In this study, an abnormal motion pattern detection method was proposed based on Energy Distance, a statistical metric used to quantify the discrepancy between motion feature distributions of pathological and healthy control groups. Motion features, including mean, standard deviation, entropy, and range of motion (ROM), were extracted from key lower-limb joints (hip and knee) using fixed-length temporal windows. Three representative actions walk, run, and jump were selected to reflect different levels of motor complexity and served as the basis for evaluating three simulated pathological groups: Elderly, Neurological, and Orthopedic. Energy Distance was employed to compute the degree of deviation between the groups, thereby reflecting abnormalities in motion behavior. The results showed that the Neurological group exhibited the greatest deviation, particularly in the Jumping action, which involves high coordination demands. Additionally, the left hip joint demonstrated the most distinct group separation, indicating a high potential for discriminating between different types of motor impairments. These findings demonstrate that Energy Distance is an effective, stable, and interpretable quantitative measure for abnormal motion pattern detection, highlighting its potential applications in functional assessment and rehabilitation monitoring.