Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

The prevalence of anxiety-related disorders, such as panic disorder, social anxiety disorder (SAD), and posttraumatic stress disorder (PTSD), has considerably increased in recent years. At the global level, anxiety disorders affected an estimated 359.2 million individuals in 2021, with an age standardised point prevalence of 4421.9 per 100 000 population, representing an 18% increase since […]

Fall, a leading cause of accidental death and injury in older adults aged 65 and above, has become a rapidly growing health concern in aging populations worldwide. Data-driven methods integrating depth imaging technology have received growing attention in automated fall risk assessment owing to their noninvasiveness and less dependence on healthcare professionals. However, most existing depth image data-based models neglect the inherent physiological and potential functional connections and lack sufficient real-world data validation. To fill the research gap, we developed a novel approach named multiscale skeletal transformer (MSS-Former), leveraging depth image technology and deep-learning models for effective fall risk prediction. Our contributions mainly consist of four parts. First, we introduced a multimodel output feature fusion transformer in fall risk prediction, enabling output merging and weighting from multiple model streams dynamically. Second, we developed an innovative scheme to construct interjoint skeletal topology, systematically focusing on joints’ intrinsic physiological and potential functional connections. Third, we constructed a ResNet-FPN, greatly enhancing multiscale feature extraction capabilities. Fourth, we conducted a field study in a local hospital and performed a comprehensive validation of our developed approach. The comparison results show that our approach achieved outstanding predictive performance, surpassing state-of-the-art methods on the real-world data set, with accuracy, precision, recall, and F1 scores of 97.84%, 97.33%, 96.97%, and 96.92%, respectively. In practice, the proposed approach would be of great value in the timely identification for individuals at high fall risk and facilitate decision making to take appropriate interventions.