Sit-to-stand (STS) motion is an essential daily activity. However, this motion becomes increasingly difficult for older adults as their muscle strength declines with age. To assist individuals in standing up while maximizing their muscle strength based on the assist-as-needed (AAN) strategy, assistive devices must detect early STS intent, specifically before the buttocks leave the chair, to ensure timely assistance. This study proposes a novel method for detecting STS intent by applying external mechanical stimuli to the toes and analyzing the resulting changes in heel and toe-reaction forces. Moreover, a structured detection framework was developed by utilizing predefined thresholds for the change rate and magnitude of the heel and toe-reaction forces to detect STS intent. Offline tests for threshold setting of STS-intent detection were established in the offline tests: change rate and magnitude of the reaction forces on the heel and toes. The thresholds for each criterion were determined using the Pareto optimization method. Using the determined thresholds, these criteria were then applied in online tests to evaluate the performance of the proposed intent detection method. The results demonstrated that mechanical stimuli improved the performance of STS-intent detection, providing accurate and stable detection. This method can be applied to STS-assistive devices to effectively implement AAN functionality for standing assistance.