Automated Movement Assessment in Stroke Rehabilitation

Ahmed, Tamim; Thopalli, Kowshik; Rikakis, Thanassis; Turaga, Pavan; Kelliher, Aisling; Huang, Jia-Bin; Wolf, Steven L.

doi:10.3389/fneur.2021.720650

Cited by 4 publications

(12 citation statements)

References 53 publications

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“…As evident from the results, Experiment c has the highest accuracy for per frame label prediction. This supports our argument that training the models with both impaired and unimpaired data makes the model more generalised and also increases the performance of the model compared to [22]. In Experiment 2a, the models were trained using noisy patient data captured in a variant activity space.…”

Section: Resultssupporting

confidence: 72%

“…Of course this also means that the engine needs to perform automated segmentation of the captured videos. In a previous publication [22], we presented our work in developing a machine learning ensemble that could perform automatic segmentation and assess task and segment completion when analyzing the impaired patient movement used for the clinician rating experiments. In this section, we discuss how we have now extended this engine to perform these tasks across impaired and unimpaired movement so that we can begin the process of automated differentiation of impaired from unimpaired movement.…”

Section: Methodsmentioning

confidence: 99%

“…In experiments detailed in [22], using this ensemble model with patient data we achieved a mean performance of 85.1% with a standard deviation of 2.14% across 5 experiments using pre-defined random train-test splits of data. Using a combination of the frame level ensemble model and the segment block and task assessment algorithms using RBBDT that emerged from our work with clinicians, we are able to correctly assess segment completion about 99% of the times and task execution over 92% of the time.…”

Section: Methodsmentioning

confidence: 99%

“…We performed three experiments using mixes of impaired patient data and unimpaired subject data and compared the results to the experiments from [22]. The data mixes of the three new experiments are shown in 1.…”

Section: Methodsmentioning

confidence: 99%

“…The detailed development of the rating process and interactive rating interfaces are described in previous publications [16, 15, 22]. Here, we summarize only the development components that proved critical to the expression of the functionality-movement quality HBM.…”

Section: Experiments 1: Rating Interfacementioning

confidence: 99%

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A Hierarchical Bayesian Model for Cyber-Human Assessment of Rehabilitation Movement

Ahmed

Thopali

Rikakis

et al. 2022

Preprint

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Background: The evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adaptations of therapy. Facilitating this quantification through computational tools can also result in the generation of large-scale data sets that can inform automated assessment of rehabilitation. Interpretable automated assessment can leave more time for clinicians to focus on treatment and allow for remotely supervised therapy at the home. Methods: In our first experiment, we developed a rating process and accompanying computational tool to assist clinicians in following a standardized movement assessment process relating functionality to movement quality. We conducted three studies with three different versions of the computational rating tool. Clinicians rated task, segment, and movement feature performance for 440 videos in which stroke survivors executed standardized upper extremity therapy tasks related to functional activities. In our second experiment, we used the 440 rated videos, in addition to 140 videos of unimpaired subjects performing the same tasks, to improve our previously developed automated assessment ensemble model that automatically generates segmentation times and task ratings across the impaired and unimpaired movement. The automated assessment ensemble integrates expert knowledge constraints into data-driven training through a combination of HMM, transformer, MSTCN++, and decision tree computational modules. In our third experiment, we used the therapist and automated ratings to develop a four-layer Hierarchical Bayesian Model (HBM) for computing the statistical relation of movement quality changes to functionality. We first calculated conditional layer probabilities using clinician ratings of task, segment, and movement features. We increased the granularity of observation of the HBM by formulating ΔHBM, a correlation graph between kinematics and movement composite features. Finally, we used k-means clustering on the ΔHBM to identify three clusters of features among the 16 movement composite and 20 kinematic features and used the centroid of these clusters as the weights of the input data to our computational assessment ensemble. Results: We evaluated the efficacy of our rating interface in terms of inter-rater reliability (IRR) across tasks, segments, and movement features. The third version of the interface produced an average IRR of 67%, while the time per session (TPS) was the lowest of the three studies. By analyzing the ratings, we were able to identify a small number of movement features that have the highest probability of predicting functional improvement. We evaluated the performance of our automated assessment model using 60% impaired and 40% unimpaired movement data and achieved a frame-wise segmentation accuracy of 87.85±0.58 and a block-segmentation accuracy of 98.46±1.6. We also demonstrated the performance of our proposed HBM in correlation to clinician ratings with a correlation of over 90%. The HBM also generates a correlation graph, ΔHBM that relates 16 composite movement features to the 20 kinematic features. We can thus integrate the HBM into the computational assessment ensemble to perform automated and integrated movement quality and functionality assessment that is driven by computationally extracted kinematics. Conclusions: Combining standardized clinician ratings of videos with knowledge based and data-driven computational analysis of rehabilitation movement allows the expression of an HBM that increases the observability of the relation of movement quality to functionality and enables the training of computational algorithms for automated assessment of rehabilitation movement. While our work primarily focuses on the upper extremity of stroke survivors, the models can be adopted to many other neurorehabilitation contexts.

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Section: Resultssupporting

confidence: 72%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%