EMG and IMU data for sitting knee extension/bending movements

Citation Author(s):
TianXing
Yang
Submitted by:
yang Tianxing
Last updated:
Tue, 04/09/2024 - 22:29
DOI:
10.21227/fwst-rj90
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Abstract 

Highly accurate and lightweight automated movements quality assessment is essential for home rehabilitation patients. We propose a method for the assessment and quantification of movement quality based on the differential feature segments, the objective  is to emulate the expert evaluations of physicians as closely as possible with minimal data features. Employing the Gaussian mixture model (GMM) to divide continuous trend time-series data into fragment features, defined as feature segments. Calculating the log-likelihood of sample movement feature segments to their corresponding standard movement feature segments, then fits these calculations into Fuzzy comprehensive evaluation (FCE) results to quantify assessment scores. We used the seated knee extension/flexion movement to validate, collecting data from inertial measurement units (IMU) and electromyography (EMG) sensors. Four boosting algorithms were tested in the data analysis experiments, the results demonstrated that using as few as two sensors and the LightGBM algorithm could emulate the physician's FCE estimate with a determination coefficient of 0.84. Compared to Dynamic time warping (DTW) and traditional GMM approaches, the proposed method based on segmented feature segments yielded superior GMM quantified regression scores, showing higher correlation with FCE outcomes. This method could maximally utilizes the information in time-series data to closely emulate physician evaluations with a minimal amount of data features.

Instructions: 

This dataset collects relevant data on sitting knee joint extension/flexion, which is a rehabilitation exercise aimed at muscle strength and range of motion after knee replacement surgery. This dataset was constructed with the participation of 10 volunteers, including 5 healthy individuals and 5 patients in the recovery stage of knee replacement surgery. The methodology framework of this study includes data collection using a single wire GER sensor and a 15g MPU6050 inertial sensor. The GER sensor is strategically placed in the middle of the quadriceps, while the MPU6050 is fixed above the ankle of the participant's tibia to accurately monitor movement. The dataset has 24 columns, each containing approximately ten actions, totaling 292 valid actions, which can be split and used.