A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study

Jung, Hee‐Tae; Park, Joonwoo; Jeong, Jugyeong; Ryu, Taekyeong; Kim, Yangsoo; Lee, Sunghoon I.

doi:10.1109/bhi.2018.8333358

Cited by 14 publications

(15 citation statements)

References 16 publications

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“…For instance, in [88], the authors used K nearest neighbor and SVM classifiers to identify compensatory motions in the pressure distribution, achieving F1 scores as high as 0.993. Jung et al [89] on the other hand used model trees [90] and found modest results with an F-measure of 79.29 percent and a ROC of 0.91. Lee, in [91], used a hybrid approach that combines a rulebased knowledge model and a predictive model for classifying the quality of motion as 0, 1, or 2.…”

Section: B Work In Automated Exercise Assessmentmentioning

confidence: 99%

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Rahman

Sarker

Haque

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Post-stroke therapy restores lost skills. Traditionally, patients are supported by skilled therapists who monitor their progress and evaluate the program's effectiveness. Due to a shortage of qualified therapists, rehabilitation facilities are both expensive and inadequate. Furthermore, evaluations may be subjective and prone to errors. These limitations motivate the researchers to devise automated systems with minimal human intervention, therapistlike assessment, and broader outreach. This article reviews seminal works from 2013 onwards, qualitatively and quantitatively adapting the PRISMA approach to examine the potential of robot-assisted, virtual reality-based rehabilitation and automated assessments through data-driven learning. Extensive experimentation on KIMORE and UI-PRMD datasets reveal high agreement between automated methods and therapists. Our investigation shows that deep learning with spatio-temporal skeleton data and dynamic attention outperforms others, with an RMSE as low as 0.55. Fully automated rehabilitation is still in development, but, being an active research topic, it could hasten objective assessment and improve outreach.

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Section: B Work In Automated Exercise Assessmentmentioning

confidence: 99%

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

Rahman

Sarker

Haque

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…This work also differs from [16], which utilizes a transmit-mode sensing method that requires skin contact on the capacitive sensor array, whereas our CSA can detect both proximity and touch. IMUs have been previously used for upper limb motion quality evaluation in [19]. The researchers engineered various features from IMU data and adopted a model tree classifier.…”

Section: A Sensors and Pattern Recognitionmentioning

confidence: 99%

An FPGA-Based Upper-Limb Rehabilitation Device for Gesture Recognition and Motion Evaluation Using Multi-Task Recurrent Neural Networks

et al. 2022

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Upper-Extremity motor impairment affects millions of Americans due to cerebrovascular incidents, spinal cord injuries, or brain trauma. Current therapy practices used to assist these individuals in regaining motor functionality often require extensive time at rehabilitation facilities with potentially prohibitive travel or financial costs. This work presents a mobile low-cost field programmable gate array (FPGA)-smart rehabilitation system that can be used in home environments. The prototype is a rehabilitation table instrumented with a capacitive sensor array (CSA) to track upper-extremity motions of the user through proximity or touch. In addition, inertial measurement units (IMUs) are placed on the affected upper limb and combined with the CSA data with our sensor fusion signal processing architecture. Motions are classified and evaluated using multi-task convolutional recurrent neural networks with three additional motion quality output classes to personalize recognition based on the particular motor skills of each patient. The prototype achieves above 99% accuracy with 32-bit fixed-point format implementation for recognizing dynamic motions and identifying unnatural characteristics (i.e., tremor or limited flexion and extension) in upper limb motions based on sensor values. The convolutional recurrent neural network (C-RNN) fusion classification network is implemented on a 200 MHz Zynq ZCU104 FPGA using an HLS-based design optimized with pipelining and parallelism techniques and achieves 5.4x speedup compared to ARM ® Cortex-A53 implementation running at an operating frequency of 1.3 GHz. The prototype is also demonstrated to perform the machine learning classification in real-time.

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“…In [15], a wireless surface Electromyography (sEMG) device was used to monitor the muscle recruitment of the post-stroke patients to see the effect of orthotic intervention. In clinical environments, five wearable sensors were placed on the trunk, upper and forearm of the two upper limbs to measure the reaching behaviours of the stroke survivors [21]. To monitor motor functions of stroke patients during rehabilitation sessions at clinics, an ecosystem including a jack and a cube for hand grasping monitoring, as well as a smart watch for arm dynamic monitoring was designed [7].…”

Section: Sensing Techniques For Automated Stroke Rehabilitation Monit...mentioning

confidence: 99%

“…Data collection. In contrast to other afore-mentioned sensing techniques [21][7] [15][11], in this study we collected the accelerometer data from wrist-worn sensors in free-living environments. The sensor used for this study, i.e., AX3 [1], is a triaxial accelerometer logger that was designed for physical activity/behaviour monitoring, and it has been widely used in the medical community (e.g., for the UK Biobank physical activity study [10]).…”

Section: Data Acquisitionmentioning

confidence: 99%

Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments

Chen¹,

Yu²,

Shi³

et al. 2020

Preprint

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Stroke is known as a major global health problem, and for stroke survivors it is key to monitor the recovery levels. However, traditional stroke rehabilitation assessment methods (such as the popular clinical assessment) can be subjective and expensive, and it is also less convenient for patients to visit clinics in a high frequency. To address this issue, in this work based on wearable sensing and machine learning techniques, we developed an automated system that can predict the assessment score in an objective and continues manner. With wrist-worn sensors, accelerometer data was collected from 59 stroke survivors in free-living environments for a duration of 8 weeks, and we aim to map the week-wise accelerometer data (3 days per week) to the assessment score by developing signal processing and predictive model pipeline. To achieve this, we proposed two new features, which can encode the rehabilitation information from both paralysed/non-paralysed sides while suppressing the high-level noises such as irrelevant daily activities. We further developed the longitudinal mixed-effects model with Gaussian process prior (LMGP), which can model the random effects caused by different subjects and time slots (during the 8 weeks). Comprehensive experiments were conducted to evaluate our system on both acute and chronic patients, and the results suggested its effectiveness.

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A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study

Cited by 14 publications

References 16 publications

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

AI-Driven Stroke Rehabilitation Systems and Assessment: A Systematic Review

An FPGA-Based Upper-Limb Rehabilitation Device for Gesture Recognition and Motion Evaluation Using Multi-Task Recurrent Neural Networks

Automated Stroke Rehabilitation Assessment using Wearable Accelerometers in Free-Living Environments

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