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Updated: Jan 18, 2026
Digital Handwriting Analysis of Characters in Chinese Patients with Mild Cognitive Impairment
Published on: March 11, 2021
Community-based assisted screening for mild cognitive impairment using gait and handwriting kinematic parameters
Yin-Xia Ren1, Bei Wu2, Jian-Lin Lou3
1School of Medicine, Huzhou Key Laboratory of Precise Prevention and Control of Major Chronic Diseases, Huzhou University, Huzhou 313000, Zhejiang Province, China.
Background:
Older adults with mild cognitive impairment (MCI) often show motor dysfunction, including slower gait and impaired handwriting. While gait and handwriting parameters are promising for MCI screening, their combined potential to distinguish MCI from cognitively normal adults is unclear.
Aim:
To assess gait and handwriting differences and their potential for screening MCI in older adults.
Methods:
Ninety-five participants, including 34 with MCI and 61 cognitively normal controls, were assessed for gait using the GAITRite® system and handwriting with a dot-matrix pen. Five machine learning models were developed to assess the discriminative power of gait and handwriting data for MCI screening.
Results:
Compared to the cognitively normal group, the MCI group had slower gait velocity (Z = -2.911, P = 0.004), shorter stride and step lengths (t = -3.005, P = 0.003; t = 2.863, P = 0.005), and longer cycle, standing, and double support times (t = -2.274, P = 0.025; t = -2.376, P = 0.018; t = -2.717, P = 0.007). They also had reduced cadence (t = 2.060, P = 0.042) and increased double support time variability (Z = -2.614, P = 0.009). In handwriting, the MCI group showed lower average pressure (all tasks: Z = -2.135, P = 0.033) and decreased accuracy (graphic task: Z = -2.447, P = 0.014; Chinese character task: Z = -3.078, P = 0.002). In the graphic task, they demonstrated longer time in air (Z = -2.865, P = 0.004), reduced X-axis maximum velocities (Z = -3.237, P = 0.001), and lower accelerations (X-axis: Z = -2.880, P = 0.004; Y-axis: Z = -1.987, P = 0.047) and maximum accelerations (X-axis: Z = -3.998, P < 0.001; Y-axis: Z = -2.050, P = 0.040). The multimodal analysis achieved the highest accuracy (74.4%) with the Gradient Boosting Classifier.
Conclusion:
Integrating gait and handwriting kinematics parameters provides a viable method for distinguishing MCI, potentially supporting large-scale screening, especially in resource-limited settings.
