Gait instability is critical in medicine and healthcare, as it has associations with balance disorder and physical impairment. With the development of sensor technology, despite the fact that numerous wearable gait detection and recognition systems have been designed to monitor users’ gait patterns, they commonly spend a lot of time and effort to extract gait metrics from signal data. This study aims to design an artificial intelligence-empowered and economic-friendly gait monitoring system. A pair of intelligent shoes with a single inertial sensor and a smartphone application were developed as a gait monitoring system to detect users’ gait cycle, stand phase time, swing phase time, stride length, and foot clearance. We recruited 30 participants (24.09 ± 1.89 years) to collect gait data and used the Vicon motion capture system to verify the accuracy of the gait metrics. The results show that the gait monitoring system performs better on the assessment of the gait metrics. The accuracy of stride length and foot clearance is 96.17% and 92.07%, respectively. The artificial intelligence-empowered gait monitoring system holds promising potential for improving gait analysis and monitoring in the medical and healthcare fields.

Adolescent idiopathic scoliosis (AIS) is often associated with unbalanced posture and proprioceptive deficits and is particularly prone to progression during puberty. However, limited research has addressed early intervention for mild curves (10-20 degrees). This study aims to examine the effects of a soft bracing intervention using the Posture Correction Girdle (PCG) on 3D postural balance and proprioception in adolescents with early-stage scoliosis. Cobb angles were measured with the X-ray Imaging before and after a 2-h wear trial with the girdle to assess immediate curve reduction. Postural angles were visualized using 3D body scanning. Proprioception was assessed using a motion capture system, focusing on trunk, neck, elbow, and knee movements under the eye-open and eye-closed condition. Data were analyzed using descriptive statistics, t-tests, and Pearson’s correlation and partial Mantel tests to examine postural balance and proprioceptive outcomes. Results indicated significant improvements in postural balance in the frontal and sagittal planes, and proprioception in the dominant-side elbow. However, other proprioceptive measures showed limited changes, with responses differing by spinal curvature. Strong correlations emerged between proprioceptive function and postural angles in the thoracic and lumbar regions. These findings suggest that soft bracing can enhance postural control for mild AIS, though individual variability in responses highlights the need for personalized bracing strategies. This study supports soft bracing as a promising therapeutic option, underscoring the importance of individualized assessment to optimize brace design and effectiveness in early-onset scoliosis management.

Intelligent textile clothing is one of the most popular topics in the field. In recent decades, rapid advances have been made in the area of intelligent textile clothing research, and the intellectual structure pertaining to this domain has significantly evolved. We used CiteSpace 6.2.R4, VOSviewer 1.6.19, to evaluate and visualize the results, analyzing articles, countries, regions, institutions, authors, journals, citations, and keywords. Both a macroscopic sketch and a microscopic characterization of the entire knowledge domain were realized. The aim of this paper is to utilize bibliometric and knowledge mapping theories to identify relevant research papers on the subject of smart textiles and clothing that have been published by the China Knowledge Network Web of Science (WOS) within the last decade. It is concluded that the main topics of smart textile and garment research can be divided into nine categories: wearable electronics, smart textiles, flexible antennas, energy storage, textile actuators, mechanical properties, asymmetric supercapacitors, carbon nanotubes, and fiber extrusion. In addition to the latter analysis, emerging trends and future research foci were predicted. This review will help scientists discern the dynamic evolution of intelligent textile clothing research as well as highlight areas for future research.

As a sports facility category, a sports bra is regarded as an enabling technology garment with advanced functions and new cultural meanings. Women, particularly elderly women, are encouraged to wear a sports bra during physical activity because wearing a sports bra can not only prevent them from exercise-induced injuries but also increase the acceptance of their bodies. However, most studies usually targeted younger women. The requirements and expectations of elderly women on sports bras differ from younger individuals. Hence, this study aims to advance and explore the design demands, providing insights into age-friendly sports bra design by using more permeable physical and psychological boundaries based on conceptual frameworks. The sports bra evaluation words were extracted and calculated to construct the network. The appearance frequency of keywords, closeness centrality, betweenness centrality, and interconnection between every pair of words were illustrated and analyzed by using semantic-based network analysis. Network analysis deduced that the discomfort of sports bras was closely related with the support, tightness, cup design, and strap design. Beside, thermal management contributes significantly to the comfort. The outlook of a sports bra was associated with cup design. The Page-rank result showed that cup, tightness, hot, straps and quality were the top five important design attributes. The unobservable mechanisms underlying the keywords provide an alternative model, which can help to explore the key improvements for sports bra design.

The measurement and prediction of breast skin deformation are key research directions in health-related research areas, such as cosmetic and reconstructive surgery and sports biomechanics. However, few studies have provided a systematic analysis on the deformations of aging breasts. Thus, this study has developed a model order reduction approach to predict the real-time strain of the breast skin of seniors during movement. Twenty-two women who are on average 62 years old participated in motion capture experiments, in which eight body variables were first extracted by using the gray relational method. Then, backpropagation artificial neural networks were built to predict the strain of the breast skin. After optimization, the R-value for the neural network model reached 0.99, which is within acceptable accuracy. The computer-aided system of this study is validated as a robust simulation approach for conducting biomechanical analyses and predicting breast deformation.