ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2021
Real-Time Activity Recognition With Instantaneous Characteristic Features of Thigh Kinematics.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 42 citations in OpenAlex.
- Real-Time Foot Height Estimation and Activity Classification Using a Foot-Mounted IMU Implemented on a Smartphone.Sensors (Basel, Switzerland) · 2026Article
- Ambilateral Activity Recognition and Continuous Adaptation with a Powered Knee-Ankle Prosthesis.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2025Article
- Lateral walking gait phase recognition for hip exoskeleton by denoising autoencoder-LSTM.Computational and structural biotechnology journal · 2025Article
- Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles.IEEE transactions on bio-medical engineering · 2024Article
- Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets.IEEE robotics and automation letters · 2024Article
- A SE-DenseNet-LSTM model for locomotion mode recognition in lower limb exoskeleton.PeerJ. Computer science · 2024Article
- Swing-phase detection of locomotive mode transitions for smooth multi-functional robotic lower-limb prosthesis control.Frontiers in robotics and AI · 2024Article
- Controlling Powered Prosthesis Kinematics Over Continuous Inter-Leg Transitions Between Walking and Stair Ascent/Descent.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Improving Amputee Endurance over Activities of Daily Living with a Robotic Knee-Ankle Prosthesis: A Case Study.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems · 2023Article
- Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking.IEEE transactions on robotics : a publication of the IEEE Robotics and Automation Society · 2023Article
- Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.Sensors (Basel, Switzerland) · 2022Article
- IMU-Based Classification of Locomotion Modes, Transitions, and Gait Phases with Convolutional Recurrent Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Modeling the Transitional Kinematics Between Variable-Incline Walking and Stair Climbing.IEEE transactions on medical robotics and bionics · 2022Article
- Stair Ascent Phase-Variable Control of a Powered Knee-Ankle Prosthesis.IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Current supervised learning or deep learning-based activity recognition classifiers can achieve high accuracy in recognizing locomotion activities. Most available techniques use a high-dimensional space of features, e.g., combinations of EMG, kinematics and kinetics, and transformations over those signals. The associated classification rules are therefore complex; the machine tries to understand the human, but the human does not understand the machine. This paper presents an activity recognition system that uses signals from a thigh-mounted IMU and a force sensitive resistor to classify transitions between sitting, walking, stair ascending, and stair descending. The system uses the thigh's orientation and velocity with foot contact information at specific moments within a given activity as the features to classify transitions to other activities. We call these Instantaneous Characteristic Features (ICFs). Because these ICFs are biomechanically intuitive, they are easy for the user to understand and thus control the activity transitions of wearable robots. We assessed our classification algorithm offline using an existing dataset with 10 able-bodied subjects and online with another 10 able-bodied subjects wearing a real-time system. The offline study analyzed the effect of subject-dependency and ramp inclinations. The real-time classification accuracy was evaluated before and after training the subjects on the ICFs. The real-time system achieved overall pre-subject-training and post-subject-training error rates of 0.59% ± 0.24% and 0.56% ± 0.20%, respectively. We also evaluated the feasibility of our ICFs for amputee ambulation by analyzing a public dataset with the open-source bionic leg. The simplicity of these classification rules demonstrates a new paradigm for activity recognition where the human can understand the machine and vice-versa.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.