A Taxonomy of Functional Upper Extremity Motion

Abstract

Background: Functional upper extremity (UE) motion enables humans to execute activities of daily living (ADLs). There currently exists no universal language to systematically characterize this type of motion or its fundamental building blocks, called functional primitives. Without a standardized classification approach, pooling mechanistic knowledge and unpacking rehabilitation content will remain challenging. Methods: We created a taxonomy to characterize functional UE motions occurring during ADLs, classifying them by motion presence, temporal cyclicity, upper body effector, and contact type. We identified five functional primitives by their phenotype and purpose: reach, reposition, transport, stabilize , and idle . The taxonomy was assessed for its validity and interrater reliability in right-paretic chronic stroke patients performing a selection of ADL tasks. We applied the taxonomy to identify the primitive content and motion characteristics of these tasks, and to evaluate the influence of impairment level on these outcomes. Results: The taxonomy could account for all motions in the sampled activities. Interrater reliability was high for primitive identification (Cohen's kappa = 0.95–0.99). Using the taxonomy, the ADL tasks were found to be composed primarily of transport and stabilize primitives mainly executed with discrete, proximal motions. Compared to mildly impaired patients, moderately impaired patients used more repeated reaches and axial-proximal UE motion to execute the tasks. Conclusions: The proposed taxonomy yields objective, quantitative data on human functional UE motion. This new method could facilitate the decomposition and quantification of UE rehabilitation, the characterization of functional abnormality after stroke, and the mechanistic examination of shared behavior in motor studies.

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer

“…We also generated confusion matrices to further characterize where ML algorithms are performing well (successful classification) vs. becoming confused (misclassification). In our analysis, we observed that NBC had limited success in differentiating between reaches and transports , functional primitives whose motion patterns are quite similar (12). The main difference between these primitives are grasp-related wrist motions (wrist extension and supination) occurring during reach (12).…”

“…In our analysis, we observed that NBC had limited success in differentiating between reaches and transports , functional primitives whose motion patterns are quite similar (12). The main difference between these primitives are grasp-related wrist motions (wrist extension and supination) occurring during reach (12). To accurately disambiguate these primitives with a lower-performing algorithm, we would emphasize [i.e., assign higher weights to (47)] data from the distal UE segment.…”

“…The task generates the following functional primitives: reach (to move into contact with a target object); transport (to convey a target object); reposition (to move proximate to a target object); and idle (to stand at the ready near target object). Functional primitives are discrete, object-oriented motions with a single goal (12). Functional primitives are non-divisible and are largely invariant across individuals (13), may be represented cortically (14–16), and provide a finer-grained capture of performance in stroke patients who may be unable to accomplish a full activity.…”

“…Investigators may also seek to use ML therapeutically, as with performance assessments. For example, investigators can identify and count classes of behavioral output in training contexts, such as the number of words uttered during speech therapy (54) or the number of functional primitives performed during occupational therapy (12).…”

At-Home Self-Administration of an Immersive Virtual Reality Therapeutic Game for Post-Stroke Upper Limb Rehabilitation

“…Activities implement a therapeutic framework, which combines DDA with clinically valid therapeutic approaches for the target indication. For upper extremity (UE) rehabilitation, activities in CogniviveVR are based on a bottom-up approach, which starts with "essential components" of movement [13][14][15] and gradually builds up a patient's ability to perform complex ADL-like tasks. For example, an activity may start by requiring a patient to reach to an object and move it, but no grasping is required.…”