Development and Evaluation of a Treadmill-based Video-see-through and Optical-see-through Mixed Reality Systems for Obstacle Negotiation Training.

Mixed reality (MR) technologies have a high potential to enhance obstacle negotiation training beyond the capabilities of existing physical systems. Despite such potential, the feasibility of using MR for obstacle negotiation on typical training treadmill systems and its effects on obstacle negotiation performance remains largely unknown. This research bridges this gap by developing an MR obstacle negotiation training system deployed on a treadmill, and implementing two MR systems with a video see-through (VST) and an optical see-through (OST) Head Mounted Displays (HMDs). We investigated the obstacle negotiation performance with virtual and real obstacles. The main outcomes show that the VST MR system significantly changed the parameters of the leading foot in cases of Box obstacle (approximately 22 cm to 30 cm for stepping over 7cm-box), which we believe was mainly attributed to the latency difference between the HMDs. In the condition of OST MR HMD, users tended to not lift their trailing foot for virtual obstacles (approximately 30 cm to 25 cm for stepping over 7cm-box). Our findings indicate that the low-latency visual contact with the world and the user's body is a critical factor for visuo-motor integration to elicit obstacle negotiation.

Analyzing the Differences in the Degree of Force Application Between Novice and Expert Physiotherapists Using a Muscle Deformation Sensor.

BACKGROUND: Training physiotherapists require substantial experience and a lengthy period of time to achieve proficiency. However, establishing an objective method for quantifying the degree of force applied during treatment remains elusive, making training difficult. OBJECTIVES: This study aims to clarify the difference in the degree of force application between novice and expert physiotherapists using muscle deformation sensors and to assist in teaching. METHODS: A muscle deformation sensor array was utilized to capture the muscle bulging (muscle deformation), and the degree of force was visualized. The experiment involved two types of physiotherapy: upper and lower extremity exercises. Subsequently, the muscle deformation value and standard deviations of the muscle deformation data obtained were compared. RESULTS: Significant differences between novices and experts were observed in forearm muscle deformation values and standard deviations across both types of physiotherapies (p<0.05). Additionally, a distinction was observed in the left lower limb flexor muscles during upper extremity exercise (p<0.05). CONCLUSION: The results of this survey showed notable differences in the degree of force application between novices and experts, as demonstrated by our findings. Moreover, these implications extend beyond physiotherapy to sports, hobbies, and the teaching of traditional skills.

Optical Myography-Based Sensing Methodology of Application of Random Loads to Muscles during Hand-Gripping Training.

Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein's idea of "repetition without repetition" suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training.

Piton: Investigating the Controllability of a Wearable Telexistence Robot.

The COVID-19 pandemic impacted collaborative activities, travel, and physical contact, increasing the demand for real-time interactions with remote environments. However, the existing remote communication solutions provide limited interactions and do not convey a high sense of presence within a remote environment. Therefore, we propose a snake-shaped wearable telexistence robot, called Piton, that can be remotely used for a variety of collaborative applications. To the best of our knowledge, Piton is the first snake-shaped wearable telexistence robot. We explain the implementation of Piton, its control architecture, and discuss how Piton can be deployed in a variety of contexts. We implemented three control methods to control Piton: HM-using a head-mounted display (HMD), HH-using an HMD and hand-held tracker, and FM-using an HMD and a foot-mounted tracker. We conducted a user study to investigate the applicability of the proposed control methods for telexistence, focusing on body ownership (Alpha IVBO), mental and physical load (NASA-TLX), motion sickness (VRSQ), and a questionnaire to measure user impressions. The results show that both the HM and HH provide relevantly high levels of body ownership, had high perceived accuracy, and were highly favored, whereas the FM control method yielded the lowest body ownership effect and was least favored. We discuss the results and highlight the advantages and shortcomings of the control methods with respect to various potential application contexts. Based on our design and evaluation of Piton, we extracted a number of insights and future research directions to deepen our investigation and realization of wearable telexistence robots.

Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration.

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

Repeated exposure to tripping like perturbations elicits more precise control and lower toe clearance of the swinging foot during steady walking.

