Recently, several studies have been conducted to lower the cost of transport of human by adding external joint stiffness elements. However, it has not been clearly elucidated whether adaptation time is required for human subjects to adapt to the added external joint stiffness. In this study, carbon plates in the form of shoe midsoles were added to the metatarsophalangeal joint, and the lower limb joint torque and mechanical energy consumption were compared before and after a total of 5 sessions (2.5 weeks) of running. A total of 11 young healthy participants exhibited higher elastic energy storage in carbon plates in the fifth session compared to the first session, and lower power in the ankle joint. This suggests that a single training session may be insufficient to validate the efficiency effect of added joint stiffness, and the human body seems to increase the elastic energy stored in the assistive joint stiffness and its reutilization.

This study analyzed the occurrence of abnormal muscle coactivations based on the assistance of upper limb weight during reaching task in stroke patients. Nine chronic stroke survivors with hemiplegia performed reaching tasks using a programmable haptic robot. Electromyography (EMG) coactivation levels in the upper limb muscles were analyzed using a linear model describing the activation levels of two muscles when the patient's upper limb weight was assisted at 0%, 25%, and 50%. As the upper limb weight assistance of the haptic robot decreased, the magnitude of the EMG signal in both the deltoid and biceps muscles increased simultaneously on both the paretic and non-paretic sides. However, no difference was found between the paretic and non-paretic sides when comparing the slope of the linear model describing the activation relationship between the deltoid and biceps. The aforementioned results suggest that in some stroke survivors, the deltoids, triceps, and biceps on the paretic side may not be abnormally coupled when supporting the upper limbs against gravity. Furthermore, these results suggest that the combination of haptic robots and EMG analysis might be utilized for evaluating abnormal coactivations in stroke patients.

This research investigates the influence of chair tilt functionality on biometric signals and user comfort, addressing the ergonomic challenges posed by modern sedentary lifestyles. Through an experimental study involving eight male participants, the impact of chair tilt on electromyography (EMG), heart rate, metabolic rate, pressure distribution, and distance between the lumbar spine and the lumbar support part of the chair was measured across different seating postures. The study utilized chairs with both synchronous and non-synchronous tilt mechanisms to explore how adjustments in chair design affect user comfort and physiological responses during prolonged sitting. Key findings suggest that chair tilt functionality can significantly reduce muscle activity and energy expenditure, enhancing user comfort and potentially mitigating health risks associated with prolonged sedentary behavior. Notably, the study revealed a preference among participants for chairs that aligned the rotational center of the tilt with the hip joint, highlighting the importance of this ergonomic feature in enhancing user comfort. Additionally, the research proposes a novel methodology for assessing seating comfort through the analysis of both biometric and physical signals, providing valuable insights for the development of ergonomic chair designs focused on user health and comfort.

Purpose : This study conducted a systematic review and meta-analysis to evaluate the clinical effectiveness of laser acupuncture for each condition using information from laser acupuncture trials registered on clinicaltrials.gov from January 2013 to June 2023. Methods : We quantitatively and qualitatively analysed the results of 16 clinical trials of laser acupuncture whose research results were confirmed. A risk of bias assessment was also carried out to assess the quality of each clinical trial. Results : A meta-analysis including three clinical trials was conducted to evaluate the comparative effectiveness of laser acupuncture and sham laser acupuncture in reducing pain and found that the laser acupuncture group had a statistically significant reduction in pain compared with the sham laser acupuncture group. In addition, 11 of the 13 trials not included in the meta-analysis showed a positive effect of laser acupuncture. Conclusion : Although laser acupuncture has a long history of clinical use and a lot of research, there is still some scepticism due to the lack of a clear mechanism of action and inconsistent reports of clinical effectiveness. In addition, there is a significant lack of systematic reviews of clinical evidence for major disease specific, and ongoing research is needed to establish an objective evidence base for the clinical effectiveness of laser acupuncture.

Diagnosing wounds presents a significant challenge in clinical settings due to its complexity and the subjective assessments by clinicians. Wound deep learning algorithms quantitatively assess wounds, overcoming these challenges. However, a limitation in existing research is reliance on specific datasets. To address this limitation, we created a comprehensive dataset by combining open dataset with self-produced dataset to enhance clinical applicability. In the annotation process, machine learning based on Gradient Vector Flow (GVF) was utilized to improve objectivity and efficiency over time. Furthermore, the deep learning model was equipped U-net with residual blocks. Significant improvements were observed using the input dataset with images cropped to contain only the wound region of interest (ROI), as opposed to original sized dataset. As a result, the Dice score remarkably increased from 0.80 using the original dataset to 0.89 using the wound ROI crop dataset. This study highlights the need for diverse research using comprehensive datasets. In future study, we aim to further enhance and diversify our dataset to encompass different environments and ethnicities.

Vitiligo is a condition characterized by the destruction or dysfunction of melanin-producing cells in the skin, resulting in a loss of skin pigmentation. Facial vitiligo, specifically affecting the face, significantly impacts patients' appearance, thereby diminishing their quality of life. Evaluating the efficacy of facial vitiligo treatment typically relies on subjective assessments, such as the Facial Vitiligo Area Scoring Index (F-VASI), which can be time-consuming and subjective due to its reliance on clinical observations like lesion shape and distribution. Various machine learning and deep learning methods have been proposed for segmenting vitiligo areas in facial images, showing promising results. However, these methods often struggle to accurately segment vitiligo lesions irregularly distributed across the face. Therefore, our study introduces a framework aimed at improving the segmentation of vitiligo lesions on the face and providing an evaluation of vitiligo lesions. Our framework for facial vitiligo segmentation and lesion evaluation consists of three main steps. Firstly, we perform face detection to minimize background areas and identify the face area of interest using high-quality ultraviolet photographs. Secondly, we extract facial area masks and vitiligo lesion masks using a semantic segmentation network-based approach with the generated dataset. Thirdly, we automatically calculate the vitiligo area relative to the facial area. We evaluated the performance of facial and vitiligo lesion segmentation using an independent test dataset that was not included in the training and validation, showing excellent results. The framework proposed in this study can serve as a useful tool for evaluating the diagnosis and treatment efficacy of vitiligo.

Steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) is one of the promising systems that can serve as an alternative input device due to its stable and fast performance. However, one of the major bottlenecks is that some individuals exhibit no or very low SSVEP responses to flickering stimulation, known as SSVEP illiteracy, resulting in low performance on SSVEP-BCIs. However, a lengthy duration is required to enhance multiple SSVEP responses using traditional single-frequency transcranial alternating current stimulation (tACS). This research proposes a novel approach using dual-frequency tACS (df-tACS) to potentially enhance SSVEP by targeting the two frequencies with the lowest signal-to-noise ratio (SNR) for each participant. Seven participants (five males, average age: 24.42) were exposed to flickering checkerboard stimuli at six frequencies to determine the weakest SNR frequencies. These frequencies were then simultaneously stimulated using df-tACS for 20 minutes, and the experiment was repeated to evaluate changes in SSVEP responses. The results showed that df-tACS effectively enhances the SNR at each targeted frequency, suggesting it can selectively improve target frequency responses. The study supports df-tACS as a more efficient solution for SSVEP illiteracy, proposing further exploration into multi-frequency tACS that could stimulate more than two frequencies, thereby expanding the potential of SSVEP-BCIs.