Objective: This study aims to investigate the validity of machine learning-derived amount of real-world functional upper extremity (UE) use in individuals with stroke. We hypothesized that machine learning classification of wrist-worn accelerometry will be as accurate as frame-by-frame video labeling (ground truth). A second objective was to validate the machine learning classification against measures of impairment, function, dexterity, and self-reported UE use. Design: Cross-sectional and convenience sampling. Setting: Outpatient rehabilitation. Participants: Individuals (>18 years) with neuroimaging-confirmed ischemic or hemorrhagic stroke >6-months prior (n = 31) with persistent impairment of the hemiparetic arm and upper extremity Fugl-Meyer (UEFM) score = 12–57. Methods: Participants wore an accelerometer on each arm and were video recorded while completing an “activity script” comprising activities and instrumental activities of daily living in a simulated apartment in outpatient rehabilitation. The video was annotated to determine the ground-truth amount of functional UE use. Main outcome measures: The amount of real-world UE use was estimated using a random forest classifier trained on the accelerometry data. UE motor function was measured with the Action Research Arm Test (ARAT), UEFM, and nine-hole peg test (9HPT). The amount of real-world UE use was measured using the Motor Activity Log (MAL). Results: The machine learning estimated use ratio was significantly correlated with the use ratio derived from video annotation, ARAT, UEFM, 9HPT, and to a lesser extent, MAL. Bland–Altman plots showed excellent agreement between use ratios calculated from video-annotated and machine-learning classification. Factor analysis showed that machine learning use ratios capture the same construct as ARAT, UEFM, 9HPT, and MAL and explain 83% of the variance in UE motor performance. Conclusion: Our machine learning approach provides a valid measure of functional UE use. The accuracy, validity, and small footprint of this machine learning approach makes it feasible for measurement of UE recovery in stroke rehabilitation trials.

/pdf/concurrent-validity-of-machine-learning-classified-dipw6sf6.pdf

Concurrent validity of machine learning-classified functional upper extremity use from accelerometry in chronic stroke

Stroke is a leading cause of long-term disability worldwide. With the advancements in sensor technologies and data availability, artificial intelligence (AI) holds the promise of improving the amount, quality and efficiency of care and enhancing the precision of stroke rehabilitation. We aimed to identify and characterize the existing research on AI applications in stroke recovery and rehabilitation of adults, including categories of application and progression of technologies over time. Data were collected from peer-reviewed articles across various electronic databases up to January 2024. Insights were extracted using AI-enhanced multi-method, data-driven techniques, including clustering of themes and topics. This scoping review summarizes outcomes from 704 studies. Four common themes (impairment, assisted intervention, prediction and imaging, and neuroscience) were identified, in which time-linked patterns emerged. The impairment theme revealed a focus on motor function, gait and mobility, while the assisted intervention theme included applications of robotic and brain-computer interface (BCI) techniques. AI applications progressed over time, starting from conceptualization and then expanding to a broader range of techniques in supervised learning, artificial neural networks (ANN), natural language processing (NLP) and more. Applications focused on upper limb rehabilitation were reviewed in more detail, with machine learning (ML), deep learning techniques and sensors such as inertial measurement units (IMU) used for upper limb and functional movement analysis. AI applications have potential to facilitate tailored therapeutic delivery, thereby contributing to the optimization of rehabilitation outcomes and promoting sustained recovery from rehabilitation to real-world settings. 

AI Applications in Adult Stroke Recovery and Rehabilitation: A Scoping Review Using AI

Background The recovery of upper extremity motor impairment after stroke remains a challenging task. The clinical effectiveness of repetitive transcranial magnetic stimulation (rTMS), which is believed to aid in the recovery process, is still uncertain. Methods A systematic search was conducted in Medline (Ovid), Cochrane and Embase electronic databases from March 28, 2014, to March 28, 2023. The inclusion criteria consisted of randomized controlled trials that assessed the effects of rTMS on the recovery of upper limb motor impairment among stroke patients. Various measurements, including the Fugl Meyer Assessment Upper Extremity Scale (FMA-UE), Brunnstrom recovery stage, Action Research Arm Test (ARAT), and Barthel index, were evaluated both before and after the intervention. Results Nineteen articles with 865 patients were included. When considering only the rTMS parameters, both inhibitory and excitatory rTMS improved FMA-UE (MD = 1.87, 95% CI = [0.88]–[2.86], p < 0.001) and Barthel index (MD = 9.73, 95% CI = [4.57]–[14.89], p < 0.001). When considering only the severity of upper limb hemiplegia, both less severe (MD = 1.56, 95% CI = [0.64]–[2.49], p < 0.001) and severe (MD = 2.05, 95% CI = [1.09]–[3.00], p < 0.001) hemiplegia benefited from rTMS based on FMA-UE. However, when considering the rTMS parameters, severity of hemiplegia and stroke stages simultaneously, inhibitory rTMS was found to be significantly effective for less severe hemiplegia in the acute and subacute phases (MD = 4.55, 95% CI = [2.49]–[6.60], p < 0.001), but not in the chronic phase based on FMA-UE. For severe hemiplegia, inhibitory rTMS was not significantly effective in the acute and subacute phases, but significantly effective in the chronic phase (MD = 2.10, 95% CI = [0.75]–[3.45], p = 0.002) based on FMA-UE. Excitatory rTMS was found to be significantly effective for less severe hemiplegia in the acute and subacute phases (MD = 1.93, 95% CI = [0.58]–[3.28], p = 0.005) based on FMA-UE. The improvements in Brunnstrom recovery stage and ARAT need further research. Conclusion The effectiveness of rTMS depends on its parameters, severity of hemiplegia, and stroke stages. It is important to consider all these factors together, as any single grouping method is incomplete.

