIONS

The original outcome of this research is a novel controller empowered by vision and reasoning and capable of high-level analysis (i.e., determining object properties) and autonomous decision making (i.e., selecting the grasp type and size). The automatic control eases the burden from the user and, as a result, the user can concentrate on what he/she does, not on how he/she should do it. The tests showed that the performance of the controller was satisfactory and that the users were able to operate the system with minimal prior training.

A hybrid adaptive control strategy for a smart prosthetic hand

This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The simulations of this hybrid controller, when compared with the proportional-integral-derivative (PID) controller showed enhanced performance. Work is in progress to extend this methodology to a five-fingered, three-dimensional prosthetic hand.

Control strategies for smart prosthetic hand technology: an overview

A chronological overview of the applications of control theory to prosthetic hand is presented. The overview focuses on hard computing or control techniques such as multivariable feedback, optimal, nonlinear, adaptive and robust and soft computing or control techniques such as artificial intelligence, neural networks, fuzzy logic, genetic algorithms and on the fusion of hard and soft control techniques. This overview is not intended to be an exhaustive survey on this topic and any omissions of other works is purely unintentional.