A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data - Beed, Alexander T.; Peduzzi, Peter; Guarino, Peter; Wininger, Michael (Frontiers in Robotics and AI, 2017)
Point-to-point exercising of the upper-limb, as elicited through the presentation of visual targets on a computer screen, is a ubiquitous paradigm in the robot-assisted rehabilitation of motor-impaired individuals. Kinematic data collected from the robot’s sensors can be used to assess motor function; these data allow objective quantification of motor performance, an approach that shows promise both for guiding therapy and documenting patient progress. It is imperative that these datasets be fully understood, and that tools be continually developed to support analysis and proper interpretation of robot-generated data. It is our experience that data collected from kinematic robots and partitioned according to target achievement may be prone to errors in analysis and interpretation because the movements of highly spastic individuals rarely stop within the target. Here, we propose that it is preferable to partition serial movement data based on local minima in velocity rather than target achievement; this design reflects the convention that movement epochs that start and end at low- or zero-velocity, an assumption that is prevalent even in severely impaired individuals. Using a commercially available robot (MIT-Manus, Interactive Motion Technologies), we recorded movements from 16 moderate- to severely impaired chronic stroke patients. Data partitioned according to target presentation typically interrupted movements mid-motion: velocity at file start was 32.6 ± 26.4% of overall velocity range. By re-apportioning we obtained velocity at file start of 7.4 ± 9.5% of total range. Across 3,200 movements, 12.4 ± 10.4% of data points were re-allocated on average. Thus, our routine is capable of re-partitioning to more accurately reflect observed behavior. Our study is thus the first to identify and propose a solution to the problem of high relevance to the community of robot-aided rehabilitation specialists, i.e. sub-optimal partitioning according to target achievement. Through the algorithm described in this paper, we were able to re-partition the data so that movement epochs were properly demarcated at velocity minima, thus adhering to the fundamental assumptions of human motor behavior, and facilitating analysis of patient performance on a per-movement basis.