AIMC Topic: Leg

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Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors.

Sensors (Basel, Switzerland)
The estimation of the speed of human motion from wearable IMU sensors is required in applications such as pedestrian dead reckoning. In this paper, we test deep learning methods for the prediction of the motion speed from raw readings of a low-cost I...

Study on the Effects of Different Seat and Leg Support Conditions of a Trunk Rehabilitation Robot.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Performance of trunk rehabilitation exercises while sitting on movable surfaces with feet on the ground can increase trunk and leg muscle activations, and constraining the feet to move with the seat isolates control of the trunk. However, there are n...

BirdBot achieves energy-efficient gait with minimal control using avian-inspired leg clutching.

Science robotics
Designers of legged robots are challenged with creating mechanisms that allow energy-efficient locomotion with robust and minimalistic control. Sources of high energy costs in legged robots include the rapid loading and high forces required to suppor...

TALBOT: A Track-Leg Transformable Robot.

Sensors (Basel, Switzerland)
This article introduces a tracked-leg transformable robot, TALBOT. The mechanical and electrical design, control method, and environment perception based on LiDAR are discussed. The original tracked-leg transformable structure allows the robot to swi...

Crab-inspired compliant leg design method for adaptive locomotion of a multi-legged robot.

Bioinspiration & biomimetics
has unique limb structures composed of a hard exoskeleton and flexible muscles. They enable the crab to locomote adaptively and safely on various terrains. In this work, we investigated the limb structures, motion principle, and gaits of the crab usi...

Deep learning for automatic segmentation of thigh and leg muscles.

Magma (New York, N.Y.)
OBJECTIVE: In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach.

Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy.

NMR in biomedicine
Cerebral palsy is a neurological condition that is known to affect muscle growth. Detailed investigations of muscle growth require segmentation of muscles from MRI scans, which is typically done manually. In this study, we evaluated the performance o...

Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only.

Sensors (Basel, Switzerland)
Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect w...

Deep learning-based photoplethysmography classification for peripheral arterial disease detection: a proof-of-concept study.

Physiological measurement
A proof-of-concept study to assess the potential of a deep learning (DL) based photoplethysmography PPG ('DLPPG') classification method to detect peripheral arterial disease (PAD) using toe PPG signals.PPG spectrogram images derived from our previous...