Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data.

Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are particularly vulnerable to falls. We study the performance of different computational methods to identify people with osteoporosis who experience a fall by analysing balance parameters. Balance parameters, from eyes open and closed posturographic studies, and prospective registration of falls were obtained from a sample of 126 community-dwelling older women with osteoporosis (age 74.3 ± 6.3) using World Health Organization Questionnaire for the study of falls during a follow-up of 2.5 years. We analyzed model performance to determine falls of every developed model and to validate the relevance of the selected parameter sets. The principal findings of this research were (1) models built using oversampling methods with either IBk (KNN) or Random Forest classifier can be considered good options for a predictive clinical test and (2) feature selection for minority class (FSMC) method selected previously unnoticed balance parameters, which implies that intelligent computing methods can extract useful information with attributes which otherwise are disregarded by experts. Finally, the results obtained suggest that Random Forest classifier using the oversampling method to balance the data independent of the set of variables used got the best overall performance in measures of sensitivity (>0.71), specificity (>0.18), positive predictive value (PPV >0.74), and negative predictive value (NPV >0.66) independent of the set of variables used. Although the IBk classifier was built with oversampling data considering information from both eyes opened and closed, using all variables got the best performance (sensitivity >0.81, specificity >0.19, PPV = 0.97, and NPV = 0.66).

Comparison between passive vision-based system and a wearable inertial-based system for estimating temporal gait parameters related to the GAITRite electronic walkway.

Quantitative gait analysis allows clinicians to assess the inherent gait variability over time which is a functional marker to aid in the diagnosis of disabilities or diseases such as frailty, the onset of cognitive decline and neurodegenerative diseases, among others. However, despite the accuracy achieved by the current specialized systems there are constraints that limit quantitative gait analysis, for instance, the cost of the equipment, the limited access for many people and the lack of solutions to consistently monitor gait on a continuous basis. In this paper, two low-cost systems for quantitative gait analysis are presented, a wearable inertial system that relies on two wireless acceleration sensors mounted on the ankles; and a passive vision-based system that externally estimates the measurements through a structured light sensor and 3D point-cloud processing. Both systems are compared with a reference clinical instrument using an experimental protocol focused on the feasibility of estimating temporal gait parameters over two groups of healthy adults (five elders and five young subjects) under controlled conditions. The error of each system regarding the ground truth is computed. Inter-group and intra-group analyses are also conducted to transversely compare the performance between both technologies, and of these technologies with respect to the reference system. The comparison under controlled conditions is required as a previous stage towards the adaptation of both solutions to be incorporated into Ambient Assisted Living environments and to provide continuous in-home gait monitoring as part of the future work.

Temporal and spatial gait parameters analysis in non-pathological Mexican children.

OBJECTIVE: To examine temporal and spatial gait parameters in Mexican healthy pediatric subjects to describe normal values which could serve as reference data to eventually compare pathological patterns of the Mexican infant gait. MATERIALS AND METHODS: Descriptive study that analyzed the gait of 120 children (61 boys and 59 girls) between the ages of 6 and 13 years old. Modifying factors (age, gender, and footwear) were recorded and its impact over temporal and spatial gait parameters was assessed. The data was stratified according to the modifying factors. A GAITRite System was used for recording the gait data. RESULTS: Significant differences were noted for the following factors: age and the use of footwear. As the individual advances in age, a decrease in number of steps, normalized velocity, velocity, cadence, normalized cadence, normalized step and stride length was observed. In contrast, step and stride length increased. Use of footwear increased velocity (normalized and non-normalized), normalized cadence, step and stride length (normalized and non-normalized), and percentage of stance GC phase; cadence and swing GC percentage diminished. Gender stratification showed no significant differences in any temporal and spatial gait parameters. There were also found significant differences with those reported for normal adult and pediatric gait in the literature. CONCLUSION: Age and footwear modified gait pattern in the studied sample, while gender apparently did not exert any influence on it.