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Basketball Activity Classification Based on Upper Body Kinematics and Dynamic Time Warping.

International journal of sports medicine
Basketball activity classification can help document players' statistics, allow coaches, trainers and the medical team to quantitatively supervise players' physical exertion and optimize training strategy, and further help prevent potential injuries....

A new deep learning-based method for the detection of gait events in children with gait disorders: Proof-of-concept and concurrent validity.

Journal of biomechanics
The stance and swing phases of the gait cycle are defined by foot strike (FS) and foot off (FO). Accurate determination of these events is thus an essential component of 3D motion recordings processing. Several methods have been developed for the aut...

Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training.

Sensors (Basel, Switzerland)
Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the pote...

Determining motions with an IMU during level walking and slope and stair walking.

Journal of sports sciences
This study investigated whether using an inertial measurement unit (IMU) can identify different walking conditions, including level walking (LW), descent (DC) and ascent (AC) slope walking as well as downstairs (DS) and upstairs (US) walking. Thirty ...

Using Recurrent Neural Networks to Compare Movement Patterns in ADHD and Normally Developing Children Based on Acceleration Signals from the Wrist and Ankle.

Sensors (Basel, Switzerland)
Attention deficit and hyperactivity disorder (ADHD) is a neurodevelopmental condition that affects, among other things, the movement patterns of children suffering it. Inattention, hyperactivity and impulsive behaviors, major symptoms characterizing ...

Methodology and validation for identifying gait type using machine learning on IMU data.

Journal of medical engineering & technology
With the rising popularity of activity tracking, there is a desire to not only count the number of steps a person takes, but also identify the type of step (e.g., walking or running) they are taking. For rehabilitation and athletic training, this dif...

Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach.

Sensors (Basel, Switzerland)
Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns...

Wheelchair propulsion: Force orientation and amplitude prediction with Recurrent Neural Network.

Journal of biomechanics
The aim of this study was to use Recurrent Neural Network (RNN) to predict the orientation and amplitude of the applied force during the push phase of manual wheelchair propulsion. Trunk and the right-upper limb kinematics data were assessed with an ...

Automatic Annotation for Human Activity Recognition in Free Living Using a Smartphone.

Sensors (Basel, Switzerland)
Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, espec...

Comparison of Data Preprocessing Approaches for Applying Deep Learning to Human Activity Recognition in the Context of Industry 4.0.

Sensors (Basel, Switzerland)
According to the Industry 4.0 paradigm, all objects in a factory, including people, are equipped with communication capabilities and integrated into cyber-physical systems (CPS). Human activity recognition (HAR) based on wearable sensors provides a m...