Optical Marker-Based Motion Capture of the Human Spine: A Scoping Review of Study Design and Outcomes.

Biomechanical analysis of the human spine is crucial to understanding injury patterns. Motion capture technology has gained attention due to its non-invasive nature. Nevertheless, traditional motion capture studies consider the spine a single rigid segment, although its alignment changes during movement.

Developing a virtual reality and AI-based framework for advanced digital manufacturing and nearshoring opportunities in Mexico.

The growing expansion of the manufacturing sector, particularly in Mexico, has revealed a spectrum of nearshoring opportunities yet is paralleled by a discernible void in educational tools for various stakeholders, such as engineers, students, and decision-makers. This paper introduces a state-of-the-art framework, incorporating virtual reality (VR) and artificial intelligence (AI) to metamorphose the pedagogy of advanced manufacturing systems. Through a case study focused on the design, production, and evaluation of a robotic platform, the framework endeavors to offer an exhaustive educational experience via an interactive VR environment, encapsulating (1) Robotic platform system design and modeling, enabling users to immerse themselves in the design and simulation of robotic platforms under varied conditions; (2) Virtual manufacturing company, presenting a detailed virtual manufacturing setup to enhance users' comprehension of manufacturing processes and systems, and problem-solving in realistic settings; and (3) Product evaluation, wherein users employ VR to meticulously assess the robotic platform, ensuring optimal functionality and customer satisfaction.

Implementing a challenge-based learning experience in a bioinstrumentation blended course.

Background: Bioinstrumentation is essential to biomedical engineering (BME) undergraduate education and professional practice. Several strategies have been suggested to provide BME students with hands-on experiences throughout the curriculum, promoting their preparedness to pursue careers in industry and academia while increasing their learning and engagement. This paper describes the implementation of challenge-based learning (CBL) in an undergraduate bioinstrumentation blended course over the COVID-19 pandemic.

Recurrence quantification analysis of center of pressure trajectories for balance and fall-risk assessment in young and older adults.

The prevalence and impact of balance impairments and falls in older adults have motivated several studies on the characterization of human balance. This study aimed to determine the ability of recurrence quantification analysis (RQA) measures to characterize balance control during quiet standing in young and older adults and to discriminate between different fall risk groups. We analyze center pressure trajectories in the medial-lateral and anterior-posterior directions from a publicly available static posturography dataset that contains tests acquired under four vision-surface testing conditions.

Transdisciplinary experiential learning in biomedical engineering education for healthcare systems improvement.

Background: The growing demand for more efficient, timely, and safer health services, together with insufficient resources, put unprecedented pressure on health systems worldwide. This challenge has motivated the application of principles and tools of operations management and lean systems to healthcare processes to maximize value while reducing waste. Consequently, there is an increasing need for professionals with the appropriate clinical experience and skills in systems and process engineering.

A machine learning approach for hypertension detection based on photoplethysmography and clinical data.

High blood pressure early screening remains a challenge due to the lack of symptoms associated with it. Accordingly, noninvasive methods based on photoplethysmography (PPG) or clinical data analysis and the training of machine learning techniques for hypertension detection have been proposed in the literature. Nevertheless, several challenges arise when analyzing PPG signals, such as the need for high-quality signals for morphological feature extraction from PPG related to high blood pressure.

Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.

Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models.

On the use of approximate entropy and sample entropy with centre of pressure time-series.

Background: Approximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to their input parameters: m (subseries length), r (tolerance) and N (data length). Yet, the effects of changing those parameters have been scarcely investigated in the analysis of COP time-series.

Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations.

Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in combination with wearable sensors, mobile phones, and smart-watches. Long-term (nominally 24 h) and short-term (nominally 5 min) HRV features have been widely investigated, physiologically justified and clear guidelines for analysing HRV in 5 min or 24 h are available.

Wearable Inertial Sensors for Fall Risk Assessment and Prediction in Older Adults: A Systematic Review and Meta-Analysis.

Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging evidence-based design of new studies and/or real-life applications. We conducted a systematic review and meta-analysis to appraise the best available evidence in the field.

The contribution of the therapist's competence in the treatment of adolescents with generalized social phobia.

Background: The purpose of this study was to explore which of the outcomes attained by the application of the psychological program Intervención en Adolescentes con Fobia Social (Intervention in Adolescents with Social Phobia) can be attributed to the therapist's competence.

Method: The experimental study consists of three conditions: Waiting list control, Group treated by expert psychologists, and Group treated by inexperienced psychologists, with a sample of 110 Spanish adolescents whose mean age was 15.42 years (SD = 0.