Recent breakthroughs in wearable robots, such as exoskeleton robots with soft actuators and soft exosuits, have enabled the use of safe and comfortable movement assistance. However, modeling and ...identification methods for soft actuators used in wearable robots have yet to be sufficiently explored. In this study, we propose a novel approach for obtaining accurate soft actuator models through the design of physical user–robot interactions for wearable robots, in which the user applies external forces to the robot. To obtain an accurate soft actuator model from the limited amount of data acquired through an interaction, we leverage an active learning framework based on Gaussian process regression. We conducted experiments using a two-degree-of-freedom upper-limb exoskeleton robot with four pneumatic artificial muscles (PAMs). Experimental results showed that physical interactions between the exoskeleton robot and the user were successfully designed to allow PAM models to be identified. Furthermore, we found that data acquired through an interaction could result in more accurate soft actuator models for the exoskeleton robots than data acquired without a physical interaction between the exoskeleton robot and the user.
•Exoskeleton assistive strategies are learned from user-robot physical interactions.•Small sample size and objective function design problems are tackled.•Sample efficient model-based reinforcement ...learning framework is exploited.•Our objective function only considers the user’s muscular effort for assistance.•Proper strategies were learned by only 60s interactions of a user and an exoskeleton.
Social demand for exoskeleton robots that physically assist humans has been increasing in various situations due to the demographic trends of aging populations. With exoskeleton robots, an assistive strategy is a key ingredient. Since interactions between users and exoskeleton robots are bidirectional, the assistive strategy design problem is complex and challenging. In this paper, we explore a data-driven learning approach for designing assistive strategies for exoskeletons from user-robot physical interaction. We formulate the learning problem of assistive strategies as a policy search problem and exploit a data-efficient model-based reinforcement learning framework. Instead of explicitly providing the desired trajectories in the cost function, our cost function only considers the user’s muscular effort measured by electromyography signals (EMGs) to learn the assistive strategies. The key underlying assumption is that the user is instructed to perform the task by his/her own intended movements. Since the EMGs are observed when the intended movements are achieved by the user’s own muscle efforts rather than the robot’s assistance, EMGs can be interpreted as the “cost” of the current assistance. We applied our method to a 1-DoF exoskeleton robot and conducted a series of experiments with human subjects. Our experimental results demonstrated that our method learned proper assistive strategies that explicitly considered the bidirectional interactions between a user and a robot with only 60 seconds of interaction. We also showed that our proposed method can cope with changes in both the robot dynamics and movement trajectories.
In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive ...joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.
In this letter, we propose an electromyography (EMG)-based optimal control framework to design physical human-robot interaction for rehabilitation and develop a novel assist-as-needed (AAN) ...controller based on a model predictive control (MPC) approach. To enhance the recovery of motor functions, encouraging the voluntary movements of patients is necessary while a therapist is assisting them. Therefore, in an AAN control framework, the robot only assists the deficient torque to generate a target movement. In our study, we first estimate the joint torque of a patient from measured EMG signals and then derive the deficient joint torque to generate the target movements by considering the patient's estimated joint torque with an MPC method. Results showed that our proposed method adaptively derived the necessary torque to follow the target elbow joint trajectories based on the subject's voluntary movements.
A physical trainer often physically guides a learner's limbs to teach an ideal movement, giving the learner proprioceptive information about the movement to be reproduced later. This instruction ...requires the learner to perceive kinesthetic information and store the instructed information temporarily. Therefore, (1) proprioceptive acuity to accurately perceive the taught kinesthetics and (2) short-term memory to store the perceived information are two critical functions for reproducing the taught movement. While the importance of proprioceptive acuity and short-term memory has been suggested for active motor learning, little is known about passive motor learning. Twenty-one healthy adults (mean age 25.6 years, range 19-38 years) participated in this study to investigate whether individual learning efficiency in passively guided learning is related to these two functions. Consequently, learning efficiency was significantly associated with short-term memory capacity. In particular, individuals who could recall older sensory stimuli showed better learning efficiency. However, no significant relationship was observed between learning efficiency and proprioceptive acuity. A causal graph model found a direct influence of memory on learning and an indirect effect of proprioceptive acuity on learning via memory. Our findings suggest the importance of a learner's short-term memory for effective passive motor learning.
Medical litigation resulting from medical errors has a negative impact on health economics for both patients and medical practitioners. In medical litigation involving orthopedic surgeons, we aimed ...to identify factors contributing to plaintiff victory (orthopedic surgeon loss) through a comprehensive assessment.
