Smartphone-Based Estimation of Ground Reaction Forces and Lower-Limb Kinematics During Functional Tasks Using OpenCap: A Feasibility Study

Featured Application: This work explores the technical feasibility of smartphone-based markerless motion capture for estimating lower-limb kinematics and ground reaction forces during functional movements. In a single healthy participant, the approach reproduced selected movement patterns and resultant loading profiles, while variable-dependent discrepancies and task-specific processing requirements were also identified. These findings support further investigation of smartphone-based biomechanics for research and field-oriented applications, while larger validation studies are required before individual-level clinical use can be considered. (1) Background: Markerless motion capture may enable scalable biomechanical assessment outside laboratory environments, yet its applicability to tasks involving elevated foot contacts and rapid loading transitions remains insufficiently characterized. This feasibility study evaluated OpenCap, an open-source smartphone-based markerless system, for estimating lower-limb kinematics and ground reaction forces during stair ascent, stair descent, squat, sit-to-stand, and forward lunge. (2) Methods: One healthy adult completed three repetitions of each task while data were acquired simultaneously with three smartphones, an eight-camera Vicon system, and two force plates. OpenCap/OpenSim outputs were adapted for stair and lunge kinetics and compared with marker-based kinematics and force-plate measurements using root mean square error, Pearson correlation, and cosine similarity. (3) Results: Agreement was strongest for sagittal-plane lower-limb kinematics, particularly knee flexion-extension (RMSE: 1.4–10.5 deg; Pearson: 0.94–0.999). Larger discrepancies occurred for hip internal-external rotation, pelvis motion during sit-to-stand, and ankle flexion-extension. Resultant GRF waveforms showed high similarity to force-plate data (cosine similarity ≥ 0.988; RMSE: 0.069–0.167 BW). Stair descent produced the largest deviations and variability, particularly for single-limb forces. (4) Conclusions: In this feasibility study, smartphone-based markerless analysis agreed best with laboratory measurements for sagittal lower-limb kinematics and resultant GRF waveforms but showed larger discrepancies in multiplanar kinematics and limb-specific loading. The adapted pipeline is technically feasible for exploratory movement analysis, although larger, repeated-session studies are needed before clinical use.