Capture, learning, and classification of upper extremity movement primitives in healthy controls and stroke patients

Guerra, Jorge; Uddin, Jasim; Nilsen, Dawn; Mclnerney, James; Fadoo, Ammarah; Omofuma, Isirame; Hughes, Shatif; Agrawal, Sunil K.; Allen, Peter K.; Schambra, Heidi M.

doi:10.1109/icorr.2017.8009305

Cited by 24 publications

(24 citation statements)

References 33 publications

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“…We developed the taxonomy using videotaped recordings of 16 subjects (healthy and stroke-impaired) performing various ADL-like tasks; an early iteration has been previously described (29). For the refined taxonomy, we examined validity and reliability using new motion data generated by healthy and stroke-impaired subjects (see Table 2 for demographic and clinical details) performing various activities (see Table 3 for task parameters).…”

Section: Methodsmentioning

confidence: 99%

A Taxonomy of Functional Upper Extremity Motion

et al. 2019

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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.

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Section: Methodsmentioning

confidence: 99%

A Taxonomy of Functional Upper Extremity Motion

et al. 2019

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“…al, developed a movement classification system that could be used to enumerate repetitions and applied the system to stroke patient data, but focused on the classification of movement primitives (components of arm movements that cannot be broken down further) rather than the exercises themselves. 15 To our knowledge, this study is the first to explore the application of upper extremity exercise classification and repetition counting on exercise data in inpatients with recent stroke.…”

Section: Discussionmentioning

confidence: 99%

Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Balestra

Sharma

Riek

et al. 2020

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Stroke is a major cause of adult-onset disability worldwide, and approximately 40% of stroke survivors have residual impairment in upper-extremity function. Prior studies suggest that increased participation in rehabilitation exercises improves motor function post-stroke, 1 however further clarification of optimal exercise dose and timing has been limited by the technical challenge of quantifying exercise activities over multiple days. In this exploratory study, we assessed the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting, and investigated which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. MC10 BioStampRC® sensors were used to measure accelerometry and gyroscopy from arms of healthy controls (n=11) and patients with arm weakness due to recent stroke (n=13) while the subjects performed three pre-selected upper extremity exercises. Sensor data was then labeled by exercise type, and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine-learning algorithm and a peak-finding algorithm was used to count exercise repetitions in non-labeled data sets. We achieved a repetition counting accuracy of 95.6 ± 2.4 % overall, and 95.0 ± 2.3 % in patients with arm weakness due to stroke. Accuracy was decreased when using fewer sensors or using accelerometry alone. Our exploratory study suggests that body-worn sensor systems are technically feasible, well-tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise "dose" in post-stroke patients during clinical rehabilitation or clinical trials.

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“…They report lower rates of accuracy (precision of approximately 80% in control patients, 79% in patients with weakness due to stroke) than in our study. 20 While speci cs about the particular UE movements studied (exercises versus movement primitives) may have contributed to this observed difference in accuracy, it is important to note that we also used a different categorization and repetition counting strategy. In our algorithm, activity is rst classi ed and then number of repetitions is estimated by counting peaks in data from the sensor that was previously seen to have maximum uctuations during the course of the exercise.…”

Section: Discussionmentioning

confidence: 99%

Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Balestra

Sharma

Riek

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Prior studies suggest that participation in rehabilitation exercises improves motor function post-stroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. Objective: In this exploratory study, we assessed the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigated which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. Methods: MC10 BioStampRC® sensors were used to measure accelerometry and gyroscopy data from arms of healthy controls (n=13) and patients with upper extremity (UE) weakness due to recent stroke (n=13) while the subjects performed three pre-selected UE exercises. Sensor data was then labeled by exercise type, and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine-learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. Results: We achieved a repetition counting accuracy of 95.6 ± 2.4 % overall, and 95.0 ± 2.3 % in patients with UE weakness due to stroke. Accuracy was decreased when using fewer sensors or using accelerometry data alone. Conclusions: Our exploratory study suggests that body-worn sensor systems are technically feasible, well-tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in post-stroke patients during clinical rehabilitation or clinical trials.

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Capture, learning, and classification of upper extremity movement primitives in healthy controls and stroke patients

Cited by 24 publications

References 33 publications

A Taxonomy of Functional Upper Extremity Motion

A Taxonomy of Functional Upper Extremity Motion

Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

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