This retrospective study included 166 litigation claims against orthopedic surgeons using a litigation database in Japan. We evaluated the sex and age of the patient (plaintiff), initial diagnosis, diagnostic error, system error, the time and place of each claim that led to malpractice litigation, the institution's size, and clinical outcomes. The main outcome was the litigation outcome (acceptance or rejection) in the final judgment. Acceptance meant that the orthopedic surgeon lost the malpractice lawsuit. We conducted multivariable logistic regression analyses to examine the association of factors with an accepted claim.
The median age of the patients was 42 years, and 65.7% were male. The litigation outcome of 85 (51.2%) claims was acceptance. The adjusted median indemnity paid was $151,818. The multivariable analysis showed that diagnostic error, system error, sequelae, inadequate medical procedure, and follow-up observation were significantly associated with the orthopedic surgeon losing the lawsuit. In particular, claims involving diagnostic errors were more likely to be acceptance claims, in which the orthopedic surgeon lost (adjusted odds ratio 16.7, 95% confidence intervals: 4.7 to 58.0, p < 0.001). All of the claims in which the orthopedic surgeon lost were associated with a diagnostic or system error, with the most common one being system error.
System errors and diagnostic errors were significantly associated with acceptance claims (orthopedic surgeon losses). Since these are modifiable factors, it is necessary to take measures not only for individual physicians but also for the overall medical management system to enhance patient safety and reduce the litigation risk of orthopedic surgeons.
Sports trainers often grasp and move trainees' limbs to give instructions on desired movements, and a merit of this passive training is the transferring of instructions via proprioceptive ...information. However, it remains unclear how passive training affects the proprioceptive system and improves learning. This study examined changes in proprioceptive acuity due to passive training to understand the underlying mechanisms of upper extremity training. Participants passively learned a trajectory of elbow-joint movement as per the instructions of a single-arm upper extremity exoskeleton robot, and the performance of the target movement and proprioceptive acuity were assessed before and after the training. We found that passive training improved both the reproduction performance and proprioceptive acuity. We did not identify a significant transfer of the training effect across arms, suggesting that the learning effect is specific to the joint space. Furthermore, we found a significant improvement in learning performance in another type of movement involving the trained elbow joint. These results suggest that participants form a representation of the target movement in the joint space during the passive training, and intensive use of proprioception improves proprioceptive acuity.
This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective ...labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.
We report a case of ipsilateral periprosthetic fractures above and below the knee that occurred at different times due to navigation tracker pin and bone fragility. A 66-year-old Japanese woman with ...rheumatoid arthritis (RA) underwent a total knee arthroplasty. Four months post-surgery, a periprosthetic fracture above the knee at the navigation pin hole was detected. She underwent osteosynthesis and could walk independently, but she developed an ipsilateral tibial component fracture. Conservative treatment with a splint was followed by bone union. Patients with RA treated with oral steroids tend to develop ipsilateral periprosthetic fractures around the knee due to bone fragility.
Purpose
Postoperative radiographs are routinely used to assess fracture reduction following intramedullary nail fixation for pertrochanteric fractures, even though computed tomography (CT) is a ...superior modality. We aimed to determine the association between reduction quality assessed by CT and rates of reoperation and to evaluate the association of reoperation and reduction quality according to the assessment modality (plain radiographs vs. CT).
Methods
A retrospective analysis of 299 consecutive patients treated with intramedullary nail fixation for pertrochanteric fractures was conducted. Fracture reduction measured by postoperative radiographs and CT was categorized as anatomical type, extramedullary type, or intramedullary type. Postoperative data for analysis included reduction status, tip-apex distance (TAD), screw position in the femoral head, sliding distance, and conditions associated with reoperation.
Results
Of the 299 patients included with a mean age of 83.1 ± 8.2 years, there were six patients who required reoperation (2.0%). According to the CT assessments, there were 42 intramedullary reductions (14.0%). Patients with a non-intramedullary reduction based on postoperative CT images were significantly more likely to have proper placement of the screw, a reduced TAD, a reduced sliding distance, and a lower reoperation rate than those with an intramedullary reduction (
P
< 0.05). The reduction quality assessed by postoperative CT was significantly associated with reoperation (95% CI, 1.45–29.31).
Conclusions
Intramedullary reduction assessed by CT was associated with reoperation. The reduction quality based on CT findings was more predictive for reoperation than that from plain radiographs.
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