Age Group Discrimination via Free Handwriting Indicators
Eugenio Lomurno Simone Toffoli Davide Di Febbo Matteo Matteucci Francesca Lunardini Simona Ferrante
The growing global elderly population is expected to increase the prevalence
of frailty, posing significant challenges to healthcare systems. Frailty, a
syndrome associated with ageing, is characterised by progressive health
decline, increased vulnerability to stressors and increased risk of mortality.
It represents a significant burden on public health and reduces the quality of
life of those affected. The lack of a universally accepted method to assess
frailty and a standardised definition highlights a critical research gap. Given
this lack and the importance of early prevention, this study presents an
innovative approach using an instrumented ink pen to ecologically assess
handwriting for age group classification. Content-free handwriting data from 80
healthy participants in different age groups (20-40, 41-60, 61-70 and 70+) were
analysed. Fourteen gesture- and tremor-related indicators were computed from
the raw data and used in five classification tasks. These tasks included
discriminating between adjacent and non-adjacent age groups using Catboost and
Logistic Regression classifiers. Results indicate exceptional classifier
performance, with accuracy ranging from 82.5% to 97.5%, precision from 81.8% to
100%, recall from 75% to 100% and ROC-AUC from 92.2% to 100%. Model
interpretability, facilitated by SHAP analysis, revealed age-dependent
sensitivity of temporal and tremor-related handwriting features. Importantly,
this classification method offers potential for early detection of abnormal
signs of ageing in uncontrolled settings such as remote home monitoring,
thereby addressing the critical issue of frailty detection and contributing to
improved care for older adults.