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Found 7 papers, 3 papers with code
Machine Vision-Enabled Sports Performance Analysis
$\textbf{Goal:}$ This study investigates the feasibility of monocular 2D markerless motion capture (MMC) using a single smartphone to measure jump height, velocity, flight time, contact time, and range of motion (ROM) during motor tasks.
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification
Additionally, a minimum of 3 IMUs are required to outperform a single camera.
Quantifying Jump Height Using Markerless Motion Capture with a Single Smartphone
This study evaluates how accurately markerless motion capture (MMC) with a single smartphone can measure bilateral and unilateral CMJ jump height.
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifiers
We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors.
Automated Mobility Context Detection with Inertial Signals
We explore two different approaches to this task: (1) using gait descriptors and features extracted from the input inertial signals sampled during walking episodes, together with classic machine learning algorithms, and (2) treating the input inertial signals as time series data and leveraging end-to-end state-of-the-art time series classifiers.
Human Activity Recognition with Convolutional Neural Netowrks
The problem of automatic identification of physical activities performed by human subjects is referred to as Human Activity Recognition (HAR).
Automatic Classification of Knee Rehabilitation Exercises Using a Single Inertial Sensor: a Case Study
In this paper, we present a classification method for unsupervised rehabilitation exercises, based on a segmentation process that extracts repetitions from a longer signal activity.
