In this study, we present a cooking activity recognition method using wearable sensors and a stacking ensemble model. Users wear a wearable device with an accelerometer and an electromyography sensor on each wrist. During cooking, the system collects acceleration, angular velocity, and EMG data. These are classified into seven activity classes (e.g., washing, peeling, cutting, frying, stirring) to train the model, which achieved an average recall of 63%. As cooking involves planning and sequential actions associated with executive functions, this system may support cognitive training and early detection of cognitive decline in older adults.