SynthesisSensors (Basel, Switzerland)2020
Machine Learning Approaches for Activity Recognition and/or Activity Prediction in Locomotion Assistive Devices-A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Social media for palliative and end-of-life care research: a systematic review.BMJ supportive & palliative care · 2024Pooled it
- A Review on Locomotion Mode Recognition and Prediction When Using Active Orthoses and Exoskeletons.Sensors (Basel, Switzerland) · 2022Pooled it
- Evaluating Machine Learning-Based Classification of Human Locomotor Activities for Exoskeleton Control Using Inertial Measurement Unit and Pressure Insole Data.Sensors (Basel, Switzerland) · 2025Article
- A Review on Assisted Living Using Wearable Devices.Sensors (Basel, Switzerland) · 2024Review
- Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy.Bioengineering (Basel, Switzerland) · 2023Review
- Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation.Sensors (Basel, Switzerland) · 2023Article
- Database of lower limb kinematics and electromyography during gait-related activities in able-bodied subjects.Scientific data · 2023Article
- Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future.Journal of biomechanics · 2023Review
- Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing.Frontiers in big data · 2023Article
- Ambulation Mode Classification of Individuals with Transfemoral Amputation through A-Mode Sonomyography and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Adaptive Lower Limb Pattern Recognition for Multi-Day Control.Sensors (Basel, Switzerland) · 2022Article
- Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms.Sensors (Basel, Switzerland) · 2022Article
- Comparison of machine learning and deep learning-based methods for locomotion mode recognition using a single inertial measurement unit.Frontiers in neurorobotics · 2022Article
- Human Activity Recognition of Individuals with Lower Limb Amputation in Free-Living Conditions: A Pilot Study.Sensors (Basel, Switzerland) · 2021Article
- A Machine Learning Classification Model for Monitoring the Daily Physical Behaviour of Lower-Limb Amputees.Sensors (Basel, Switzerland) · 2021Article
- Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice.Sensors (Basel, Switzerland) · 2021Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Understanding LSTM Network Behaviour of IMU-Based Locomotion Mode Recognition for Applications in Prostheses and Wearables.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Locomotion assistive devices equipped with a microprocessor can potentially automatically adapt their behavior when the user is transitioning from one locomotion mode to another. Many developments in the field have come from machine learning driven controllers on locomotion assistive devices that recognize/predict the current locomotion mode or the upcoming one. This review synthesizes the machine learning algorithms designed to recognize or to predict a locomotion mode in order to automatically adapt the behavior of a locomotion assistive device. A systematic review was conducted on the Web of Science and MEDLINE databases (as well as in the retrieved papers) to identify articles published between 1 January 2000 to 31 July 2020. This systematic review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and is registered on Prospero (CRD42020149352). Study characteristics, sensors and algorithms used, accuracy and robustness were also summarized. In total, 1343 records were identified and 58 studies were included in this review. The experimental condition which was most often investigated was level ground walking along with stair and ramp ascent/descent activities. The machine learning algorithms implemented in the included studies reached global mean accuracies of around 90%. However, the robustness of those algorithms seems to be more broadly evaluated, notably, in everyday life. We also propose some guidelines for homogenizing future reports.
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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.