A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data
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 functio...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-11-01
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| Series: | Frontiers in Robotics and AI |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/frobt.2017.00057/full |
| _version_ | 1830508329664774144 |
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| author | Alexander T. Beed Alexander T. Beed Peter Peduzzi Peter Peduzzi Peter Guarino Peter Guarino Peter Guarino Michael Wininger Michael Wininger Michael Wininger |
| author_facet | Alexander T. Beed Alexander T. Beed Peter Peduzzi Peter Peduzzi Peter Guarino Peter Guarino Peter Guarino Michael Wininger Michael Wininger Michael Wininger |
| author_sort | Alexander T. Beed |
| collection | DOAJ |
| description | 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 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 in mid-motion: velocity at file start was 32.6 ± 26.4% of the 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. |
| first_indexed | 2024-12-22T01:17:44Z |
| format | Article |
| id | doaj.art-460ee3dfa1394189b6543f45d56a94dc |
| institution | Directory Open Access Journal |
| issn | 2296-9144 |
| language | English |
| last_indexed | 2024-12-22T01:17:44Z |
| publishDate | 2017-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Robotics and AI |
| spelling | doaj.art-460ee3dfa1394189b6543f45d56a94dc2022-12-21T18:43:49ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442017-11-01410.3389/frobt.2017.00057298925A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic DataAlexander T. Beed0Alexander T. Beed1Peter Peduzzi2Peter Peduzzi3Peter Guarino4Peter Guarino5Peter Guarino6Michael Wininger7Michael Wininger8Michael Wininger9Department of Biostatistics, Yale School of Public Health, New Haven, CT, United StatesCooperative Studies Program, Department of Veterans Affairs, West Haven, CT, United StatesDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, United StatesCooperative Studies Program, Department of Veterans Affairs, West Haven, CT, United StatesDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, United StatesCooperative Studies Program, Department of Veterans Affairs, West Haven, CT, United StatesStatistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Research Center, Seattle, WA, United StatesDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, United StatesCooperative Studies Program, Department of Veterans Affairs, West Haven, CT, United StatesDepartment of Rehabilitation Sciences, University of Hartford, West Hartford, CT, United StatesPoint-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 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 in mid-motion: velocity at file start was 32.6 ± 26.4% of the 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.http://journal.frontiersin.org/article/10.3389/frobt.2017.00057/fullrehabilitationvelocitypositiontargetovershootstroke |
| spellingShingle | Alexander T. Beed Alexander T. Beed Peter Peduzzi Peter Peduzzi Peter Guarino Peter Guarino Peter Guarino Michael Wininger Michael Wininger Michael Wininger A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data Frontiers in Robotics and AI rehabilitation velocity position target overshoot stroke |
| title | A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data |
| title_full | A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data |
| title_fullStr | A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data |
| title_full_unstemmed | A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data |
| title_short | A Partitioning Algorithm for Extracting Movement Epochs from Robot-Derived Kinematic Data |
| title_sort | partitioning algorithm for extracting movement epochs from robot derived kinematic data |
| topic | rehabilitation velocity position target overshoot stroke |
| url | http://journal.frontiersin.org/article/10.3389/frobt.2017.00057/full |
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