ArticleSensors (Basel, Switzerland)2014
Toward real-time automated detection of turns during gait using wearable inertial measurement units.
Article in Sensors (Basel, Switzerland), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- The Effect of Non-Invasive Brain Stimulation on Running Performance and Inertial Measurement Unit-Derived Spatiotemporal Parameters in Endurance-Trained Runners.Sensors (Basel, Switzerland) · 2026Article
- A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic data.Scientific reports · 2025Article
- Deriving Motor States and Mobility Metrics from Gamified Augmented Reality Rehabilitation Exercises in People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Detection of Gait Events Using Ear-Worn IMUs During Functional Movement Tasks.Sensors (Basel, Switzerland) · 2025Article
- How Approaching Angle, Bottleneck Width and Walking Speed Affect the Use of a Bottleneck by Individuals.Sensors (Basel, Switzerland) · 2024Article
- An investigation of the contribution of different turn speeds during standing turns in individuals with and without Parkinson's disease.Scientific reports · 2022Article
- Article
- Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit.Frontiers in neurorobotics · 2022Article
- Inertial Sensor Algorithms to Characterize Turning in Neurological Patients With Turn Hesitations.IEEE transactions on bio-medical engineering · 2021Article
- Atypical Gait Cycles in Parkinson's Disease.Sensors (Basel, Switzerland) · 2021Article
- Abnormal Gait Detection Using Wearable Hall-Effect Sensors.Sensors (Basel, Switzerland) · 2021Article
- Centre of Pressure Estimation during Walking Using Only Inertial-Measurement Units and End-To-End Statistical Modelling.Sensors (Basel, Switzerland) · 2020Article
- Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson's Disease.Sensors (Basel, Switzerland) · 2020Article
- Characterizing Human Box-Lifting Behavior Using Wearable Inertial Motion Sensors.Sensors (Basel, Switzerland) · 2020Article
- Article
- Human arm joints reconstruction algorithm in rehabilitation therapies assisted by end-effector robotic devices.Journal of neuroengineering and rehabilitation · 2018Article
- Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions.Frontiers in behavioral neuroscience · 2018Article
- Quantifying performance on an outdoor agility drill using foot-mounted inertial measurement units.PloS one · 2017Article
- Wearable IMU for Shoulder Injury Prevention in Overhead Sports.Sensors (Basel, Switzerland) · 2016Article
- Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors.Sensors (Basel, Switzerland) · 2016Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement units. Several different sensor positions (head, back and legs) and three different detection criteria (orientation, angular velocity and both) are compared with regard to their ability to correctly detect turn onset. Furthermore, the different sensor positions are compared with regard to their ability to predict the turn direction and amplitude. The evaluation was performed on ten healthy subjects who performed left/right turns at three amplitudes (22, 45 and 90 degrees). Results showed that turn onset can be most accurately detected with sensors on the back and using a combination of orientation and angular velocity. The same setup also gives the best prediction of turn direction and amplitude. Preliminary measurements with a single amputee were also performed and highlighted important differences such as slower turning that need to be taken into account.
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.