Combining principal component analysis and logistic regression for multifactorial fall risk prediction among community-dwelling older adults.

Falls require comprehensive assessment in older adults due to their diverse risk factors. This study aimed to develop an effective fall risk prediction model for community-dwelling older adults by integrating principal component analysis (PCA) with machine learning. Data were collected for 45 fall-related variables from 1630 older adults in Taiwan, and models were developed using PCA and logistic regression.

Feasibility of predicting functional decline in the elderly through key posture information during sit-to-stand movement.

Background: Early detection of functional decline in the elderly in day care centres facilitates timely implementation of preventive and treatment measures.

Research Question: Whether or not a predictive model can be developed by applying image recognition to analyze elderly individuals' posture during the sit-to-stand (STS) manoeuvre.

Methods: We enrolled sixty-six participants (24 males and 42 females) in an observational study design.

Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data.

Fall-risk assessment studies generally focus on identifying characteristics that affect postural balance in a specific group of subjects. However, falls affect a multitude of individuals. Among the groups with the most recurrent fallers are the community-dwelling elderly and stroke survivors.

[Education for Sustainability and the Development of Smart Healthcare Systems: An Exploratory Study].

The development of smart healthcare systems that are accurate and efficient may be used to improve the health and well-being of different age groups. These systems should incorporate a human-centered design approach to ensure that products and services meet user needs and that systems are user-friendly. In this study, an "education for sustainability" perspective is used in tandem with human-factors engineering and human-computer interaction techniques to achieve a creative dimensional design within a workshop setting.

Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test.

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention.

Using a Stacked Autoencoder for Mobility and Fall Risk Assessment Time-Frequency Representations of the Timed Up and Go Test.

Fall risk assessment is very important for the graying societies of developed countries. A major contributor to the fall risk of the elderly is mobility impairment. Timely detection of the fall risk can facilitate early intervention to avoid preventable falls.

Effects of robotic care interventions for dementia care: A systematic review and meta-analysis randomised controlled trials.

Background: The role of robotic care has been studied because it may be a care option applicable to dementia care. However, the effects of robotic care in dementia care are still inconclusive.

Aim: To explore the span of the effects of robotic care intervention among patients with dementia.

Assessing elderly's functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests.

Background: Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly's functional balance based on Short Form Berg Balance Scale (SFBBS) score.

Evaluation of postural stability based on a force plate and inertial sensor during static balance measurements.

Background: Previous research on balance mostly focused on the assessment, training, and improvements of balance through interventions. We investigated tools commonly used to study static balance. Differences in postural stability were analyzed using multiscale entropy (MSE) and feature analysis.

Influence of the Perspectives on the Movement of One-Leg Lifting in an Interactive-Visual Virtual Environment: A Pilot Study.

Background: Numerous studies have confirmed the feasibility of active video games for clinical rehabilitation. To maximize training effectiveness, a personal program is necessary; however, little evidence is available to guide individualized game design for rehabilitation. This study assessed the perspectives and kinematic and temporal parameters of a participant's postural control in an interactive-visual virtual environment.

An impact study of the design of exergaming parameters on body intensity from objective and gameplay-based player experience perspectives, based on balance training exergame.

Kinect-based exergames allow players to undertake physical exercise in an interactive manner with visual stimulation. Previous studies focused on investigating physical fitness based on calories or heart rate to ascertain the effectiveness of exergames. However, designing an exergame for specific training purposes, with intensity levels suited to the needs and skills of the players, requires the investigation of motion performance to study player experience.