Date Presented 03/26/20
Tasks requiring intra- and interlimb coordination (ILC) can be difficult for persons with hemiparesis. Clinical tools are often not sensitive to small changes in ILC that can occur with recovery or rehabilitation. Our goal is to develop a quantitative measure of upper-extremity ILC and validate it against clinical tools. Future implementation of this sensor-based system would allow OTs to better quantify ILC in the community and home settings.
Primary Author and Speaker: Susan Duff
Contributing Authors: Aaron Miller, Lori Quinn, Gregory Youdan, Lauri Bishop, Heather Ruthrauff, Eric Wade
PURPOSE: Persons with post-stroke hemiparesis often have difficulty with tasks requiring upper extremity (UE) interlimb coordination (ILC), yet methods to quantify ILC are limited. Clinical tools used to assess ILC such as the Adult Assisting Hand Assessment (AdAHA) are not usually sensitive to small changes in motor behavior that can occur with natural recovery or over the course of rehabilitation. Inertial measurement unit (IMU) sensors are body-worn sensors that enable data collection in the community or home setting. Our aim was to develop a quantitative method to identify distinct features of UE ILC. We also compared the relationship of the wrist sensor data to scores on common clinical assessments.
DESIGN: Quasi-experimental; two-group cohort study.
METHODS: Twenty adults, post-stroke and 20 age-matched healthy controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, gyroscope, orientation) were examined for three metrics (peak to peak amplitude, time/frequency similarity). The nine resultant values were combined into one motion parameter, per sensor, using a novel algorithm. This motion parameter was compared in a group by task, analysis of variance using a similarity score (0-1) between key sensor pairs: sternum to wrist, wrist to wrist, and wrist to upper arm. Adult Assisting Hand Assessment and UE Fugl-Meyer (UE-FMA) clinical scores were compared to an arm use ratio (paretic/non-paretic arm) based on the integral for the three wrist sensor modalities
RESULTS: A significant group x task interaction in the similarity score was found for all key sensor pairs (all p<0.01). Post-hoc analyses revealed significant differences for key sensor pairs in 8 of 9 task comparisons for controls and 3 of 9 task comparisons for persons post stroke. The arm use ratio for all 3 metrics was predictive of mean clinical scores for the AdAHA and UE-FMA (R2=0.74 to 0.81, all p<0.001).
CONCLUSIONS: Our algorithm and analyses of sensor data distinguished between group and task type and was predictive of clinical scores. It also was able to accurately quantify compensatory movement strategies observed during performance. Future work will further assess the reliability and validity of this novel metric of interlimb coordination in adults and children with hemiparesis along with other conditions which influence UE task performance.
References
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Van Gils, A., Meyer, S., Van Dijk, M., et. al. (2018). The Adult Assisting Hand Assessment Stroke: Psychometric Properties of an Observation-Based Bimanual Upper Limb Performance Measurement. Archives in Physical Medicine and Rehabilitation, 99(12), 2513-2522
Levin, M. R., Kleim, J. A., Wolf, S. L. (2009). What do motor “recovery” and “compensation” mean in patients following stroke? Neurorehabilitation and Neural Repair, 23, 313–319.
Doman, C. A., Waddell, K. J., Bailey, R. R., Moore, J. L., Lang, C. E. (2016). Changes in upper-extremity functional capacity and daily performance during outpatient occupational therapy for people with stroke. American Journal of Occupational Therapy, 70(3), 7003290040p1-7003290040p11.
