Background and Purpose:The way we move changes throughout our lifetime and often we move less as we age. Distinguishing the motor deficits caused by a stroke from changes in motion due to normal aging is important for the accurate assessment of post-stroke recovery and to determine the effectiveness of treatment. The whole repertoire of complex human motion is enabled by forces applied by our muscles and controlled by the nervous system. However, the current medical standard for assessing motor deficits is based on quantifying movement, without a comprehensive analysis of the active forces that cause this movement. The objective here is to estimate active muscle forces from the quantitative recording of motion. Methods:The motion of twenty-two people was captured when reaching to virtual targets in a center-out task. Eight of the participants had chronic hemiparesis after a stroke, and another six participants were of similar age to the stroke participants. All participants served as their own controls. We used inverse dynamic analysis to derive muscle moments, which were the result of the neural control signals to muscles and caused the recorded multijointed movements. These muscle moments were separated into forces that were related only to movement production from those only related to posture maintenance against gravity. We then compared these muscle forces between limbs to assess how stroke in one hemisphere disrupts the control signals in individuals with hemiparesis compared to the young and age-matched individuals. Results:We show that both aging and stroke causes the control signals from dominant and nondominant hemispheres to be less symmetrical in a pattern that indicates worsening neural control of intersegmental dynamics. We also show that the force-based assessment provides consistently higher quality measures of individual motor deficits due to stroke compared to traditional motion-based assessment. Using the force-based assessment, but not motion-based assessment, it was possible to distinguish the motor deficits due to stroke from age-related movement variability. Conclusions:The results of our study show that it is feasible to distinguish between age-related and stroke-related deficits in the neural control of reaching using inverse dynamic analysis of motion. This is useful for objective home-based monitoring using wearable and mobile devices of patients recovering from a stroke and elderly people at risk of disability. Our results further indicate that the disruption of the neural control of intersegmental dynamics contributes not only to motor deficits after stroke but also to the inefficiency of movement in the elderly.