Surface electromyography (sEMG) sensor is widely used to operate a myoelectric prosthesis and it has been further developed for various applications. In this paper, a multi-channel sEMG knit band sensor is newly suggested to be worn easily by amputee for myoelectric prosthesis. The band sensor was fabricated by knitting the silver-plated conductive yarn for electrodes with the moisture-wicking technical yarn for the comfort, ultimately, to record eight-channel sEMG signals simultaneously. Several experiments were conducted to verify the performance of knit band sensor. The normalized correlation between the developed knitted electrodes and the disposable (Ag/AgCl) electrodes is very high (0.95) at the same muscular area. The average signal-to-noise ratio value of the knitted electrodes (17.91 dB) was higher than that of the disposable electrodes (13.30 dB). For the performances of motion classification, overall classification accuracy of the knitted electrodes (93.2%) was higher than that of the disposable (87.9%). These experimental results have supported that the developed knit band sensor was as effective as the disposable electrodes.

In recent years, flexible sensors for data gloves have been developed that aim to achieve excellent wearability, but they are associated with difficulties due to the complicated manufacturing and embedding into the glove. This study proposes a knitted glove integrated with strain sensors for pattern recognition of hand postures. The proposed sensing glove is fabricated at all once by a knitting technique without sewing and bonding, which is composed of strain sensors knitted with conductive yarn and a glove body with non-conductive yarn. To verify the performance of the developed glove, electrical resistance variations were measured according to the flexed angle and speed. These data showed different values depending on the speed or angle of movements. We carried out experiments on hand postures pattern recognition for the practicability verification of the knitted sensing glove. For this purpose, 10 able-bodied subjects participated in the recognition experiments on 10 target hand postures. The average classification accuracy of 10 subjects reached 94.17% when their own data were used. The accuracy of up to 97.1% was achieved in the case of grasp posture among 10 target postures. When all mixed data from 10 subjects were utilized for pattern recognition, the average classification expressed by the confusion matrix arrived at 89.5%. Therefore, the comprehensive experimental results demonstrated the effectiveness of the knitted sensing gloves. In addition, it is expected to reduce the cost through a simple manufacturing process of the knitted sensing glove.

The paper proposes an EMG-FMG-based prosthesis using a vibrational feedback method. The control loop for bio-feedback consists of a feed-forward loop for controlling the prosthetic hand and a sensory feedback loop for delivering the sense of vibration according to the distance with an object. In addition, the motion of the prosthetic hand using the EMG-FMG knit band sensor as an input device is generated via a decision tree algorithm, the distance is measured using a capacitive knit data glove, and the vibrational feedback using a PVDF piezoelectric actuator is provided. The control loop for bio-feedback was verified experimentally. First, the performance of the sensors and control methods was analyzed, and the results of EMG-FMG motion recognition and vibrational feedback were verified. Second, the experiment was conducted on ten subjects who wore prosthesis to which the bio-feedback method was applied. Thanks to the control loops for bio-feedback, the grasping time with blindfolded was reduced by 2.92 seconds and the number of failures decreased by 1.04 times. As a result, it was confirmed that the control loop for bio-feedback could improve the performance of the prosthesis.

Myoelectric prostheses assist users to live their daily lives. However, the majority of users are primarily confined to forearm amputees because the surface electromyography (sEMG) that understands the motion intents should be acquired from a residual limb for control of the myoelectric prosthesis. This study proposes a novel fabric vest socket that includes embroidered electrodes suitable for a high-level upper amputee, especially for shoulder disarticulation. The fabric vest socket consists of rigid support and a fabric vest with embroidered electrodes. Several experiments were conducted to verify the practicality of the developed vest socket with embroidered electrodes. The sEMG signals were measured using commercial Ag/AgCl electrodes for a comparison to verify the performance of the embroidered electrodes in terms of signal amplitudes, the skin-electrode impedance, and signal-to-noise ratio (SNR). These results showed that the embroidered electrodes were as effective as the commercial electrodes. Then, posture classification was carried out by able-bodied subjects for the usability of the developed vest socket. The average classification accuracy for each subject reached 97.92%, and for all the subjects it was 93.2%. In other words, the fabric vest socket with the embroidered electrodes could measure sEMG signals with high accuracy. Therefore, it is expected that it can be readily worn by high-level amputees to control their myoelectric prostheses, as well as it is cost effective for fabrication as compared with the traditional socket.

The degrees-of-freedom of robotic prosthetic hands have recently increased, but high-level amputees such as those with shoulder disarticulation and trans-humeral amputation do not have enough muscular areas on their upper limbs upon which to base surface electromyogram (sEMG) signals. In this paper, a wearable fabric sensor is proposed to measure the sEMG on the lower limb and to classify the foot postures by using the proposed convolutional neural network (CNN), ultimately, for the application to high-level upper limb amputees. First, we determined that sEMG signals of the lower limb can be classified into levels in a manner similar to those of the upper limb for eight postures. Second, a multilayer perceptron (MLP) and the proposed CNN was used to compare the pattern recognition accuracy for classifying eight postures. Finally, the wearable fabric sensor and the proposed CNN network were demonstrated by the trans-radial amputees. These results showed that the wearable fabric sensor verified different eight patterns based on similar motions of both limbs ( p < 0.001). In addition, the classification accuracy (91.3%) of the proposed CNN was much higher than that (79%) of MLP ( p < 0.05). The wearable fabric sensor allowed the measurement location to change from the upper limb to the lower limb and allowed the number of the classifiable patterns to increase thanks to the sixteen-channel sEMG signals acquired from 32 fabric electrodes. The high classification accuracy of the proposed CNN will be useful for various users who have to wear myoelectric prosthesis every day.

The paper proposes a stiffness measurement device composed of a measurement part that includes four indenters and an arm anchorage part that includes four linear servomotors. The device is able to make simultaneous stiffnesses measurements of the human forearm. An indenter using parallelogram mechanisms has two degrees-of-freedom and is designed to measure the stiffness at various locations. The kinematics of the parallelogram mechanism is computed to control the position of the indenters. Additionally, the admittance compensator for force control is designed so that the indenter can press the skin surface of the human forearm. According to the force level measured at loadcell installed on the indenter, the control is switched between pure position control and admittance control. Further, this device includes a two-axis potentiometer at the indenter endpoint. This potentiometer acts like a passive two degrees-of-freedom universal joint, allowing the indenter to press perpendicular to the measurement skin area. For this purpose, the mapping between the voltage output and the potentiometer angle was obtained by fitting for each axis. The proposed measurement device was tested for accuracy and repeatability. Ultimately, the measurement device was able to measure the stiffnesses of four regions of the human limb simultaneously.