Sensing for wearable robots is an ongoing challenge, especially given the recent trend of soft and compliant robots. Recently, a wearable origami exoshell has been designed to sense the user’s torso motion and provide mobility assistance. The materials of the exoshell contribute to a lightweight design with compliant joints, which are ideal characteristics for a wearable device. Common sensors are not ideal for the exoshell as they compromise these design characteristics. Rotary encoders are often rigid metal devices that add considerable weight and compromise the flexibility of the joints. Inertial measurement unit sensors are affected by environments with variable electromagnetic fields and therefore not ideal for wearable applications. Hall effect sensors and gyroscopes are utilized as alternative compatible sensors, which introduce their own set of challenges: noisy measurements and drift due to sensor bias. To mitigate this, we designed the Kinematically Constrained Kalman filter for sensor fusion of gyroscopes and Hall effect sensors, with the goal of estimating the human’s torso and robot joint angles. We augmented the states to consider bias related to the torso angle in order to compensate for drift. The forward kinematics of the robot is incorporated into the Kalman filter as state constraints to address the unobservability of the torso angle and its related bias. The proposed algorithm improved the estimation performance of the torso angle and its bias, compared to the individual sensors and the standard Kalman filter, as demonstrated through bench tests and experiments with a human user.