Multichannel haptic feedback unlocks prosthetic hand dexterity

Loss of tactile sensations is a major roadblock preventing upper limb-absent people from multitasking or using the full dexterity of their prosthetic hands. With current myoelectric prosthetic hands, limb-absent people can only control one grasp function at a time even though modern artificial hands are mechanically capable of individual control of all five digits. In this paper, we investigated whether people could precisely control the grip forces applied to two different objects grasped simultaneously with a dexterous artificial hand. Toward that end, we developed a novel multichannel wearable soft robotic armband to convey artificial sensations of touch to the robotic hand users. Multiple channels of haptic feedback enabled subjects to successfully grasp and transport two objects simultaneously with the dexterous artificial hand without breaking or dropping them, even when their vision of both objects was obstructed. Simultaneous transport of the objects provided a significant time savings to perform the deliveries in comparison to a one-at-a-time approach. This paper demonstrated that subjects were able to integrate multiple channels of haptic feedback into their motor control strategies to perform a complex simultaneous object grasp control task with an artificial limb, which could serve as a paradigm shift in the way prosthetic hands are operated.

Compliant Underwater Manipulator with Integrated Tactile Sensor for Nonlinear Force Feedback Control of an SMA Actuation System

Design, sensing, and control of underwater gripping systems remain challenges for soft robotic manipulators. Our study investigates these critical issues by designing a shape memory alloy (SMA) actuation system for a soft robotic finger with a directly 3D-printed stretchable skin-like tactile sensor. SMA actuators were thermomechanically trained to assume a curved finger-like shape when Joule heated, and the flexible multi-layered tactile sensor was directly 3D-printed onto the surface of the fingertip. A nonlinear controller was developed to enable precise fingertip force control using feedback from the compliant tactile sensor. Underwater experiments were conducted using closed-loop force feedback from the directly 3D-printed tactile sensor with the SMA actuators, showing satisfactory force tracking ability. Furthermore, a 3D finite element model was developed to more deeply understand the shape memory thermal-fluidic-structural multi-physics simulation of the manipulator underwater. An application for human control via electromyogram (EMG) signals also demonstrated an intuitive way for a person to operate the submerged robotic finger. Together, these results suggested that the soft robotic finger could be used to carefully manipulate fragile objects underwater.

Effect of object and human-factor characteristics on the preference of thumb-index finger grasp type

This work is to investigate the factors affecting the preference of human thumb-index finger grasping type. A multinomial logistic regression analysis shown that the object characteristics (equivalent diameter and shape) and human-factor characteristics (hand-used, finger-length sum and finger-length ratio) had significant contributions on the preference of thumb-index finger grasp type (p < 0.05) but the gender had not (p > 0.05). Subsequently, two mathematical equations were proposed for predicting the probability at which the precision-pinch and power-grasp were chosen for grasping an object. The probability at which the precision-pinch was chosen gradually decreased with the increase in the equivalent diameter of objects, but it is opposite for the power-grasp case. The shorter the finger-length sum, the more likely the participant was to select the power-grasp for grasping an object compared to the precision-pinch. The power-grasp was the most frequently chosen for the finger-length ratios of 1.0-1.25 and 1.75-2.0. Practitioner summary: This fruitful study gave explanation of the relationship between the object and human-factor characteristics and the preference of human thumb-index finger grasp type, which would be helpful to make intelligent grasping planning strategies for two-finger bionic mechanical hands.

Simulated robotic device malfunctions resembling malicious cyberattacks impact human perception of trust, satisfaction, and frustration

Robot assistants and wearable devices are highly useful; however, these artificial systems are susceptible to hackers. In this article, two sets of experiments were conducted. The first part of this study simulated a malicious attack on a prosthetic arm system to adversely affect the operation of the prosthetic system, while the perception of 10 human subjects was surveyed. These 10 able-bodied subjects controlled the prosthetic arm and hand with electromyogram signals, while an artificial sensation of touch was conveyed to their arms as they operated the system, which enabled them to feel what the prosthetic hand was grasping as they were asked to transport an object from one location to another. This haptic feedback was provided in both the normal and abnormal operational modes but was disabled in the extremely abnormal mode. The electromyogram control signals for the arm were reversed in both the abnormal and extremely abnormal modes. Results from the simulated malicious attack on a prosthetic arm system showed that the subjects found the haptic feedback helpful in both the normal and abnormal modes of operation. Both the abnormal and extremely abnormal modes of operation negatively impacted the self-reported levels of trust, satisfaction, and frustration with the prosthetic system as the subjects grasped and transported an object. While these metrics were negatively impacted by system malfunctions resembling a malicious attack on the control functionality, it was possible to rebuild them to their former higher levels after the functionality of the prosthetic system was restored. A parallel study in this article involved simulating a malicious attack on a robot assistant to unfavorably affect the delivery operation modes, while the perception of 20 human subjects was surveyed. Results showed that the simulated malfunctions unfavorably impacted the perception of trust, satisfaction, and frustration, but it was possible to restore these metrics in two different ways as the device functionality was restored.

Direction of Slip Detection for Adaptive Grasp Force Control with a Dexterous Robotic Hand

A novel method of tactile communication among human-robot and robot-robot collaborative teams is developed for the purpose of adaptive grasp control of dexterous robotic hands. Neural networks are applied to the problem of classifying the direction objects slide against different tactile fingertip sensors in real-time. This ability to classify the direction that an object slides in a dexterous robotic hand was used for adaptive grasp synergy control to afford context dependent robotic reflexes in response to the direction of grasped object slip. Case studies with robot-robot and human-robot collaborative teams successfully demonstrated the feasibility; when object slip in the direction of gravity (towards the ground) was detected, the dexterous hand increased the grasp force to prevent dropping the object. When a human or robot applied an upward force to cause the grasped object to slip upward, the dexterous hand was programmed to release the object into the hand of the other team member. This method of adaptive grasp control using direction of slip detection can improve the efficiency of human-robot and robot-robot teams.

Human-Inspired Reflex to Autonomously Prevent Slip of Grasped Objects Rotated with a Prosthetic Hand

Autonomously preventing grasped objects from slipping out of prosthetic hands is an important feature for limb-absent people since they cannot directly feel the grip force applied to grasped objects. Oftentimes, a satisfactory grip force in one situation will be inadequate in different situations, such as when the object is rotated or transported. Over time, people develop a grip reflex to prevent slip of grasped objects when they are rotated with respect to gravity by their natural hands. However, this reflexive trait is absent in commercially available prosthetic hands. This paper explores a human-inspired grasp reflex controller for prosthetic hands to prevent slip of objects when they are rotated. This novel human-inspired grasped object slip prevention controller is evaluated with 6 different objects in benchtop tests and by 12 able-bodied subjects during human experiments replicating realistic tasks of daily life. An analysis of variance showed highly significant improvement in the number of successfully completed cycles for both the benchtop and human tests when the slip prevention reflex was active. An object sorting task, which was designed to serve as a cognitive distraction for the human subjects while controlling the prosthetic hand, had a significant impact on many of the performance metrics. However, assistance from the novel slip prevention reflex mitigated the effects of the distraction, offering an effective method for reducing both object slip and the required cognitive load from the prosthetic hand user.