Identifying neuropathies through time series analysis of postural tests

Villegas, Claudio Meneses; Curinao, Jorge Littin; Aqueveque, David Coo; Guerrero-Henríquez, Juan; Matamala, Martín Vargas

doi:10.1016/j.gaitpost.2022.09.090

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…This approach enables the periodic assessments of users' performance on the game platform over an extended duration, thus facilitating the capture of more accurate and representative data. Previous research has primarily focused on utilizing in-game data from serious games for the purpose of cognitive screening [17,[20][21][22][23], and exergames for motor training [24][25][26], with occasional applications in physical health assessments [30,31]. In contrast, our proposed work addresses the challenge of designing a game platform that functions as both an intervention and an assessment tool for evaluating motor and cognitive abilities in elderly individuals.…”

Section: Discussionmentioning

confidence: 99%

“…Villegas et al used time series analysis on the data collected from 32 participants using a WBB platform to distinguish between healthy individuals, those with diabetes, and those with diabetic neuropathy [31]. Utilizing statistical techniques and machine learning, a probabilistic model was created, yielding over 98% accuracy.…”

Section: Physical Health Assessmentmentioning

confidence: 99%

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A Machine-Learning-Based Motor and Cognitive Assessment Tool Using In-Game Data from the GAME2AWE Platform

Danousis

Goumopoulos

2023

Informatics

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With age, a decline in motor and cognitive functionality is inevitable, and it greatly affects the quality of life of the elderly and their ability to live independently. Early detection of these types of decline can enable timely interventions and support for maintaining functional independence and improving overall well-being. This paper explores the potential of the GAME2AWE platform in assessing the motor and cognitive condition of seniors based on their in-game performance data. The proposed methodology involves developing machine learning models to explore the predictive power of features that are derived from the data collected during gameplay on the GAME2AWE platform. Through a study involving fifteen elderly participants, we demonstrate that utilizing in-game data can achieve a high classification performance when predicting the motor and cognitive states. Various machine learning techniques were used but Random Forest outperformed the other models, achieving a classification accuracy ranging from 93.6% for cognitive screening to 95.6% for motor assessment. These results highlight the potential of using exergames within a technology-rich environment as an effective means of capturing the health status of seniors. This approach opens up new possibilities for objective and non-invasive health assessment, facilitating early detections and interventions to improve the well-being of seniors.

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