Abstract:
Surface electromyography (sEMG) represents a well-established technique for capturing
muscle activation signals across numerous applications. However, sEMG-based
handwritten character recognition for underrepresented languages such as Sinhala
remains largely unexplored in the literature. This research addresses the existing knowledge
gap by proposing a comprehensive framework for recognizing six Sinhala handwritten
characters selected based on stroke complexity: ර (r) and ට (ṭa) from ascending letters
group, අ (a) and උ (u) from descending letters group, and ක (ka) and ග (ga) from
middle letters group. To accomplish this objective, a novel dataset was collected using a
consumer-grade OpenBCI sEMG acquisition system. Four electrodes were strategically
positioned across multiple muscle groups, with particular emphasis on the extensor
digitorum muscle due to its role in finger control during handwriting. Eight participants
wrote six selected Sinhala characters 50 times each at natural speed while sEMG signals
were recorded, generating 300 samples per character. Signal preprocessing was performed
using high-pass, low-pass, and notch filtering techniques to remove noise components
from the collected dataset. Mean absolute value, root mean square, variance, zero crossing,
slope sign change, waveform length, and Willison amplitude features were extracted from
the sEMG signals. Several classification algorithms including Random Forest, K-Nearest
Neighbors, Naive Bayes, and Support Vector Machine were employed to train predictive
models using the derived features, and their effectiveness was assessed using conventional
performance metrics. The Random Forest algorithm demonstrated optimal results, attaining
a recognition accuracy of 98.96% on the experimental dataset, confirming the practical
applicability and robustness of the sEMG- based Sinhala handwriting recognition
framework. The study indicates the potential of using machine learning technology with
selected sEMG signal characteristics for the classification of Sinhala characters from
comparable neuromuscular activation patterns. This work establishes a foundation for
developing sEMG-based assistive writing technologies for Sinhala script, benefiting
individuals with physical disabilities.
