The growing focus on health management and smart technology advancements have propelled the use of wearable sensors in healthcare and human body motion analysis, particularly in preventing work-related upper limb musculoskeletal disorders (MSDs) through guided exercises. However, most available wearable medical sensors are rigid, bulky, and incapable of in situ recognition of the comprehensive motion of human body. Here, a conformal self-powered inertial displacement sensor (CSIDS) with geometric optimization for in situ noninvasive inertial data acquisition is proposed. Leveraging template-assisted processing and COMSOL simulation, the geometric configurations of tribo-layer materials, specifically focusing on the curvature of semicylindrical protrusions is systematically altered. This enhancement significantly improves the detection accuracy of joint range of motion. The features of shoulder joint bending angles and linear accelerations of the humerus are accurately captured using a deep learning model based on multilayer perceptron (MLP) networks, resulting in an exceptional recognition accuracy of 99.38% and 99.58%. Compared to traditional TENG wearable sensors that can only identify single metrics, CSIDS achieves a more comprehensive health assessment through inertial data detection. This system provides early warning for shoulder joint diseases, prevents MSDs, and extends to smart wearables for comprehensive joint health and ergonomic monitoring.