ZMP Estimation From Wearable Sensor Using Deep Learning for Gait Analysis

Zero Moment Point (ZMP) is a key biomechanical indicator of dynamic gait stability, conventionally measured using force plates or pressure insoles that are costly and impractical for continuous wearable use. This work presents a low-cost wearable framework for ZMP estimation using only thigh–shank inertial measurement units (IMUs) and knee joint kinematics. A custom flex sensor-based insole was used solely to compute reference ZMP during training and evaluation. Data were collected from healthy subjects walking on flat and inclined treadmills. A BiLSTM–ANN model trained on synchronized IMU and knee angle data achieved subject-wise 5-fold cross-validation RMSEs of 0.56 cm (mediolateral) and 0.09 cm (anterior–posterior), with corresponding R2values of 0.82 and 0.90. Leave-one-subject-out evaluation demonstrated subject-independent generalization with a modest increase in error due to inter-subject variability. Real time deployment on an NVIDIA Jetson Nano achieved an average inference time of 9.2ms with a quantized model size of 1.8MB, enabling on-device ZMP estimation in controlled gait settings.