In situ 4D tomography image analysis framework to follow sintering within 3D-printed glass scaffolds.

We propose a novel image analysis framework to automate analysis of X-ray microtomography images of sintering ceramics and glasses, using open-source toolkits and machine learning. Additive manufacturing (AM) of glasses and ceramics usually requires sintering of green bodies. Sintering causes shrinkage, which presents a challenge for controlling the metrology of the final architecture. Therefore, being able to monitor sintering in 3D over time (termed 4D) is important when developing new porous ceramics or glasses. Synchrotron X-ray tomographic imaging allows in situ, real-time capture of the sintering process at both micro and macro scales using a furnace rig, facilitating 4D quantitative analysis of the process. The proposed image analysis framework is capable of tracking and quantifying the densification of glass or ceramic particles within multiple volumes of interest (VOIs) along with structural changes over time using 4D image data. The framework is demonstrated by 4D quantitative analysis of bioactive glass ICIE16 within a 3D-printed scaffold. Here, densification of glass particles within 3 VOIs were tracked and quantified along with diameter change of struts and interstrut pore size over the 3D image series, delivering new insights on the sintering mechanism of ICIE16 bioactive glass particles in both micro and macro scales.

Advancements in Textile-Based sEMG Sensors for Muscle Fatigue Detection: A Journey from Material Evolution to Technological Integration.

Textile-based surface electromyography (sEMG) electrodes have emerged as a prominent tool in muscle fatigue assessment, marking a significant shift toward innovative, noninvasive methods. This review examines the transition from metallic fibers to novel conductive polymers, elastomers, and advanced material-based electrodes, reflecting on the rapid evolution of materials in sEMG sensor technology. It highlights the pivotal role of materials science in enhancing sensor adaptability, signal accuracy, and longevity, crucial for practical applications in health monitoring, while examining the balance of clinical precision with user comfort. Additionally, it maps the global sEMG research landscape of diverse regional contributors and their impact on technological progress, focusing on the integration of Eastern manufacturing prowess with Western technological innovations and exploring both the opportunities and challenges in this global synergy. The integration of such textile-based sEMG innovations with artificial intelligence, nanotechnology, energy harvesting, and IoT connectivity is also anticipated as future prospects. Such advancements are poised to revolutionize personalized preventive healthcare. As the exploration of textile-based sEMG electrodes continues, the transformative potential not only promises to revolutionize integrated wellness and preventive healthcare but also signifies a seamless transition from laboratory innovations to real-world applications in sports medicine, envisioning the future of truly wearable material technologies.

Face mediated human-robot interaction for remote medical examination.

Realtime visual feedback from consequences of actions is useful for future safety-critical human-robot interaction applications such as remote physical examination of patients. Given multiple formats to present visual feedback, using face as feedback for mediating human-robot interaction in remote examination remains understudied. Here we describe a face mediated human-robot interaction approach for remote palpation. It builds upon a robodoctor-robopatient platform where user can palpate on the robopatient to remotely control the robodoctor to diagnose a patient. A tactile sensor array mounted on the end effector of the robodoctor measures the haptic response of the patient under diagnosis and transfers it to the robopatient to render pain facial expressions in response to palpation forces. We compare this approach against a direct presentation of tactile sensor data in a visual tactile map. As feedback, the former has the advantage of recruiting advanced human capabilities to decode expressions on a human face whereas the later has the advantage of being able to present details such as intensity and spatial information of palpation. In a user study, we compare these two approaches in a teleoperated palpation task to find the hard nodule embedded in the remote abdominal phantom. We show that the face mediated human-robot interaction approach leads to statistically significant improvements in localizing the hard nodule without compromising the nodule position estimation time. We highlight the inherent power of facial expressions as communicative signals to enhance the utility and effectiveness of human-robot interaction in remote medical examinations.

Decoding of Hand Gestures from Electrocorticography with LSTM Based Deep Neural Network.

Hand gesture decoding is a key component of controlling prosthesis in the area of Brain Computer Interface (BCI). This study is concerned with classification of hand gestures, based on Electrocorticography (ECoG) recordings. Recent studies have utilized the temporal information in ECoG signals for robust hand gesture decoding. In our preliminary analysis on ECoG recordings of hand gestures, we observed different power variations in six frequency bands ranging from 4 to 200 Hz. Therefore, the current trend of including temporal information in the classifier was extended to provide equal importance to power variations in each of these frequency bands. Statistical and Principal Component Analysis (PCA) based feature reduction was implemented for each frequency band separately, and classification was performed with a Long Short-Term Memory (LSTM) based neural network to utilize both temporal and spatial information of each frequency band. The proposed architecture along with each feature reduction method was tested on ECoG recordings of five finger flexions performed by seven subjects from the publicly available 'fingerflex' dataset. An average classification accuracy of 82.4% was achieved with the statistical based channel selection method which is an improvement compared to state-of-the-art methods.

Portable Acquisition of Auditory ERPs: A Pilot Study of Premature Infants.

BACKGROUND: Prior work suggests that event-related potential (ERP) studies in infancy may help predict developmental outcome. METHODS: As part of a longitudinal study of early child development, we used the auditory oddball stimulus paradigm with a portable electroencephalography system to obtain ERP data from two-month-old infants (32 term, six preterm) in Sri Lanka. The mismatch negativity was calculated between 200 and 350 milliseconds after stimulus presentation. RESULTS: We found a significant correlation between birth weight and mismatch negativity (P = 0.046), and our time-frequency analysis indicated power differences between standard and oddball tones at approximately 5 and 18 Hz. There was no significant difference between mismatch negativity in children undergoing ERP studies in a hospital setting (30) versus in the child's home (eight). CONCLUSIONS: Although our modest sample size precludes drawing definitive conclusions, these preliminary results show that it is possible to acquire ERP datasets using currently available portable technology in a hospital or home setting, even in a developing nation such as Sri Lanka. Follow-up of this cohort will include developmental assessments, which will add to the growing literature relating early electrophysiology to developmental outcome.