Automating the Clinical Assessment of Independent Wheelchair Sitting Pivot Transfer Techniques.

BACKGROUND: Using proper transfer technique can help to reduce forces and prevent secondary injuries. However, current assessment tools rely on the ability to subjectively identify harmful movement patterns. OBJECTIVES: The purpose of the study was to determine the accuracy of using a low-cost markerless motion capture camera and machine learning methods to evaluate the quality of independent wheelchair sitting pivot transfers. We hypothesized that the algorithms would be able to discern proper (low risk) and improper (high risk) wheelchair transfer techniques in accordance with component items on the Transfer Assessment Instrument (TAI). METHODS: Transfer motions of 91 full-time wheelchair users were recorded and used to develop machine learning classifiers that could be used to discern proper from improper technique. The data were labeled using the TAI item scores. Eleven out of 18 TAI items were evaluated by the classifiers. Motion variables from the Kinect were inputted as the features. Random forests and k-nearest neighbors algorithms were chosen as the classifiers. Eighty percent of the data were used for model training and hyperparameter turning. The validation process was performed using 20% of the data as the test set. RESULTS: The area under the receiver operating characteristic curve of the test set for each item was over 0.79. After adjusting the decision threshold, the precisions of the models were over 0.87, and the model accuracies were over 71%. CONCLUSION: The results show promise for the objective assessment of the transfer technique using a low cost camera and machine learning classifiers.

Functional assessment and performance evaluation for assistive robotic manipulators: Literature review.

CONTEXT: The user interface development of assistive robotic manipulators can be traced back to the 1960s. Studies include kinematic designs, cost-efficiency, user experience involvements, and performance evaluation. This paper is to review studies conducted with clinical trials using activities of daily living (ADLs) tasks to evaluate performance categorized using the International Classification of Functioning, Disability, and Health (ICF) frameworks, in order to give the scope of current research and provide suggestions for future studies. METHODS: We conducted a literature search of assistive robotic manipulators from 1970 to 2012 in PubMed, Google Scholar, and University of Pittsburgh Library System - PITTCat. RESULTS: Twenty relevant studies were identified. CONCLUSION: Studies were separated into two broad categories: user task preferences and user-interface performance measurements of commercialized and developing assistive robotic manipulators. The outcome measures and ICF codes associated with the performance evaluations are reported. Suggestions for the future studies include (1) standardized ADL tasks for the quantitative and qualitative evaluation of task efficiency and performance to build comparable measures between research groups, (2) studies relevant to the tasks from user priority lists and ICF codes, and (3) appropriate clinical functional assessment tests with consideration of constraints in assistive robotic manipulator user interfaces. In addition, these outcome measures will help physicians and therapists build standardized tools while prescribing and assessing assistive robotic manipulators.