Ageing is associated with cognitive and functional decline, which can hamper daily activities and independent living. Chronic diseases may intensify this process. Early detection of unhealthy decline is key but hindered by similarity to normal ageing. This study presents an approach for early screening of healthy ageing, using an instrumented ink pen to ecologically assess handwriting performance in three age groups: 40-59, 60-69 and 70+ years old. Raw handwriting data from 60 healthy subjects were used to extract fourteen indicators related to gesture and tremor. The indicators were then used to discriminate between subjects of different age groups in three binary classification tasks, using machine learning algorithms. This approach produced remarkable results, particularly in identifying subjects at the very beginning of the ageing process (Group 2) from elderly subjects (Group 3), achieving an accuracy of 97.5%, an F1 score of 97.44% and a ROC-AUC of 95%. Analysis of the Shapley values revealed age-dependent sensitivity of handwriting and tremor-related indicators. The proposed method represents a promising solution for early detection of abnormal signs of ageing, designed for remote, non-invasive, unsupervised home monitoring to improve the care of older adults.