In the elderly, geriatric problems such as the risk of fall or frailty are a challenge for society. Patients with frailty present difficulties in walking and higher fall risk. The use of sensors for gait analysis allows the detection of objective parameters related to these pathologies and to make an early diagnosis. Inertial Measurement Units (IMUs) are wearables that, due to their accuracy, portability, and low price, are an excellent option to analyze human gait parameters in health-monitoring applications. Many relevant gait parameters (e.g., step time, walking speed) are used to assess motor, or even cognitive, health problems in the elderly, but we perceived that there is not a full consensus on which parameters are the most significant to estimate the risk of fall and the frailty state. In this work, we analyzed the different IMU-based gait parameters proposed in the literature to assess frailty state (robust, prefrail, or frail) or fall risk. The aim was to collect the most significant gait parameters, measured from inertial sensors, able to discriminate between patient groups and to highlight those parameters that are not relevant or for which there is controversy among the examined works. For this purpose, a literature review of the studies published in recent years was carried out; apart from 10 previous relevant reviews using inertial and other sensing technologies, a total of 22 specific studies giving statistical significance values were analyzed. The results showed that the most significant parameters are double-support time, gait speed, stride time, step time, and the number of steps/day or walking percentage/day, for frailty diagnosis. In the case of fall risk detection, parameters related to trunk stability or movements are the most relevant. Although these results are important, the total number of works found was limited and most of them performed the significance statistics on subsets of all possible gait parameters; this fact highlights the need for new frailty studies using a more complete set of gait parameters.

Falls are a severe problem in older adults, often resulting in severe consequences such as injuries or loss of consciousness. It is crucial to screen fall risk in order to prescribe appropriate therapies that can potentially prevent falls. Identifying individuals who have experienced falls in the past, commonly known as fallers, is used to evaluate fall risk, as a prior fall indicates a higher likelihood of future falls. The methods that have the most support from evidence are Gait Speed (GS) and Time Up and Go (TUG), which use specific cut-off values to evaluate the fall risk. There have been proposals for alternative methods that use wearable sensor technology to improve fall risk assessment. Although these technological alternatives are promising, further research is necessary to validate their use in clinical settings. In this study, we propose a method for identifying fallers based on a Support Vector Machine (SVM) classifier. The inputs for the classifier are the gait parameters obtained from a 30-minute walk recorded using an Inertial Measurement Unit (IMU) placed at the foot of patients. We validated our proposed method using a sample of 157 patients aged over 70 years. Our findings indicate significant differences (p< 0.05) in stride speed, clearance, angular velocity, acceleration, and coefficient of variability among steps between fallers and non-fallers. The proposed method demonstrates the its potential to classify fallers with an accuracy of $[79.6]{\%}$ , slightly outperforming the GS method which provides an accuracy of $[77.0]{\%}$ , and also overcomes its dependency on the cut-off speed to determine fallers. This method could be valuable in detecting fallers during long-term monitoring that does not require periodic evaluations in a clinical setting.

The use of gait analysis techniques to identify health conditions has increased significantly in recent years. Among the many technologies available, inertial sensor-based solutions are one of the most popular due to their cost, accuracy and portability. However, the accuracy obtained in gait analysis is primarily determined by a reliable segmentation of the steps. Most classical algorithms use some direct information extracted from accelerometers and gyroscopes, such as angles or signal magnitudes, and although they are effective for standard walking modes, they are very sensitive to unusual walking styles. In this sense, it would be desirable to obtain an effective and robust walking-style segmentation method. In this paper, we analyze and compare a typical angular velocity-based algorithm for gait segmentation (AVGS), a commercial software for gait analysis (GaitUpLab) and a new algorithm proposed by the authors based on foot forward displacement for gait segmentation (FoDiGS). The three algorithms have been evaluated under different walking-style tests using Optitrack optical system (gold standard). The new proposed FoDiGS algorithm detects 96.9% of the 3205 steps analyzed and improves the gait parameter estimation, decreasing the Mean Relative Error by 20.89% against the AVGS algorithm and by 28.87% compared with the commercial system GaitUp. The results suggest that the proposed method, which does not require any previous training with a database or adaptive thresholds, provides an accurate segmentation method for different walking modes and outperforms other well-known methods.

Background: The World Health Organization recommends health initiatives focused on the early detection of frailty and falls. Objectives: 1—To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without falls. 2—To identify variables that stratify participants according to frailty status and falls. 3—To verify the sensitivity, specificity and accuracy of the model that stratifies participants according to frailty status and falls. Methods: Observational, multicenter case-control study. Participants, adults over 70 years with and without falls were recruited from two outpatient clinics and three nursing homes from September 2021 to March 2022. Clinical variables and gait parameters were gathered using the G-STRIDE inertial sensor. Random Forest regression was applied to stratify participants. Results: 163 participants with a mean age of 82.6 ± 6.2 years, of which 118 (72%) were women, were included. Significant differences were found in all gait parameters (both conventional assessment and G-STRIDE evaluation). A hierarchy of factors contributed to the risk of frailty and falls. The confusion matrix and the performance metrics demonstrated high accuracy in classifying participants. Conclusions: Gait parameters, particularly those assessed by G-STRIDE, are effective in stratifying individuals by frailty status and falls. These findings underscore the importance of gait analysis in early intervention strategies.

Multicomponent exercise effectively prevents falls in older adults, but access to supervised programmes may be limited. This study compared the effectivene

The GAIT2CARE dataset provides information for studying the effects of exercise programs on gait and functional performance in older adults. Data were collected in a multicenter quasi-experimental trial involving 127 participants aged 70 years or older, recruited from three Spanish public hospitals. Participants were assigned to two intervention groups: an on-site group program delivered in the hospital and a home-based program guided remotely via the VIVIFIL mobile app. Assessments were conducted at baseline (week 0) and after the 8-week intervention (week 8), with additional follow-up at weeks 4, 12, and 24. The dataset includes sociodemographic and anthropometric information, standardized functional test results, gait parameters derived from foot-mounted inertial measurement units (IMUs), and responses to satisfaction and adverse event questionnaires. The data are provided in a structured Excel file summarizing participant-level results across weeks, complemented by raw inertial signals and processed step-by-step gait parameters in CSV format. This resource aims to support research on aging, frailty, mobility disorders, and the evaluation of exercise interventions in geriatric populations.