Controlling minimum toe clearance (MTC) is considered an important factor in preventing tripping. In the current study, we investigated modifications of neuro-muscular control underlying toe clearance during steady locomotion induced by repeated exposure to tripping-like perturbations of the right swing foot. Fourteen healthy young adults (mean age 26.4 ± 3.1 years) participated in the study. The experimental protocol consisted of three identical trials, each involving three phases: steady walking (baseline), perturbation, and steady walking (post-perturbation). During the perturbation, participants experienced 30 tripping-like perturbations at unexpected timing delivered by a custom-made mechatronic perturbation device. The temporal parameters (cadence and stance phase%), mean, and standard deviation of MTC were computed across approximately 90 strides collected during both baseline and post-perturbation phases, for all trials. The effects of trial (three levels), phase (two levels: baseline and post-perturbation) and foot (two levels: right and left) on the outcome variables were analyzed using a three-way repeated measures analysis of variance. The results revealed that exposure to repeated trip-like perturbations modified MTC toward more precise control and lower toe clearance of the swinging foot, which appeared to reflect both the expectation of potential forthcoming perturbations and a quicker compensatory response in cases of a lack of balance. Moreover, locomotion control enabled subjects to maintain symmetric rhythmic features during post-perturbation steady walking. Finally, the effects of exposure to perturbation quickly disappeared among consecutive trials.

Prediction Algorithm of Parameters of Toe Clearance in the Swing Phase.

The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the beginning of the swing phase. In the experiments, seven subjects walked on a treadmill for 360 s. The radial basis function network was trained with gait data ranging from 20 to 200 data points and tested with 100 data points. The root mean square error between the true and predicted values was 3.28 mm for the maximum toe clearance in the earlier swing phase and 2.30 mm for the minimum toe clearance in the later swing phase. Moreover, using gait data of other five subjects, the root mean square error between the true and predicted values was 4.04 mm for the maximum toe clearance and 2.88 mm for the minimum toe clearance when the walking velocity changed. This provided higher prediction accuracy compared with existing methods. The proposed algorithm used the information of joint movements at the start of the swing phase and could predict both the future maximum and minimum toe clearances within the same swing phase.

Prediction of Minimum Toe Clearance with a Radial Basis Function Network at the Start of the Swing Phase.

Prediction of minimum toe clearance (MTC) during walking can decrease the risk of tripping. In this paper, we proposed a novel MTC prediction method using a radial basis function network. Input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the start of the swing phase. In experiments, five subjects walked on a treadmill for 360 s. The radial basis function network was trained with 60 s of gait data and tested with the remaining 300 s of gait data. The root mean square error between the true and predicted MTC values was lower than 2.79 mm in all subjects.

Timing of intermittent torque control with wire-driven gait training robot lifting toe trajectory for trip avoidance.

Gait training robots are useful for changing gait patterns and decreasing risk of trip. Previous research has reported that decreasing duration of the assistance or guidance of the robot is beneficial for efficient gait training. Although robotic intermittent control method for assisting joint motion has been established, the effect of the robot intervention timing on change of toe clearance is unclear. In this paper, we tested different timings of applying torque to the knee, employing the intermittent control of a gait training robot to increase toe clearance throughout the swing phase. We focused on knee flexion motion and designed a gait training robot that can apply flexion torque to the knee with a wire-driven system. We used a method of timing detecting for the robot conducting torque control based on information from the hip, knee, and ankle angles to establish a non-time dependent parameter that can be used to adapt to gait change, such as gait speed. We carried out an experiment in which the conditions were four time points: starting the swing phase, lifting the foot, maintaining knee flexion, and finishing knee flexion. The results show that applying flexion torque to the knee at the time point when people start lifting their toe is effective for increasing toe clearance in the whole swing phase.

Relationship between magnitude of applied torque in pre-swing phase and gait change for prevention of trip in elderly people.

Elderly people are at risk of tripping because of their narrow range of articular motion. To avoid tripping, gait training that improves their range of articular motion would be beneficial. In this study we propose a gait-training robot that applies a torque during the pre-swing phase to achieve this goal. We investigated the relationship between magnitude of applied torque and change in the range of knee-articular motion while walking before and after the application of this torque. We developed a wearable robot and carried out an experiment on human participants in which a motor pulls a string embedded on the robotic frame, applying torque in the pre-swing phase for a period of 20 [s]. Before and after applying torque the participant walked normally for 15 [s] without interference from the robot. We found that knee flexion angle increased after applying the torque if the torque was within the range of approximately 6-8 [Nm]. Therefore, we were able to verify that a new range of knee articular motion can be learned through application of torque.