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This paper presents a fingertip grip force sensor based on custom capacitive sensors for glove-type assistive devices with an open-pad structure. The design of the sensor allows using human tactile sensations during grasping and manipulating an object. The proposed sensor can be attached on both sides of the fingertip and measure the force caused by the expansion of the fingertip tissue when a grasping force is applied to the fingertip. The number of measurable degrees of freedom (DoFs) are the two DoFs (flexion and adduction) for the thumb and one DoF (flexion) for the index and middle fingers. The proposed sensor allows the combination with a glove-type assistive device to measure the fingertip force. Calibration was performed for each finger joint angle because the variations in the expansion of the fingertip tissue depend on the joint angles. The root mean square error (RMSE) for fingertip force estimation ranged from 3.75% to 9.71% after calibration, regardless of the finger joint angles or finger posture. [ABSTRACT FROM AUTHOR]

Stimuli-responsive soft robots have provided new directions for obtaining advanced biomedical healthcare systems, such as targeted drug delivery capsules, less-invasive biopsy tools, and untethered microsurgical robots. We designed, 3D printed, and tested diverse time-dependent shape changeable 3D pH-responsive soft grippers consisting of N-isopropylacrylamide (NIPAM) and N-isopropylacrylamide-co-acrylic acid (NIPAM-AAc) bilayer. We found that the swelling/deswelling-driven actuation of the pH-responsive NIPAM/NIPAM-AAc gripper is primarily affected by the volume percent (% v/v) of the acrylic acid (AAc) and intensity of UV light. We expect that this study can be applied to untethered pH-responsive soft grippers as smart drug delivery capsules or biopsy tools in biomedical healthcare systems.

In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices.

We have developed a one-of-a-kind hand exoskeleton, called Maestro, which can power finger movements of those surviving severe disabilities to complete daily tasks using compliant joints. In this paper, we present results from an electromyography (EMG) control strategy conducted with spinal cord injury (SCI) patients (C5, C6, and C7) in which the subjects completed daily tasks controlling Maestro with EMG signals from their forearm muscles. With its compliant actuation and its degrees of freedom that match the natural finger movements, Maestro is capable of helping the subjects grasp and manipulate a variety of daily objects (more than 15 from a standardized set). To generate control commands for Maestro, an artificial neural network algorithm was implemented along with a probabilistic control approach to classify and deliver four hand poses robustly with three EMG signals measured from the forearm and palm. Increase in the scores of a standardized test, called the Sollerman hand function test, and enhancement in different aspects of grasping such as strength shows feasibility that Maestro can be capable of improving the hand function of SCI subjects., Competing Interests: The authors declare no competing interests exist., (© The Author(s) 2020.)

We propose a new method - twitch force - for estimation of the muscle behavior during voluntary contraction for assessing localized muscle fatigue. The proposed method uses the sEMG peaks as input and the measured force as output. The twitch force, which is a transfer function to generate force, was estimated during fatiguing contraction. We verified the estimated twitch force based on the measured results with electrical stimulation. The participants performed isometric little finger flexion until exhaustion. SEMG was recorded on the flexor digiti minimi brevis muscle for the proposed method and the electrical stimulation electrodes on the ulnar nerve induced involuntary contraction for reference. As the muscle fatigue level increased, the twitch peaks decreased in both methods. The proposed method can be widely used in the quantitative analysis of muscle fatigue during voluntary contraction.

Many studies have estimated joint force using surface electromyography (SEMG), however, the time-variant characteristic of SEMG is not considered. The change of SEMG amplitude is one of manifestations of muscle fatigue. This study proposes a force estimation method using SEMG in fatiguing contraction. The SEMG amplitude is used to determine the signal states by k-means clustering method. According to the signal state changes, the corresponding gain is used to estimate the force. The target contraction is an isometric abduction of an index finger in static and dynamic force conditions for 5 healthy subjects. The estimation performance was evaluated by percentage of root mean squared error (RMSE). The RMSE for the proposed method is 2.5 ± 1.0% under static condition and 8.8 ± 1.2% under dynamic condition. The accuracy using a constant gain calculated at initial time was used to compare with the proposed method. The RMSE are 8.9 ± 2.2% under static condition and 10.1 ± 2.4% under dynamic condition. The proposed method had better performance in both conditions.