Search

Featured Application: Most existing research centers on examining the effects of sit–stand desks on sitting and standing positions, often overlooking the influence of the workplace environment and seat tilt angles on comfort. This study develops and constructs a model of a standing desk chair to address this gap and explores how seat tilt angles and various task conditions impact chair comfort. In this article, researchers adopt an innovative approach to evaluate the comfort of standing desk chairs, integrating the analysis of pressure distribution and electromyography signals. This holistic method not only delivers an objective assessment of chair comfort but also sheds light on how chairs affect muscle activity. Through the analysis of pressure distribution, researchers gain insight into how standing desk chairs support different body parts, enabling evaluation of their overall comfort and supportiveness. Furthermore, by incorporating electromyography signal analysis, researchers delve into the impact of chairs on muscle activity, thereby facilitating a more comprehensive evaluation of chair comfort. The distinctiveness of this integrated analytical approach lies in its consideration of both pressure distribution on the seated body and changes in muscle activity, resulting in a thorough and precise assessment of chair comfort. This research methodology provides valuable insights and guidance for the design and enhancement of standing desk chairs, ultimately improving user comfort and satisfaction. This study explores the health consequences of prolonged sitting by introducing a standing chair and examining the effects of seat tilt angles on comfort. Using synchronized pressure distribution testing and surface electromyographic (sEMG) signal analysis, we assessed pressure and sEMG responses at tilt angles of 0°, 20°, and 40° during tasks such as computer work, writing, and ironing. Initial measurements with body pressure distribution sensors targeted the buttocks and feet, while surface electromyographic equipment captured signals from the bilateral lumbar erector spinae, vastus lateralis, and gastrocnemius muscles. MATLAB processing facilitated the analysis of integrated electromyographic values and mean power frequencies, elucidating the effects of tilt angles on comfort. Our research findings indicate that a 20° tilt angle significantly enhances comfort during computer and writing tasks and noticeably increases the comfort of the erector spinae muscles during ironing. Conversely, a 0° tilt angle is more beneficial for the vastus lateralis and gastrocnemius muscles. These results underscore the importance of selecting appropriate tilt angles to improve comfort across various tasks. Furthermore, integrating pressure sensors and surface electromyographic signals enables a comprehensive evaluation of chair ergonomic quality, offering valuable insights for chair design optimization. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study—a 42-year-old man with left shoulder disarticulation—EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model's ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users. [ABSTRACT FROM AUTHOR]

The lower limb exoskeleton technology is designed to facilitate the movement of human lower limbs. Significant progress has been made in this technology, which has important implications for rehabilitation patients and individuals who are eager to enhance their mobility. Electromyogram (EMG) signals, which encompass the complexity of human physiology, are integrated into lower limb exoskeletons due to their deep connection to movement and predictability before movement begins, and this integration is expected to enable intelligent control and improved human-computer interaction. This review explores a pattern based on EMG signals for identifying human motor intent in lower limb exoskeletons. Firstly, the development of lower limb exoskeleton and the existing lower limb exoskeleton products are systematically described. Combined with the intelligent control system of wearable device, the main methods and research progress of recognizing the motion intention of lower limb exoskeleton by surface EMG are discussed. It shows that the use of surface EMG can effectively improve the human-machine interaction of lower limb exoskeleton. Together, the study provides insight into the challenges that are hindering the commercialization of the market and provides a perspective on the future development of EMG signals.

In the present work, decision trees are employed to determine the activation patterns in electromyographic signals in multiple muscles of interest. Due to the interaction of several muscles when performing a movement, it is common for several muscles to activate simultaneously. The problem is to determine if any muscle of interest is active and, in the case of multiple active muscles, which of them is the predominant one. In turn, we are interested in determining if a muscle is active enough to associate it with a voluntary movement. The proposed scheme is based on decision trees (which are supervised learning strategies for classification), and the envelope of a signal as a classification feature. The detection of an activation is performed in two stages: the first stage determines the candidate muscle to be active and a second stage determines its activation level. The algorithm was tested in control of a robotic arm in real time. The activation of a muscle is associated with a control command of a DC motor of the robotic arm. The proposed algorithm made it possible to manipulate the movements of that robotic arm to perform a simple task using electromyographic signals. [ABSTRACT FROM AUTHOR]

Aims: To investigate the effects of one‐time soft tissue therapy (STT) on pelvic floor muscle (PFM) electromyographic signals in women with stress and/or mixed urinary incontinence. Methods: An intervention study conducted with 63 women with stress and/or mixed urinary incontinence. Participants were randomly assigned to either the one‐time STT group (experimental group) or the control group. The same teaching model for voluntary contraction and relaxation of the PFM was used for all participants. Electromyographic signals from the PFM during functional tasks were the primary clinical outcome measures at baseline and immediately after the intervention. Electromyographic signals were analyzed using root mean square amplitude. Results: There was no significant difference between groups in electromyographic PFM signals in prebaseline rest (mean difference: −0.146 [95% confidence interval (CI): −0.44 to 0.148; p = 0.470]), phasic contractions (mean difference: 0.807 [95% CI: 0.123–1.491; p = 0.459]), tonic contractions (mean difference: 1.06 [95% CI: 0.255–1.865; p = 0.302]), endurance contractions (mean difference: 0.896 [95% CI: 0.057–1.735; p = 0.352]) and postbaseline rest (mean difference: −0.123 [95% CI: −0.406 to 0.16; p = 0.591]) immediately after the one‐time STT intervention. Conclusion: A one‐time STT intervention does not appear to effectively alter electromyographic signal of the PFM in women with urinary incontinence. Due to the limitations of the study, further research is needed to confirm these results. [ABSTRACT FROM AUTHOR]

This study explores the health consequences of prolonged sitting by introducing a standing chair and examining the effects of seat tilt angles on comfort. Using synchronized pressure distribution testing and surface electromyographic (sEMG) signal analysis, we assessed pressure and sEMG responses at tilt angles of 0°, 20°, and 40° during tasks such as computer work, writing, and ironing. Initial measurements with body pressure distribution sensors targeted the buttocks and feet, while surface electromyographic equipment captured signals from the bilateral lumbar erector spinae, vastus lateralis, and gastrocnemius muscles. MATLAB processing facilitated the analysis of integrated electromyographic values and mean power frequencies, elucidating the effects of tilt angles on comfort. Our research findings indicate that a 20° tilt angle significantly enhances comfort during computer and writing tasks and noticeably increases the comfort of the erector spinae muscles during ironing. Conversely, a 0° tilt angle is more beneficial for the vastus lateralis and gastrocnemius muscles. These results underscore the importance of selecting appropriate tilt angles to improve comfort across various tasks. Furthermore, integrating pressure sensors and surface electromyographic signals enables a comprehensive evaluation of chair ergonomic quality, offering valuable insights for chair design optimization.

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study—a 42-year-old man with left shoulder disarticulation—EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model’s ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users.