The stratified effects of repetitive transcranial magnetic stimulation in upper limb motor impairment recovery after stroke: a meta-analysis

Wearable sensors, such as inertial measurement units (IMUs), can objectively measure upper limb performance outside clinical settings, providing complementary outcomes to support the rehabilitation of neurological patients. Measuring performance, and thus quantifying functional movements in daily life, is a necessary step towards the implementation of more personalized goal-adjusted therapy. However, it remains challenging to accurately and reliably separate functional from non-functional movements based on IMU data. Existing digital health metrics are typically too sensitive, classifying non-functional movements as functional or too specific. Additionally, these metrics rely on empirical thresholds, and their dependence on the individual has not been explored. This paper explores methods to optimize the thresholds used to compute IMU-based metrics (activity count, gross movement score, and GMAC) measuring functional upper limb movements on a group level and per individual. To this end, a preliminary study was conducted on 10 non-impaired subjects wearing IMUs on both wrists while performing predefined daily life activities. The optimized thresholds were found with cross-validation by maximizing the Youden index. At the group level, the most effective metric was the optimized GMAC, with a Youden index of 0.58 ± 0.05, achieved by combining thresholds on the magnitude of the arm's acceleration and the angular rotation of the arm. Notably, the individualized optimized thresholds on arm acceleration varied widely, ranging from 2 to 59 counts for the non-dominant hand. These findings suggest that personalized thresholds might be necessary to improve the detection of functional movements in daily life.

Optimizing Digital Health Metrics to Quantify Functional Upper Limb Movements with Inertial Measurement Units

Was ist Präzisionsneurorehabilitation und weshalb ist es seit einigen Jahren ein so häufiges Schlagwort? Welche Assessments und personalisierte Trainings sind im Klinikalltag möglich? Dieser Artikel geht diesen Fragen mit Beispielen aus dem Alltag der Neurorehabilitationsklinik Cereneo nach.

Präzisionsrehabilitation im Neuroreha-Alltag

Background: Arm use metrics derived from wrist-mounted movement sensors are widely used to quantify the upper limb performance in real-life conditions of individuals with stroke throughout motor recovery. The calculation of real-world use metrics, such as arm use duration and laterality preferences, relies on accurately identifying functional movements. Hence, classifying upper limb activity into functional and non-functional classes is paramount. Acceleration thresholds are conventionally used to distinguish these classes. However, these methods are challenged by the high inter and intra-individual variability of movement patterns. In this study, we developed and validated a machine learning classifier for this task and compared it to methods using conventional and optimal thresholds. Methods: Individuals after stroke were video-recorded in their home environment performing semi-naturalistic daily tasks while wearing wrist-mounted inertial measurement units. Data were labeled frame-by-frame following the Taxonomy of Functional Upper Limb Motion definitions, excluding whole-body movements, and sequenced into 1-s epochs. Actigraph counts were computed, and an optimal threshold for functional movement was determined by receiver operating characteristic curve analyses on group and individual levels. A logistic regression classifier was trained on the same labels using time and frequency domain features. Performance measures were compared between all classification methods. Results: Video data (6.5 h) of 14 individuals with mild-to-severe upper limb impairment were labeled. Optimal activity count thresholds were ≥20.1 for the affected side and ≥38.6 for the unaffected side and showed high predictive power with an area under the curve (95% CI) of 0.88 (0.87,0.89) and 0.86 (0.85, 0.87), respectively. A classification accuracy of around 80% was equivalent to the optimal threshold and machine learning methods and outperformed the conventional threshold by ∼10%. Optimal thresholds and machine learning methods showed superior specificity (75–82%) to conventional thresholds (58–66%) across unilateral and bilateral activities. Conclusion: This work compares the validity of methods classifying stroke survivors’ real-life arm activities measured by wrist-worn sensors excluding whole-body movements. The determined optimal thresholds and machine learning classifiers achieved an equivalent accuracy and higher specificity than conventional thresholds. Our open-sourced classifier or optimal thresholds should be used to specify the intensity and duration of arm use.

/pdf/classification-of-functional-and-non-functional-arm-use-by-snvmpdp7.pdf

Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke

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Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke