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Automated Movement Assessment in Stroke Rehabilitation.

ABSTRACT: We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e., type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e., lower extremity training for neurological accidents).

SUBMITTER: Ahmed T

PROVIDER: S-EPMC8417323 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients' functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients' arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice.ObjectiveWe aim to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders.MethodsWe conducted semi-structured interviews with four stroke survivors and 15 occupational therapists (OTs) based on real-world arm use data that we collected for contextualization. To situate our participants, we leveraged a pair of finger-worn accelerometers to collect stroke survivors' arm use data in real-world settings, which we used to create study probes for stroke survivors and OTs, respectively. The interview data was analyzed using the thematic approach.ResultsOur study unveiled a detailed account of (1) the receptiveness of stroke survivors and OTs for using wearable sensors in clinical practice, (2) OTs' envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice.ConclusionsThese findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.

Project description:BackgroundMotor (re)learning via technology-dependent therapy has the potential to complement traditional therapies available to older adults living with stroke after hospital discharge and increase therapy dose. To date, little is known about the feasibility of technology-dependent therapy in a home setting for this population.ObjectiveTo develop a technology-dependent therapy that provides mental and physical training for older adults with stroke and assess feasibility. Specifically we ask, "Can it work"?DesignSingle group repeated measures.Methods13 participants, aged 18 years and over, were recruited over a six-month period. All participants had mild upper limb impairment following a stoke and were no longer receiving intensive rehabilitation. All participants received 18 days of technology-dependent therapy in their own home. Information was gathered on recruitment and retention, usability, and suitability of outcome measures.Results11 participants completed the study. The recruitment rate (number recruited/number canvassed; 10.7%) suggests 1907 participants would need to be canvassed to recruit the necessary sample size (n = 204) for a definitive trial designed to provide 90% power at 5% level of significance to detect a clinically meaningful difference of 5.7 points on the Action Research Arm Test. The usability of the application was rated as exceptional on the System Usability Scale. Effectiveness cannot be determined from this study; however, there was a trend for improvement in measures of upper limb function and emotional well-being. Limitations. The study was limited by a relatively small sample size and lack of control group.ConclusionsThis study demonstrated proof of concept of a technology-dependent therapy for upper limb rehabilitation following stroke. The data suggest a definitive trial is feasible, additional strategies to improve recruitment should be considered. Outcome measures aligned with the residual motor function of participants are required.

Project description:BackgroundAs many as 80% of stroke survivors experience upper limb (UL) disability. The strong relationships between disability, lost productivity, and ongoing health care costs mean reducing disability after stroke is critical at both individual and society levels. Unfortunately, the amount of UL-focused rehabilitation received by people with stroke is extremely low. Activity monitoring and promotion using wearable devices offer a potential technology-based solution to address this gap. Commonly, wearable devices are used to deliver a haptic nudge to the wearer with the aim of promoting a particular behavior. However, little is known about the effectiveness of haptic nudging in promoting behaviors in patient populations.ObjectiveThis study aimed to estimate the effect of haptic nudging delivered via a wrist-worn wearable device on UL movement in people with UL disability following stroke undertaking inpatient rehabilitation.MethodsA multiple-period randomized crossover design was used to measure the association of UL movement with the occurrence of haptic nudge reminders to move the affected UL in 20 people with stroke undertaking inpatient rehabilitation. UL movement was observed and classified using movement taxonomy across 72 one-minute observation periods from 7:00 AM to 7:00 PM on a single weekday. On 36 occasions, a haptic nudge to move the affected UL was provided just before the observation period. On the other 36 occasions, no haptic nudge was given. The timing of the haptic nudge was randomized across the observation period for each participant. Statistical analysis was performed using mixed logistic regression. The effect of a haptic nudge was evaluated from the intention-to-treat dataset as the ratio of the odds of affected UL movement during the observation period following a "Planned Nudge" to the odds of affected limb movement during the observation period following "No Nudge."ResultsThe primary intention-to-treat analysis showed the odds ratio (OR) of affected UL movement following a haptic nudge was 1.44 (95% CI 1.28-1.63, P<.001). The secondary analysis revealed an increased odds of affected UL movement following a Planned Nudge was predominantly due to increased odds of spontaneous affected UL movement (OR 2.03, 95% CI 1.65-2.51, P<.001) rather than affected UL movement in conjunction with unaffected UL movement (OR 1.13, 95% CI 0.99-1.29, P=.07).ConclusionsHaptic nudging delivered via a wrist-worn wearable device increases affected UL movement in people with UL disability following stroke undertaking inpatient rehabilitation. The promoted movement appears to be specific to the instructions given.Trial registrationAustralia New Zealand Clinical Trials Registry 12616000654459; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=370687&isReview=true.

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Automated Movement Assessment in Stroke Rehabilitation.

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