Noriaki Kuwahara

Ensuring that older adults perform chair-based exercises (CBEs) correctly is essential for improving physical outcomes and reducing the risk of injury, particularly in home and community rehabilitation settings. However, evaluating the correctness of movements accurately and objectively outside clinical environments remains challenging. In this study, camera-based methods have been used to evaluate practical exercise quality. A single-camera system utilizing MediaPipe pose estimation was used to capture joint angle data as twenty older adults performed eight CBEs. Simultaneously, surface electromyography (sEMG) recorded muscle activity. Participants were guided to perform both proper and commonly observed incorrect forms of each movement. Statistical analyses compared joint angles and sEMG signals, and a support vector machine (SVM) was trained to classify movement correctness. The analysis showed that correct executions consistently produced distinct joint angle patterns and significantly higher sEMG activity than incorrect ones (p < 0.001). After modifying the selection of joint angle features for Movement 5 (M5), the classification accuracy improved to 96.26%. Including M5, the average classification accuracy across all eight exercises reached 97.77%, demonstrating the overall robustness and consistency of the proposed approach. In contrast, high variability across individuals made sEMG less reliable as a standalone indicator of correctness. The strong classification performance based on joint angles highlights the potential of this approach for real-world applications. While sEMG signals confirmed the physiological differences between correct and incorrect executions, their individual variability limits their generalizability as a sole criterion. Joint angle data derived from a simple single-camera setup can effectively distinguish movement quality in older adults, offering a low-cost, user-friendly solution for real-time feedback in home and community settings. This approach may help support independent exercise and reduce reliance on professional supervision.

Introduction: This study aims to provide a safe, effective, and sustainable resistance training environment for the elderly by modifying chairs and movement systems used during training, particularly under unsupervised conditions. Materials and Methods: The research focused on investigating the effect of modified chair designs on enhancing physical stability during resistance training by involving 19 elderly participants (mean 72.1, SD 4.7). The study measured changes in the body’s acceleration during movements to compare the effectiveness of the modified chairs with those commonly used in chair-based exercise (CBE) training in maintaining physical stability. A system was developed based on experimental video data, which leverages MediaPipe to analyze the videos and compute joint angles, identifying whether the actions are executed correctly. Results and Conclusions: Comparisons revealed that modified chairs offered better stability during sitting (p < 0.001) and stand-up (p < 0.001) resistance training. According to the questionnaire survey results, compared to the regular chair without an armrest, the modified chair provided a greater sense of security and a better user experience for the elderly. Video observations indicated that the correct completion rate for most exercises, except stand-up resistance training, was only 59.75%, highlighting the insufficiency of modified chairs alone in ensuring accurate movement execution. Consequently, the introduction of an automatic system to verify proper exercise performance is essential. The model developed in this study for recognizing the correctness of movements achieved an accuracy rate of 97.68%. This study proposes a new chair design that enhances physical stability during resistance training and opens new avenues for utilizing advanced technology to assist the elderly in their training.

Nishijin is famous for its traditional technique of cutting Japanese paper to a thickness of 0.2 to 0.3 mm and weaving it with warp and weft threads. This technique is called ‘ hikibaku’. In the previous paper, we reported on the creation of a new textile with a new tactile feeling by using natural cowhide instead of Japanese paper used in hikibaku. In this paper, we describe the details of the automation of the hikibaku technique that created the new textile, and conduct a durability test of the newly produced textile using artificial leather, which is of stable quality, instead of natural cowhide, and discuss its new applications.

Reminiscence and conversation between older adults and younger volunteers using past photographs are very effective in improving the emotional state of older adults and alleviating depression. However, we need to evaluate the emotional state of the older adult while conversing on the past photographs. While electroencephalogram (EEG) has a significantly stronger association with emotion than other physiological signals, the challenge is to eliminate muscle artifacts in the EEG during speech as well as to reduce the number of dry electrodes to improve user comfort while maintaining high emotion recognition accuracy. Therefore, we proposed the CTA-CNN-Bi-LSTM emotion recognition framework. EEG signals of eight channels (P3, P4, F3, F4, F7, F8, T7, and T8) were first implemented in the MEMD-CCA method on three brain regions separately (Frontal, Temporal, Parietal) to remove the muscle artifacts then were fed into the Channel-Temporal attention module to get the weights of channels and temporal points most relevant to the positive, negative and neutral emotions to recode the EEG data. A Convolutional Neural Networks (CNNs) module then extracted the spatial information in the new EEG data to obtain the spatial feature maps which were then sequentially inputted into a Bi-LSTM module to learn the bi-directional temporal information for emotion recognition. Finally, we designed four group experiments to demonstrate that the proposed CTA-CNN-Bi-LSTM framework outperforms the previous works. And the highest average recognition accuracy of the positive, negative, and neutral emotions achieved 98.75%.

