KineticoMyoGraphy (KMG) is an emerging sensor technology offering innovative solutions for tracking amputees’ fine muscle movements, promising better hand gesture recognition with greater sustainability than existing methods. The primary challenge in KMG technology lies in the required number and placement of magnetic sensors to balance accuracy, sustainability, and cost-efficiency for practical hand gesture interpretation. To tackle this issue, we propose a Stochastic Sequential Strategy for Magnetic Sensory Selection (S3MSS). We apply this strategy to a configuration of 16 magnetic sensors surrounding surgically implanted magnets in a patient’s forearm. The method uses an Error-Correcting Output Codes (ECOC) framework with Multiclass Linear Discriminant Analysis (MCLDA) and Multiclass Support Vector Machines (MCSVM). Our approach emphasizes robust sensory selection and consistent performance through time-based seeding and K-fold cross-validation. Clinical results indicate consistency in sensory selection across two independent trials, underlining this factor as crucial for reliability. Statistical significance test confirms the superiority of the MCLDA over the MCSVM approach, achieving a 93% accuracy in the classification of Fingers, Wrist, and Thumb gestures using only five sensors near the magnets’ motion range. This underscores our strategy’s effectiveness in accurately detecting hand movements, highlighting its potential for

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