Hiroaki Wagatsuma

Knee osteoarthritis is a growing public health concern and a leading cause of functional disability, particularly among older adults. Knee orthoses are widely used in rehabilitation to provide joint support, alleviate pain, and improve mobility. Recent advances in exoskeletons and orthopedic orthoses have focused on improving flexibility, comfort, and reducing device weight. However, their performance is often limited by elevated limb-interface stresses and the inability of conventional rigid hinged joints to conform to the complex geometry of the human body. To address these limitations, this study investigates a metastructure-based compliant knee orthosis that employs architected lattice structures with tunable stiffness and enhanced conformability. A comparative investigation was conducted on four metastructure configurations, namely re-entrant, chiral, sinusoidal, and triangular-wave architectures. Their biomechanical performance was evaluated through experimental testing and finite element analysis to assess their effectiveness as compliant mechanisms for knee rehabilitation. The engineered architectures provide passive flexibility with selective stiffness, resulting in improved biomechanical compatibility and user comfort. The developed prototype achieved up to 40% weight reduction compared with a conventional hinged metal orthosis, while reducing peak limb-interface stress by approximately 35%. These results demonstrate the potential of metastructure-based compliant orthoses for improving comfort, reducing pain, and enhancing mobility during rehabilitation, particularly for elderly individuals and patients recovering from knee injuries.

In wheelchair basketball, classes are based on competition observations. Since 2021, out-of-competition testing has been implemented; however, research remains limited. This study aimed to determine whether the quantified volume of action (VOA) can be an indicator for classification and examined the influence of a competitive wheelchair on VOA evaluation. This cross-sectional study included 47 wheelchair basketball players (21 able-bodied, 26 with physical impairments: class 1, n = 8; class 2, n = 5; class 3, n = 4; class 4, n = 9). Tests were performed in a wheelchair (wheelchair condition) and on a trainer bed (bed condition). Participants held a ball and rotated their trunks in various planes. Movements were recorded using four cameras, and position coordinates were extracted using the three-dimensional DLT method. Classes and sitting conditions were compared across five groups: classes 1, 2, 3, 4, and able-bodied. Comparisons between classes revealed significant differences in all planes, including wheelchair and bed conditions (p < 0.05). The VOA expanded in the wheelchair condition compared to the bed condition across multiple classes and planes (p < 0.05). Measuring the VOA outside the competition while sitting on a bed may effectively classify players by eliminating equipment influence.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III–IV(A) and BCI competition IV–I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.

Electrical activities of the human brain can be recorded with electroencephalography (EEG). To characterize motor imagery (MI) tasks for brain–computer interface (BCI) implementation is an easy and cost-effective tool. The MI task is represented by a short-time trial of multichannel EEG. In this paper, the signal of each channel of raw EEG is decomposed into a finite set of narrowband signals using a Fourier-transformation-based bandpass filter. Rhythmic components of EEG are represented by each of the narrowband signals that characterize the brain activities related to MI tasks. The subband signals are arranged to extend the dimension of the EEG trial in the spatial domain. The spatial features are extracted from the set of extended trials using a common spatial pattern (CSP). An optimum number of features are employed to classify the motor imagery tasks using an artificial neural network. An integrated approach with full-band and narrowband signals is implemented to derive discriminative features for MI classification. In addition, the subject-dependent parameter optimization scheme enhances the performance of the proposed method. The performance evaluation of the proposed method is obtained using two publicly available benchmark datasets (Dataset I and Dataset II). The experimental results in terms of classification accuracy (93.88% with Dataset I and 91.55% with Dataset II) show that it performs better than the recently developed algorithms. The enhanced MI classification accuracy is very much applicable in BCI implementation.

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

Autonomous vehicles (AV) are a hot topic for safe mobility, which inevitably requires sensors to achieve autonomy, but relying too heavily on sensors will be a risk factor. A high-definition map (HD map) reduces the risk by giving geographical information if it covers dynamic information from moving entities on the road. Cooperative intelligent transport systems (C-ITS) are a prominent approach to solving the issue and local dynamic maps (LDMs) are expected to realize the ideal C-ITS. An actual LDM implementation requires a fine database design to be able to update the information to represent potential risks based on future interactions of vehicles. In the present study, we proposed an advanced method for embedding the geographical future occupancy of vehicles into the database by using a binary decision diagram (BDD). In our method, the geographical future occupancy of vehicles was formulated with Kamm’s circle. In computer experiments, sharing BDD-based occupancy data was successfully demonstrated in the ROS-based simulator with the linked list-based BDD. Algebraic operations in exchanged BDDs effectively managed future interactions such as data insertion and timing of collision avoidance in the LDM. This result opened a new door for the realization of the ideal LDM for safety in AVs.

This study presents an effective artificial neural network (ANN) approach to combine measurements from inertial measurement units (IMUs) and time-of-flight (TOF) measurements from an ultra-wideband (UWB) system with OptiTrack Motion Capture System (OptiT-MCS) data to guarantee the positioning accuracy of motion tracking in indoor environments. The proposed fusion approach unifies the following advantages of both technologies: high data rates from the MCS, and global translational precision from the inertial measurement unit (IMU)/UWB localization system. Consequently, it leads to accurate position estimates when compared with data from the IMU/UWB system relative to the OptiT-MCS reference system. The calibrations of the positioning IMU/UWB and MCS systems are utilized in real-time movement with a diverse set of motion recordings using a mobile robot. The proposed neural network (NN) approach experimentally revealed accurate position estimates, giving an enhancement average mean absolute percentage error (MAPE) of 17.56% and 7.48% in the X and Y coordinates, respectively, and the coefficient of correlation R greater than 99%. Moreover, the experimental results prove that the proposed NN fusion is capable of maintaining high accuracy in position estimates while preventing drift errors from increasing in an unbounded manner, implying that the proposed approach is more effective than the compared approaches.