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2 results for “Force Myography”
Dataset on Force Myography for Human Robot Interactions
<p>Force myography (FMG) is a contemporary, non-invasive, wearable technology that can read the underlying muscle volumetric changes during muscle contractions and expansions. The FMG technique can be used in recognizing human applied hand forces during physical human robot interactions (pHRI) via data-driven models. Several FMG-based pHRI studies were conducted in 1D, 2D and 3D during dynamic interactions between a human participant and a robot to realize human applied forces in intended directions during certain tasks. Raw FMG signals were collected via 16-channel (forearm) and 32-channel (forearm and upper arm) FMG bands while interacting with a biaxial stage (linear robot) and a serial manipulator (Kuka robot). In this paper, we present the datasets and their structures, the pHRI environments, and the collaborative tasks performed during the studies. We believe these datasets can be useful in future studies on FMG biosignal-based pHRI control design.</p> <p>The full description of this dataset, it’s components and structure are available in the data descriptor article submitted in MDPI Data. Please cite the following data descriptor article if you are using this open-access dataset for legitimate scientific research:</p> <p> U. Zakia, and C. Menon. Dataset on Force Myography for Human Robot Interactions. Data 2022, vol., no., pp, <a href="https://doi.org/10.3390/data7040050">doi:</a> (submitted on June 2022).</p> <p> </p>
FIDGM: A Force Myography and IMU Dataset for Precise Gait Measurement
<p>This dataset contains sensor values from Force Myography (FMG) sensors and Inertial Measurement Units (IMUs) and corresponding Ankle Joint Power and Ground Reaction Force. The FMG strap was positioned about two inches above the ankle of the left leg. Four IMUs were positioned on foot, distal shank, distal thigh, and proximal thigh of the left leg. There are sensors values and corresponding reference values from nine participants and five different walking speeds (0.4 m/s, 0.7 m/s, 1.0 m/s, 1.3 m/s, and 1.6 m/s). This dataset portrays how values from different sensors vary with respect to Ankle Joint Power and Ground Reaction Force. The dataset can be utilized to train machine learning models to predict Ankle Joint Power and Ground Reaction Force using FMG and IMUs sensor values, which in turn can eliminate the requirement of a complex biomechanical setup to measure Ankle Joint Power and Ground Reaction Force.</p>
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