Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning

Bochniewicz, Elaine M.; Emmer, Geoff; McLeod, Adam; Barth, Jessica; Dromerick, Alexander W.; Lum, Peter S.

doi:10.1016/j.jstrokecerebrovasdis.2017.07.004

Cited by 64 publications

(90 citation statements)

References 22 publications

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“…Biswas et al and Bochniewicz et al built classi cation algorithms for UE exercises and applied them to stroke patient data, but did not attempt to create a repetition quantifying system to measure exercise dose. 14,15 Other examples, such as Zhang et al and Crema et al, created UE exercise classi cation and repetition counting systems but did not apply them to data from patients. 18,19 More recently, Guerra and colleagues developed a movement classi cation system that could be used to enumerate repetitions and applied the system to stroke patient data with a focus on the classi cation of movement primitives (components of UE movements that cannot be broken down further).…”

Section: Discussionmentioning

confidence: 99%

“…Body-worn sensors have been used to identify speci c movement patterns of the wearer in both healthy controls and patients with illness, including patients with neurological disease. 14,15 We therefore conducted a pilot-study in the inpatient setting to assess the feasibility of automatically measuring exercise repetition "dose" using body-worn sensors. We recruited healthy controls and patients with hemiparesis due to stroke admitted to our hospital's stroke inpatient and acute rehabilitation units and asked study participants to wear super cial sensors (BioStampRC, MC 10 Inc., Lexington, MA, USA) while performing several sets of three pre-de ned upper extremity (UE) exercises.…”

Section: Introductionmentioning

confidence: 99%

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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

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

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

show abstract

“…Biswas et al and Bochniewicz et al built classification algorithms for arm exercises and applied them to stroke patient data, but did not attempt to create a repetition quantifying system to measure exercise dose. 11,12 Other examples, such as Zhang et al and Crema et al created arm exercise classification and repetition counting systems, but did not apply them to data from patients. 13,14 The most recent study on this topic, Guerra et.…”

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

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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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“…Future studies may consider using objective methods to assess upper extremity activity as an outcome measure. 64 It is unlikely the gains during the followup period were due to encouragement or guidance from therapists, since the interaction with therapists was limited to the clinical evaluations and subjects were not given a home therapy plan during the followup period.…”

Section: Discussionmentioning

confidence: 99%

Clinical Trial of HEXORR II for Robotic Hand Movement Therapy After Stroke

Ji¹,

Black²,

Nichols³

et al. 2020

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BackgroundImpaired use of the hand in functional tasks remains difficult to overcome in many individuals after a stroke. This often leads to compensation strategies using the less-affected limb, which allows for independence in some aspects of daily activities. However, recovery of hand function remains an important therapeutic goal of many individuals, and is often resistant to conventional therapies. In prior work, we developed HEXORR I, a robotic device that allows practice of finger and thumb movements with robotic assistance. In this study, we describe modifications to the device, now called HEXORR II, and a clinical trial in individuals with chronic stroke. MethodsFifteen individuals with a diagnosis of chronic stroke were randomized to 12 or 24 sessions of robotic therapy. The sessions involved playing several video games using thumb and finger movement. The robot applied assistance to extension movement that was adapted based on task performance. Clinical and motion capture evaluations were performed before and after training and again at a 6 month followup. ResultsFourteen individuals completed the protocol. Fugl-Meyer scores improved significantly over the 3 time points, indicating reductions in upper extremity impairment. Flexor hypertonia (Ashworth) also decreased significantly due to the intervention. Motion capture found increased finger range of motion and extension ability when the arm was supported by gravity. However, extension ability did not improve significantly during a reach and grasp task, and there was no change in a functional measure (Action Research Arm Test). At the followup, the high dose group had significant gains in hand displacement during a forward reach task. There were no other significant differences between groups. ConclusionsFuture work with HEXORR II should focus on integrating it with functional task practice and incorporating grip and squeezing tasks. Trial registration: CLINICALTRIALS.GOV, NCT04536987. Registered 3 September 2020 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT04536987

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Measuring Functional Arm Movement after Stroke Using a Single Wrist-Worn Sensor and Machine Learning

Cited by 64 publications

References 22 publications

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

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

Clinical Trial of HEXORR II for Robotic Hand Movement Therapy After Stroke

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