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.
Abstract-We present an approach to wearable sensor-based assessment of motor function in individuals post stroke. We make use of one on-body inertial measurement unit (IMU) to automate the functional ability (FA) scoring of the Wolf Motor Function Test (WMFT). WMFT is an assessment instrument used to determine the functional motor capabilities of individuals post stroke. It is comprised of 17 tasks, 15 of which are rated according to performance time and quality of motion. We present signal processing and machine learning tools to estimate the WMFT FA scores of the 15 tasks using IMU data. We treat this as a classification problem in multidimensional feature space and use a supervised learning approach.
We present an adaptive biofeedback game for teaching self-regulation of stress. Our approach consists of monitoring the user's physiology during gameplay and adapting the game using a positive feedback loop that rewards relaxing behaviors and penalizes states of high arousal. We evaluate the approach using a casual game under three biofeedback modalities: electrodermal activity, heart rate variability, and breathing rate. The three biosignals can be measured noninvasively with wearable sensors, and represent different degrees of voluntary control and selectivity toward arousal. We conducted an experiment trial with 25 participants to compare the three modalities against a standard treatment (deep breathing) and a control condition (the game without biofeedback). Our results indicate that breathing-based game biofeedback is more effective in inducing relaxation during treatment than the other four groups. Participants in this group also showed greater retention of the relaxation skills (without biofeedback) during a subsequent stressor.
Abstract-The advent of new health sensing technologies has presented us with the opportunity to gain richer data from patients undergoing clinical interventions. Such technologies are particularly suited for applications requiring temporal accuracy. The Wolf Motor Function Test (WMFT) is one such application. This assessment is an instrument used to determine functional ability of the paretic and non-paretic limbs in individuals poststroke . It consists of 17 tasks, 15 of which are scored according to both time and a functional ability scale. We propose a technique that uses wearable sensors and performance sensors to estimate the timing of seven of these tasks. We have developed a sensing framework and an algorithm to automatically detect total movement time. We have validated the system's accuracy on the seven selected WMFT tasks. We also suggest how this framework can be adapted to the remaining tasks.
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