Assessing muscle activation and monitoring stroke recovery progress are vital aspects of evaluating motor impairment. Surface electromyography, a common tool for measuring muscle activity, has practical limitations for clinical implementation and fails to capture deep muscle activities, leading to challenges in accurately identifying muscle activation patterns in stroke patients. To address these limitations, we introduce an All Muscle Activity Test and Evaluation (AMATE) system, combining wearable sensor technology with a musculoskeletal model. AMATE computes activation levels of all lower limb muscles during various activities without requiring surface electrodes. It also generates an Activation Dissimilarity Index (ADI) to illustrate muscle activation patterns among individuals. We evaluated AMATE’s effectiveness through a pilot study involving 22 walking trials with healthy subjects and stroke patients. The results highlight AMATE’s capability in aiding clinicians to analyze and understand diverse lower limb muscle activation patterns in stroke patients, particularly identifying muscles with abnormal activation for targeted rehabilitative training.