Electrical muscle stimulation (EMS) is an emergent technique that miniaturizes force feedback, especially popular for untethered haptic devices, such as mobile gaming, VR, or AR. However, the actuation displayed by interactive systems based on EMS is coarse and imprecise. EMS systems mostly focus on inducing movements in large muscle groups such as legs, arms, and wrists; whereas individual finger poses, which would be required, for example, to actuate a user's fingers to fingerspell even the simplest letters in sign language, are not possible. The lack of dexterity in EMS stems from two fundamental limitations: (1) lack of independence: when a particular finger is actuated by EMS, the current runs through nearby muscles, causing unwanted actuation of adjacent fingers; and, (2) unwanted oscillations: while it is relatively easy for EMS to start moving a finger, it is very hard for EMS to stop and hold that finger at a precise angle; because, to stop a finger, virtually all EMS systems contract the opposing muscle, typically achieved via controllers (e.g., PID)—unfortunately, even with the best controller tuning, this often results in unwanted oscillations. To tackle these limitations, we propose dextrEMS, an EMS-based haptic device featuring mechanical brakes attached to each finger joint. The key idea behind dextrEMS is that while the EMS actuates the fingers, it is our mechanical brake that stops the finger in a precise position. Moreover, it is also the brakes that allow dextrEMS to select which fingers are moved by EMS, eliminating unwanted movements by preventing adjacent fingers from moving. We implemented dextrEMS as an untethered haptic device, weighing only 68g, that actuates eight finger joints independently (metacarpophalangeal and proximal interphalangeal joints for four fingers), which we demonstrate in a wide range of haptic applications, such as assisted fingerspelling, a piano tutorial, guitar tutorial, and a VR game. Finally, in our technical evaluation, we found that dextrEMS outperformed EMS alone by doubling its independence and reducing unwanted oscillations.

DextrEMS: Increasing Dexterity in Electrical Muscle Stimulation by Combining it with Brakes

Robots are required to autonomously respond to changing situations. Imitation learning is a promising candidate for achieving generalization performance, and extensive results have been demonstrated in object manipulation. However, cooperative work between humans and robots is still a challenging issue because robots must control dynamic interactions among themselves, humans, and objects. Furthermore, it is difficult to follow subtle perturbations that may occur among coworkers. In this study, we find that cooperative work can be accomplished by imitation learning using bilateral control. Thanks to bilateral control, which can extract response values and command values independently, human skills to control dynamic interactions can be extracted. Then, the task of serving food is considered. The experimental results clearly demonstrate the importance of force control, and the dynamic interactions can be controlled by the inferred action force.

Imitation Learning Based on Bilateral Control for Human–Robot Cooperation

Imitation learning has been studied as an efficient and high-performance method to generate robot motion. Specifically, bilateral control-based imitation learning has been proposed as a method of realizing fast motion. However, the learning approach of this method leads to the accumulation of prediction errors during the prediction process and may not generate desirable long-term behavior. Therefore, in this paper, we propose a method of autoregressive learning for bilateral control-based imitation learning to reduce the accumulation of prediction errors. A new neural network model for implementing autoregressive learning is also proposed. Three types of experiments are conducted to verify the effectiveness of the proposed method, where the method is shown to have improved performance over those of conventional approaches. Due to the structure and method of autoregressive learning employed by the developed model, the proposed method can generate desirable long-term motion for successful tasks and has a high generalization ability for environmental changes based on the human demonstrations of tasks.

Motion Generation Using Bilateral Control-Based Imitation Learning With Autoregressive Learning

Imitation Learning Based on Bilateral Control for Human-Robot Cooperation

Bilateral Control of Human Upper Limbs Using Functional Electrical Stimulation Based on Dynamic Model Approximation

It is important to decrease cost of power assist bicycles for their wide spread. Because force sensors for pedaling torque estimation are expensive, torque sensorless pedaling torque estimation is a good way to reduce cost. Therefore, in this paper, a pedaling torque estimation method for power assist bicycles is proposed. It can be implemented only in front and rear wheel independently driven type power assist bicycles, which were also proposed by the authors. In the proposed method, front and rear wheels are controlled to be the same velocity. Owing to this, a friction coefficient between a front wheel and the ground is equal to a friction coefficient between a rear wheel and the ground. Hence, we can eliminate one unknown parameter, a friction coefficient, from motion equations. In addition, because the proposed power assist bicycles have two motors, both external torques of front and rear wheels can be estimated. Together with data from acceleration sensor and the two estimated external torques, pedaling torque, normal forces on each wheel, and running resistance, can be estimated in real-time by using an iterative least square method. The validity of the proposed method is verified by simulations and experiments.

Sensorless pedaling torque estimation by front and rear wheels independently driven power assist bicycle

This paper describes the reduction of chattering in functional electrical stimulation (FES) with Smith compensator. We proposed a bilateral controller using FES. In the bilateral controller, in addition to normal remote control, the reaction forces of the distant location are transmitted back to the operator. Therefore, the operator feels like being in the distant location. In controlling of the human body using FES, a dead time occurs. As a result, high-frequency vibration (called chattering) occurs. In this paper, we propose a control method suppressing chattering in FES. In the experiment, the performance of the proposed method was verified by target value control and bilateral control. In the target value control, chattering was suppressed by the proposed method. However, in the bilateral control, chattering was not be suppressed by the interference of the time delay of the two subjects.

Chattering reduction of functional electrical stimulation with the smith compensator

In this paper, we describe the control of a body using functional electrical stimulation (FES) by adjusting pulse amplitude and frequency. FES is a technique used in the rehabilitation of spinal injury patients and application development for healthy people. However, control using the FES involves a delay time, resulting a high-frequency vibration (called chattering). In this paper, we propose that the control performance can be improved by adjusting the frequency in addition to the conventional pulse amplitude adjustment. In experiments, target values were controlled using subjects, and the performance of the proposed method was verified. Results confirmed that chattering is suppressed by the proposed method.

Control with Adjusted Pulse Frequency and Amplitude in Functional Electrical Stimulation

Closed Loop Control of Index Finger Using Functional Electrical Stimulation

In this study, we investigated bilateral control in the vertical direction using functional electrical stimulation (FES). Bilateral control is a remote operation technique that incorporates force feedback. This technology has potential applications in everyday life, including allowing medical doctors to make diagnoses or friends to communicate using tactile sensations when far apart. However, current iterations of this technology require robot arms to be worn. This is undesirable as such arms are heavy, and arms with multi degree-of-freedom are difficult to produce. To address these problems, we investigated bilateral control using FES. However, in previous studies, which applied FES to bilateral control, control in the vertical direction and the effect of reaction forces were not investigated. In this study, we verified that vertical control is achievable. First, the movements of a human operator were shown to follow those of another operator in the vertical direction, demonstrating that vertical bilateral control using FES is effective. We next investigated whether an operator could recognize when the partner was holding a weight. This experiment demonstrated that the operator was able to recognize the presence or absence of the weight in the hand of the partner. However, shaking was observed in the arms of the master and slave. We believe that this problem can be eliminated by estimating the reaction forces of arms.

Bilateral Control in the Vertical Direction Using Functional Electrical Stimulation

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