Measurements of Electrodermal Activity, Tissue Oxygen Saturation, and Visual Analog Scale for Different Cuff Pressures.

The quantification of comfort in binding parts, essential human-machine interfaces (HMI) for the functioning of rehabilitation robots, is necessary to reduce physical strain on the user despite great achievements in their structure and control. This study aims to investigate the physiological impacts of binding parts by measuring electrodermal activity (EDA) and tissue oxygen saturation (StO2). In Experiment 1, EDA was measured from 13 healthy subjects under three different pressure conditions (10, 20, and 30 kPa) for 1 min using a pneumatic cuff on the right thigh. In Experiment 2, EDA and StO2 were measured from 10 healthy subjects for 5 min. To analyze the correlation between EDA parameters and the decrease in StO2, a survey using the visual analog scale (VAS) was conducted to assess the level of discomfort at each pressure. The EDA signal was decomposed into phasic and tonic components, and the EDA parameters were extracted from these two components. RM ANOVA and a post hoc paired t-test were used to determine significant differences in parameters as the pressure increased. The results showed that EDA parameters and the decrease in StO2 significantly increased with the pressure increase. Among the extracted parameters, the decrease in StO2 and the mean SCL proved to be effective indicators. Such analysis outcomes would be highly beneficial for studies focusing on the comfort assessment of the binding parts of rehabilitation robots.

A Study on Standard for Safety Requirements for Care Robots.

Recently, care robots are being developed that incorporate robotics into assistive products that focus on daily care for the physically disabled or elderly with reduced physical function. However, although care robots can reduce the physical burden of human intervention, they can also be dangerous depending on their situational awareness. This study describes a standardization that defines safety requirements for care robots and includes verification methods to test their safety requirements. As an example of the application of this standard, a standard for the safety and performance method of a feeding robot is shown. This standardization study is expected to contribute to the spread of care robots in the future.

Evaluation of Inertial Sensor-Based Pre-Impact Fall Detection Algorithms Using Public Dataset.

In this study, pre-impact fall detection algorithms were developed based on data gathered by a custom-made inertial measurement unit (IMU). Four types of simulated falls were performed by 40 healthy subjects (age: 23.4 ± 4.4 years). The IMU recorded acceleration and angular velocity during all activities. Acceleration, angular velocity, and trunk inclination thresholds were set to 0.9 g, 47.3°/s, and 24.7°, respectively, for a pre-impact fall detection algorithm using vertical angles (VA algorithm); and 0.9 g, 47.3°/s, and 0.19, respectively, for an algorithm using the triangle feature (TF algorithm). The algorithms were validated by the results of a blind test using four types of simulated falls and six types of activities of daily living (ADL). VA and TF algorithms resulted in lead times of 401 ± 46.9 ms and 427 ± 45.9 ms, respectively. Both algorithms were able to detect falls with 100% accuracy. The performance of the algorithms was evaluated using a public dataset. Both algorithms detected every fall in the SisFall dataset with 100% sensitivity). The VA algorithm had a specificity of 78.3%, and TF algorithm had a specificity of 83.9%. The algorithms had higher specificity when interpreting data from elderly subjects. This study showed that algorithms using angles could more accurately detect falls. Public datasets are needed to improve the accuracy of the algorithms.

Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors.

BACKGROUND: Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. OBJECTIVE: In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. METHODS: The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. RESULTS AND CONCLUSIONS: As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

Effects of 4-Week Intensive Active-Resistive Training with an EMG-Based Exoskeleton Robot on Muscle Strength in Older People: A Pilot Study.

This study aims to investigate the idea that an active-resistive training with an EMG-based exoskeleton robot could be beneficial to muscle strength and antagonist muscle cocontraction control after 4-week intensive elbow flexion/extension training. Three older people over 65 years participated the training for an hour per session and completed total 20 sessions during four weeks. Outcome measures were chosen as the maximum joint torque and cocontraction ratio between the biceps/triceps brachii muscles at pre-/post-training. The Wilcoxon signed-ranks test was performed to evaluate paired difference for the outcome measures. As a result, there was no significant difference in the maximum flexion or extension torque at pre- and post-training. However, the cocontraction ratio of the triceps brachii muscle as the antagonist was significantly decreased by 9.8% after the 4-week intensive training. The active-resistive training with the exoskeleton robot in the older people yielded a promising result, showing significant changes in the antagonist muscle cocontraction.

Biomechanical analysis of the circular friction hand massage.

BACKGROUND: A massage can be beneficial to relieve muscle tension on the neck and shoulder area. Various massage systems have been developed, but their motions are not uniform throughout different body parts nor specifically targeted to the neck and shoulder areas. OBJECTIVE: Pressure pattern and finger movement trajectories of the circular friction hand massage on trapezius, levator scapulae, and deltoid muscles were determined to develop a massage system that can mimic the motion and the pressure of the circular friction massage. METHODS: During the massage, finger movement trajectories were measured using a 3D motion capture system, and finger pressures were simultaneously obtained using a grip pressure sensor. RESULTS: Results showed that each muscle had different finger movement trajectory and pressure pattern. The trapezius muscle experienced a higher pressure, longer massage time (duration of pressurization), and larger pressure-time integral than the other muscles. CONCLUSIONS: These results could be useful to design a better massage system simulating human finger movements.

Electromagnetic acupuncture to enhance the effects of manual acupuncture on recovery from muscle fatigue of the quadriceps.

The aim of this study was to investigate a new method of manual acupuncture that used a magnetic field to stimulate only one acupoint vertically. We developed an eight-channel electromagnetic acupuncture (EMA) system that uses a solenoid-type electrode to insert the manual acupuncture needle into a hole in an electrode. We used a manual acupuncture needle for magnetic induction in order to penetrate vertically and deeply into tissues. In order to confirm the usefulness of EMA, we investigated the effects of treatment on muscle fatigue after strenuous knee extension/flexion exercises that had been performed by three groups: the nonstimulation, the manual acupuncture, and the EMA groups. Electromyograms showed that the median frequency (MF) in the EMA group had rapidly recovered after 4 minutes (p = 0.608), but that the peak torque had not recovered to the normal state (p < 0.05). Thus, we confirmed that compared with manual acupuncture, EMA resulted in better recovery from muscle fatigue.