The Fugl Meyer Assessment (FMA) is a widely-used assessment for tracking motor function recovery post-stroke. Due to the limited access to rehabilitation, there exists a need for remote and automated assessment solutions. Wearable sensors and data-driven methods have shown promise for enabling automatic upper extremity FMA (FMA-UE) estimation, but minimizing user input motion and aligning with current clinical activities will aid the adoption of sensor-based assessments. In this work, we present an FMA-UE estimator which can make score predictions for a key subset of the assessment (70$\% $ of all items) using data from inertial measurement units (IMUs) placed on the arms and the trunk from three volitional reaching motions representative of functional daily activities. We collected a dataset of eleven stroke participants performing a subset of FMA-UE, and three reaching motions. The FMA-UE of each participant was assessed by an occupational therapist providing the labeled score for the training data. The estimator was trained on windowed data during FMA-UE motions and was able to make score estimates from reaching motions. Through leave-one-subject-out cross validation, the estimator achieved a normalized RMSE of 7$\% $, which is comparable to or below the established minimal clinically important difference and minimal detectable change of FMA-UE of post-stroke individuals. Comparison experiments of various model designs also revealed the importance of trunk-based features inspired by compensation strategies common post stroke and features extracted from the hand sensor. The proposed estimator has the potential to broaden the possibility of automatic assessment via wearable sensors.

Assist-as-needed control aims at maximizing stroke survivors involvement during robotic-led therapies of neurorehabilitation. Besides the specific characteristics of the designed adaptive control strategy, a fundamental property of this control architecture is the choice of the error signal which will drive the adaptation process. This driving source is a necessary control parameter to be chosen, although often sidelined in the control design, and several solutions already exist in the state-of-the-art. For this reason, we wanted to compare three different strategies to guide the adaptation, respectively based on the local joint performances, on the end-effector only behaviour, or on the performance of one specific joint in the kinematic chain of the robot. The resulting analysis evaluates the possibilities offered by simply changing from one source to another with respect to the specific stage of the motor recovery of the patients, potentially extending the capabilities of current exoskeleton controllers for neurorehabilitation.