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Ageing is a physiological phenomenon associated with cognitive and functional decline which, in the long term, could hamper the execution of daily life activities and threaten both social and independent life. The onset of chronic diseases can intensify this process, increasing the risk of hospitalisation and admission to long term care. This represents a significant burden on public health and reduces the quality of life of those affected. Early detection of unhealthy decline is therefore key, but the similarity to normal ageing hinders its prompt screening. This study presents a first step towards the early screening of unhealthy ageing, based on an innovative instrumented ink pen to ecologically assess handwriting performance in different age groups: 40-59 (Group 1), 60-69 (Group 2) and 70+ (Group 3) years old. Raw handwriting data were collected from 60 healthy subjects and 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 a selection of machine learning algorithms. This approach produced remarkable results, particularly in the task of greatest interest, 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%. Explainability of the model, facilitated by the analysis of the Shapley values of the learned indicators, revealed age-dependent sensitivity of handwriting and tremor-related indicators. The proposed method represents a promising solution for the early detection of abnormal signs of ageing, and is designed for the remote, non-invasive, unsupervised home monitoring, to improve the care of older adults.
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Introduction: Since the uptake of digitizers, quantitative spiral drawing assessment allowed gaining insight into motor impairments related to Parkinson's disease. However, the reduced naturalness of the gesture and the poor user-friendliness of the data acquisition hamper the adoption of such technologies in the clinical practice. To overcome such limitations, we present a novel smart ink pen for spiral drawing assessment, intending to better characterize Parkinson's disease motor symptoms. The device, used on paper as a normal pen, is enriched with motion and force sensors. Methods: Forty-five indicators were computed from spirals acquired from 29 Parkinsonian patients and 29 age-matched controls. We investigated between-group differences and correlations with clinical scores. We applied machine learning classification models to test the indicators ability to discriminate between groups, with a focus on model interpretability. Results: Compared to control, patients' drawings were characterized by reduced fluency and lower but more variable applied force, while tremor occurrence was reflected in kinematic spectral peaks selectively concentrated in the 4-7 Hz band. The indicators revealed aspects of the disease not captured by simple trace inspection, nor by the clinical scales, which, indeed, correlate moderately. The classification achieved 94.38% accuracy, with indicators related to fluency and power distribution emerging as the most important. Conclusion: Indicators were able to significantly identify Parkinson's disease motor symptoms. Our findings support the introduction of the smart ink pen as a time-efficient tool to juxtapose the clinical assessment with quantitative information, without changing the way the classical examination is performed.
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Handwriting skills could be highly impaired in patients affected by Parkinson's disease (PD), and for this reason its analysis had always been considered relevant. In handwriting assessment, Archimedes spiral drawing is one of the most proposed tasks, due to its peculiar shape and ease of execution. In the last decades, digitizing tablets had been widely employed for the evaluation of the spiral performance, providing a cheap and non-invasive way to gather quantitative information, to be combined with the classical clinical examination. Despite this advantage, such approach cannot easily be adopted in an unsupervised scenario and lacks the natural feel of the traditional pen-and-paper approach. This work aims at overcoming these limitations by employing a smart ink pen, designed to write on paper and instrumented with inertial and force sensors, to automatically collect data related to spiral drawing execution of PD patients (n=30) and age-matched healthy controls (n=30). From the raw data, several time and frequency domains features were extracted and compared between the groups. The statistical analysis revealed some significant differences, showing less smooth acceleration and force profiles for PD patients. However, given the heterogeneous symptoms presented by the PD cohort, a detailed analysis of exemplifying PD patients was conducted, showing the ability of Archimedes spiral drawing to capture and quantify PD characteristic features.Clinical Relevance- Among the first clinical manifestations of the pathology, handwriting impairment appears in PD patients. It is often underestimated and not investigated properly. This easy-to-use tool could be very useful as a large-scale screening, but also for treatment efficacy evaluation and for the identification of PD subgroups.