Musculoskeletal injuries can severely inhibit performance of activities of daily living. In order to regain function, rehabilitation is often required. Assistive rehabilitation devices can be used to increase arm mobility by guiding therapeutic exercises or assisting with motion. Electromyography (EMG) may be able to provide an intuitive interface between the patient and the device if appropriate classification models allow smart systems to relate these signals to the desired device motion. Unfortunately, the accuracy of pattern recognition models classifying motion in constrained laboratory environments significantly drops when used for detecting dynamic unconstrained movements. The objectives of this study were to quantity how various motion factors affect arm muscle activations during dynamic motion, and to use these motion factors and EMG signals for detecting interaction forces between the person and the environment during motion. The results quantity how EMG features change significantly with variations in arm positions, interaction forces, and motion velocities. The results also show that pattern recognition models were able to detect intended characteristics of motion based solely on EMG signals. Prediction of force was improved from 73.77% correct to 79.17% accuracy during elbow flexion-extension by properly selecting the features, and providing measurable arm position and velocity information as additional inputs to a linear discriminant analysis model.