ArticleScientific data2023
A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- The variable stiffness orthosis: customizable mechanics for assistance and rehabilitation.Journal of neuroengineering and rehabilitation · 2026Article
- Dataset for rapid estimation of femoral neck loading during gait and dynamic exercises.Scientific data · 2026Article
- Sagittal-Plane Knee Flexion Moment Estimation Using a Lightweight Deep Learning Framework Based on Sequential Surface EMG Feature Frames.Sensors (Basel, Switzerland) · 2026Article
- Efficient and Dynamically Consistent Joint Torque Estimation for Wearable Neurotechnology via Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2026Article
- A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors.Scientific data · 2026Article
- Exploring Variable Achilles Tendon Loading Via Active Acoustics.IEEE sensors journal · 2026Article
- Simplifying rehabilitation control of lower-limb exoskeletons in five ambulation modes via dataset-driven state-machine calibration.Journal of neuroengineering and rehabilitation · 2026Article
- A Full-Body Motion Capture Gait Dataset of Healthy Young Adults Walking Ramps Up and Down.Scientific data · 2026Article
- Is EMG Information Necessary for Deep Learning Estimation of Joint and Muscle Level States?IEEE transactions on bio-medical engineering · 2026Article
- Wesam Al Attar singularity evaluation-infinity: a predictive simulation framework for motor intent collapse in athletes.Frontiers in sports and active living · 2026Article
- Continuous Lower Limb Biomechanics Prediction via Prior-Informed Lightweight Marker-GMformer.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- GAITEX: Human motion dataset of impaired gait and rehabilitation exercises using inertial and optical sensors.Scientific data · 2025Article
- Whole-body optical marker and ground reaction force data of healthy humans performing non-cyclic activities.Scientific data · 2025Article
- Dataset of walking and running biomechanics with different step widths across different speeds.Scientific data · 2025Article
- A Knee Rehabilitation Exercises Dataset for Postural Assessment using Wearable Devices.Scientific data · 2025Article
- Learning based lower limb joint kinematic estimation using open source IMU data.Scientific reports · 2025Article
- Optimizing the Mechanics of a Variable-Stiffness Orthosis with Energy Recycling to Mitigate Foot Drop.IEEE transactions on medical robotics and bionics · 2025Article
- Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- Multi-Planar Cervical Motion Dataset: IMU Measurements and Goniometer.Scientific data · 2025Article
- Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach.PeerJ. Computer science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task. To capture a more holistic picture of lower limb movements in everyday life, we collected data from 12 participants performing 20 non-cyclic activities (e.g. sit-to-stand, jumping, squatting, lunging, cutting) as well as 11 cyclic activities (e.g. walking, running) while kinematics (motion capture and IMUs), kinetics (force plates), and electromyography (EMG) were collected. This dataset provides normative biomechanics for a highly diverse range of activities and common tasks from a consistent set of participants and sensors.
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.