ArticleSensors (Basel, Switzerland)2019
Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 81 citations in OpenAlex.
- Deep Learning-Based Temporal Gait Analysis Using a Smartphone IMU in Older Adults with and Without Non-Specific Low Back Pain.Bioengineering (Basel, Switzerland) · 2026Article
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis.Cureus · 2025Review
- Clinical trials of GPE-based muscle support algorithm for robotic hip exoskeleton: a pilot study.Scientific reports · 2025Article
- A Review of Gait Analysis Using Gyroscopes and Inertial Measurement Units.Sensors (Basel, Switzerland) · 2025Review
- A Recurrent Deep Network for Gait Phase Identification from EMG Signals During Exoskeleton-Assisted Walking.Sensors (Basel, Switzerland) · 2024Article
- Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction.Annals of biomedical engineering · 2023Article
- Design and Optimization of Lower Limb Rehabilitation Exoskeleton with a Multiaxial Knee Joint.Biomimetics (Basel, Switzerland) · 2023Article
- A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses.Annual reviews in control · 2023Article
- Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition.Frontiers in neurorobotics · 2023Review
- Co-simulation of human digital twins and wearable inertial sensors to analyse gait event estimation.Frontiers in bioengineering and biotechnology · 2023Article
- Effects of stance controlFrontiers in bioengineering and biotechnology · 2023Article
- Biomechanical Effects of Adding an Ankle Soft Actuation in a Unilateral Exoskeleton.Biosensors · 2022Article
- Development of a 3D Relative Motion Method for Human-Robot Interaction Assessment.Sensors (Basel, Switzerland) · 2022Article
- Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar.Sensors (Basel, Switzerland) · 2021Article
- A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States.Sensors (Basel, Switzerland) · 2021Article
- Generation of Gait Events with a FSR Based Cane Handle.Sensors (Basel, Switzerland) · 2021Article
- Asymmetric Gait Analysis Using a DTW Algorithm with Combined Gyroscope and Pressure Sensor.Sensors (Basel, Switzerland) · 2021Article
- The Actuation System of the Ankle Exoskeleton T-FLEX: First Use Experimental Validation in People with Stroke.Brain sciences · 2021Article
- Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration.Sensors (Basel, Switzerland) · 2021Article
- Sensor Fusion in Assistive and Rehabilitation Robotics.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0 . 75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.
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.