AIMC Topic: Foot

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Sexing white 2D footprints using convolutional neural networks.

PloS one
Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi...

Segmentation Approaches for Diabetic Foot Disorders.

Sensors (Basel, Switzerland)
Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabe...

Machine-Learning Based Determination of Gait Events from Foot-Mounted Inertial Units.

Sensors (Basel, Switzerland)
A promising but still scarcely explored strategy for the estimation of gait parameters based on inertial sensors involves the adoption of machine learning techniques. However, existing approaches are reliable only for specific conditions, inertial me...

Artificial Intelligence-Assisted motion capture for medical applications: a comparative study between markerless and passive marker motion capture.

Computer methods in biomechanics and biomedical engineering
We aimed to determine whether artificial intelligence (AI)-assisted markerless motion capture software is useful in the clinical medicine and rehabilitation fields. Currently, it is unclear whether the AI-assisted markerless method can be applied to ...

Hier R-CNN: Instance-Level Human Parts Detection and A New Benchmark.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Detecting human parts at instance-level is an essential prerequisite for the analysis of human keypoints, actions, and attributes. Nonetheless, there is a lack of a large-scale, rich-annotated dataset for human parts detection. We fill in the gap by ...

Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach.

Gait & posture
BACKGROUND: Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial acceler...

Combining wearable sensor signals, machine learning and biomechanics to estimate tibial bone force and damage during running.

Human movement science
There are tremendous opportunities to advance science, clinical care, sports performance, and societal health if we are able to develop tools for monitoring musculoskeletal loading (e.g., forces on bones or muscles) outside the lab. While wearable se...

Neurodynamic modeling of the fruit fly Drosophila melanogaster.

Bioinspiration & biomimetics
This manuscript describes neuromechanical modeling of the fruit fly Drosophila melanogaster in the form of a hexapod robot, Drosophibot, and an accompanying dynamic simulation. Drosophibot is a testbed for real-time dynamical neural controllers model...

Fast Wearable Sensor-Based Foot-Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping.

Sensors (Basel, Switzerland)
Classification of foot-ground contact phases, as well as the swing phase is essential in biomechanics domains where lower-limb motion analysis is required; this analysis is used for lower-limb rehabilitation, walking gait analysis and improvement, an...

Kinematic Calibration of a Parallel 2-UPS/RRR Ankle Rehabilitation Robot.

Journal of healthcare engineering
In order to better perform rehabilitation training on the ankle joint complex in the direction of dorsiflexion/plantarflexion and inversion/eversion, especially when performing the isokinetic muscle strength exercise, we need to calibrate the kinemat...