Falls and fall-induced injuries are major global public health problems. Balance and gait disorders have been the second leading cause of falls. Inertial motion sensors and force sensors have been widely used to monitor both static and dynamic balance performance. Based on the detected performance, instant visual, auditory, electrotactile and vibrotactile biofeedback could be provided to augment the somatosensory input and enhance balance control. This review aims to synthesize the research examining the effect of biofeedback systems, with wearable inertial motion sensors and force sensors, on balance performance. Randomized and non-randomized clinical trials were included in this review. All studies were evaluated based on the methodological quality. Sample characteristics, device design and study characteristics were summarized. Most previous studies suggested that biofeedback devices were effective in enhancing static and dynamic balance in healthy young and older adults, and patients with balance and gait disorders. Attention should be paid to the choice of appropriate types of sensors and biofeedback for different intended purposes. Maximizing the computing capacity of the micro-processer, while minimizing the size of the electronic components, appears to be the future direction of optimizing the devices. Wearable balance-improving devices have their potential of serving as balance aids in daily life, which can be used indoors and outdoors.

The aim of our scoping review was to identify and summarize current evidence on the biomechanics of fencing to inform athlete development and injury prevention. Scoping review. Peer-reviewed research was identified from electronic databases using a structured keyword search. Details regarding experimental design, study group characteristics and measured outcomes were extracted from retrieved studies, summarized and information regrouped under themes for analysis. The methodological quality of the evidence was evaluated. Thirty-seven peer-reviewed studies were retrieved, the majority being observational studies conducted with experienced and elite athletes. The methodological quality of the evidence was “fair” due to the limited scope of research. Male fencers were the prevalent group studied, with the lunge and use of a foil weapon being the principal movement evaluated. Motion capture and pedabarography were the most frequently used data collection techniques. Elite fencers exhibited sequential coordination of upper and lower limb movements with coherent patterns of muscle activation, compared to novice fencers. These elite features of neuromuscular coordination resulted in higher magnitudes of forward linear velocity of the body center of mass and weapon. Training should focus on explosive power. Sex- and equipment-specific effects could not be evaluated based on available research.

Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Ankle arthrodesis is one popular surgical treatment for ankle arthritis, chronic instability, and degenerative deformity. However, complications such as foot pain, joint arthritis, and bone fracture may cause patients to suffer other problems. Understanding the internal biomechanics of the foot is critical for assessing the effectiveness of ankle arthrodesis and provides a baseline for the surgical plan. This study aimed to understand the biomechanical effects of ankle arthrodesis on the entire foot and ankle using finite element analyses. A three-dimensional finite element model of the foot and ankle, involving 28 bones, 103 ligaments, the plantar fascia, major muscle groups, and encapsulated soft tissue, was developed and validated. The biomechanical performances of a normal foot and a foot with ankle arthrodesis were compared at three gait instants, first-peak, mid-stance, and second-peak. Changes in plantar pressure distribution, joint contact pressure and forces, von Mises stress on bone and foot deformation were predicted. Compared with those in the normal foot, the peak plantar pressure was increased and the center of pressure moved anteriorly in the foot with ankle arthrodesis. The talonavicular joint and joints of the first to third rays in the hind- and mid-foot bore the majority of the loading and sustained substantially increased loading after ankle arthrodesis. An average contact pressure of 2.14 MPa was predicted at the talonavicular joint after surgery and the maximum variation was shown to be 80% in joints of the first ray. The contact force and pressure of the subtalar joint decreased after surgery, indicating that arthritis at this joint was not necessarily a consequence of ankle arthrodesis but rather a progression of pre-existing degenerative changes. Von Mises stress in the second and third metatarsal bones at the second-peak instant increased to 52 MPa and 34 MPa, respectively, after surgery. These variations can provide indications for outcome assessment of ankle arthrodesis surgery.