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.

翻译：全球老年人口的持续增长预计将增加虚弱症的流行率，给医疗系统带来重大挑战。虚弱是一种与衰老相关的综合征，其特征为健康状况进行性衰退、对应激源的易感性增加以及死亡风险上升。它给公共卫生带来沉重负担，并降低患者的生活质量。目前缺乏一种普遍接受的虚弱评估方法和标准化定义，这凸显了关键的研究空白。鉴于这一缺陷及早期预防的重要性，本研究提出了一种创新方法，利用配备传感器的墨水笔在生态情境下评估手写特征以进行年龄组分类。研究分析了来自80名健康参与者（分属20-40、41-60、61-70及70岁以上年龄组）的无内容手写数据。从原始数据中计算出14个与手势及震颤相关的指标，并用于五项分类任务，包括使用Catboost和逻辑回归分类器对相邻及非相邻年龄组进行区分。结果表明分类器性能优异，准确率介于82.5%至97.5%，精确率介于81.8%至100%，召回率介于75%至100%，ROC-AUC介于92.2%至100%。通过SHAP分析实现模型可解释性，揭示了时间相关及震颤相关手写特征对年龄的依赖性敏感度。重要的是，该分类方法为在非受控环境（如远程居家监测）中早期检测异常衰老迹象提供了潜力，从而应对虚弱检测的关键问题，并助力改善老年人护理水平。