Objective: Numerous communication support systems based on reminiscence therapy have been developed. However, when using communication support systems, the emotional assessment of older people is generally conducted using verbal feedback or questionnaires. The purpose of this study is to investigate the feasibility of using Electroencephalography (EEG) signals for automatic emotion recognition during RT for older people.Participants: Eleven older people (mean 71.25, SD 4.66) and seven young people (mean 22.4, SD 1.51) participated in the experiment.Methods: Old public photographs were used as material for reminiscence therapy. The EEG signals of the older people were collected while the older people and young people were talking about the contents of the photos. Since emotions change slowly and responses are characterized by delayed effects in EEG, the depth models LSTM and Bi-LSTM were selected to extract complex emotional features from EEG signals for automatic recognition of emotions.Results: The EEG data of 8 channels were inputted into the LSTM and Bi-LSTM models to classify positive and negative emotions. The recognition highest accuracy rate of the two models were 90.8% and 95.8% respectively. The four-channel EEG data based Bi-LSTM also reached 94.4%.Conclusion: Since the Bi-LSTM model could tap into the influence of “past” and “future” emotional states on the current emotional state in the EEG signal, we found that it can help improve the ability to recognize positive and negative emotions in older people. In particular, it is feasible to use EEG signals without the necessity of multimodal physiological signals for emotion recognition in the communication support systems for reminiscence therapy when using this model.

In Japan, a shift in family patterns has led to a sense of social isolation among older people, which increases the risk of major neurocognitive disorder. Interventions for them using old photos to implement reminiscence therapy (RT) have been proved to be effective. A super-aged society has in turn led to a shortage of medical resources and older people prefer home care over institutional care. Therefore, there is an urgent need for volunteers to help in RT. However, the age of volunteers tends to be increasingly younger. The lack of knowledge and experience of the past for the young volunteers makes it difficult for them to select appropriate stimulated materials. To improve this situation, a library of old photos for RT was developed to support conversation between the two generations. A two-factor experiment and emotion assessment scales were designed to explore the effect of different old photo types on the fluency of conversation between the two generations and their emotion. It was found that the types of old photos have little effect on older people and that conversations were almost pleasant. However, the pleasantness of older people was enhanced when using photos that they wanted to talk about (P= 0.006). Meanwhile, pleasure in conversation of the older people increased with the attention of the young people to the topic (R= 0.304,p< 0.001). Conversely, photo type has a strong impact on young people. When photos are selected that older people do not want to talk about or photos that young people do not know the content and are not interested in, concern for the topic of young people drops dramatically. Therefore, when RT, it is important to avoid using the types of photos above that cause a drop in younger people's attention.

Recently, human patient simulators have been widely developed as substitutes for real patients with the objective of applying them as training tools in nursing education. Such simulated training is perceived as beneficial for imparting the required practical skills to students. Considering the aging world population, this study aimed to develop a robot patient for training nursing students in the sit-to-stand (STS) transfer skill, which is indispensable in caring for elderly people. To assess a student’s skill, the robot patient should be able to access the skill correctness and behave according to whether the skill is correctly or incorrectly implemented. Accordingly, an STS control method was proposed to reproduce the different STS movements during correct and incorrect applications of the skill by the nurses. The lower limbs of a prototype robot were redesigned to provide an active joint with a compliant unit, which enables the measurement of external torque and flexibility of the human joint to be reproduced. An experiment was conducted with four nurse teachers, each of whom was asked to demonstrate both correct and incorrect STS transfer skills. The results of the external torque and joint torque measured in robot’s lower limbs revealed that a significant difference (p < 0.05) between correct and incorrect skills. It also indicates the introduction of the proposed control method for the robot can satisfy the requirement of the assessment of STS skill. Among the various measurements conducted, the external torque of the hip joint exhibited the most significant difference and therefore represented the most robust measure for assessing whether the STS transfer skill was correctly applied.

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To more efficiently enhance the patient transfer skills of nursing students, this study aims to integrate a transfer skills evaluation system and a robot patient. The evaluation parameters, namely, the translational acceleration of the waist, rotational speed of the chest, and joint angles of the shoulder, hip, and knee, were selected on the basis of the pre-experimental results obtained with a simulated patient acted by the human individuals. To measure these parameters, inertial measurement unit (IMU) and angular position sensors were installed on the robot patient. An experiment was conducted with four nursing teachers to verify whether the robot patient could distinguish the incorrect methods of the transfer skills, determined to be a common mistake made by the nurses. According to the results, most transfer steps had the same effect on the simulated patient and the robot patient, which demonstrates that the robot patient is a suitable substitute for an actual patient. However, in certain steps, the robot patient was not able to distinguish between the correct and incorrect methods using the chosen parameters owing to the differences being insignificant. These insignificant differences were mostly attributed to the passive joint design of the robot patient.