A Real-Time Missing Data Recovery Method Using Recurrent Neural Network for Multiple Transmissions

Lin, Bor-Shing; Lin, Yu‐Syuan; Lee, I-Jung; Lin, Bor‐Shyh

doi:10.1007/978-3-030-03745-1_13

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…However, accelerometer and gyroscope data are obtained from signals transmitted by multiple devices. When a server cannot receive and process data instantly, the data spikes, packet loss, and data gaps may occur [24]. In [25], a functional data technique was used to remove noise, and IMU trajectory coefficients determined using this technique were useful for characterizing the motor impairment levels of patients with stroke.…”

Section: Introductionmentioning

confidence: 99%

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness

Hwang,

Tung,

Chen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Patients who experience upper-limb paralysis after stroke require continual rehabilitation. Rehabilitation must be evaluated for appropriate treatment adjustment; such evaluation can be performed using inertial measurement units (IMUs) instead of standard scales or subjective evaluations. However, IMUs produce large quantities of discretized data, and using these data directly is challenging. In this study, B-splines were used to estimate IMU trajectory data for objective evaluations of hand function and stability by using machine learning classifiers and mathematical indices. IMU trajectory data from a 2018 study on upper-limb rehabilitation were used to validate the proposed method. Features extracted from B-spline trajectories could be used to classify individuals in the 2018 study with high accuracy, and the proposed indices revealed differences between these groups. Compared with conventional rehabilitation evaluation methods, the proposed method is more objective and effective.

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Section: Introductionmentioning

confidence: 99%

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness

Hwang,

Tung,

Chen

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…[18] computed summary statistics, such as centrality and variablity, and the extremes of variable trajectories, which yielded over 3000 features. Related raw data were collected from signals transmitted from multiple devices, and data spikes, packet loss, and data gaps were noted when the server could not receive and process data instantly [19]. Furthermore, an inherent deficiency in related devices, such as clock stretching, can result in sporadic incorrect data points [20].…”

Section: Introductionmentioning

confidence: 99%

Use of Functional Data to Model the Trajectory of an Inertial Measurement Unit and Classify Levels of Motor Impairment for Stroke Patients

Hwang

Lin

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor impairment evaluations are key rehabilitation-related assessments for patients with stroke. Currently, such evaluations are subjective; they are based on physicians' judgements regarding the actions performed by patients. This leads to inconsistent clinical results. Many inertial sensing elements for motion detection have been designed. However, to more easily and rapidly evaluate motor impairment, we require a system that can collect data effectively to predict the degree of motor impairment. Lin et al. used data gloves equipped with an inertial measurement unit (IMU) to collect movement trajectories for motor impairment evaluations in patients with stroke. The present study used functional data analysis to model data trajectories to reduce the influence of noise from IMU data and proposed using coefficients of function as features for classifying motor impairment. To verify the appropriateness of feature construction, five classification methods were used to evaluate the extracted features in terms of the overall and sensor-specific ability to classify levels of motor impairment. The results indicated that the features derived from cubic smoothing splines could effectively reflect key data characteristics, and a support vector machine yielded relatively high overall and sensor-specific accuracy for distinguishing between levels of motion impairment in patients with stroke. Future data glove systems can contain cubic smoothing splines to extract hand function features and then classify motion impairment for appropriate rehabilitation programs to be prescribed.

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A Real-Time Missing Data Recovery Method Using Recurrent Neural Network for Multiple Transmissions

Cited by 2 publications

References 12 publications

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness

B-Spline Modeling of Inertial Measurements for Evaluating Stroke Rehabilitation Effectiveness

Use of Functional Data to Model the Trajectory of an Inertial Measurement Unit and Classify Levels of Motor Impairment for Stroke Patients

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