ArticleSensors (Basel, Switzerland)2021
A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 32 citations in OpenAlex.
- Wearable Sensor Technologies and Gait Analysis for Early Detection of Dementia: Trends and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning.BMC medical informatics and decision making · 2025Article
- Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques.Sensors (Basel, Switzerland) · 2024Review
- Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking.IEEE open journal of engineering in medicine and biology · 2024Article
- A Deep Learning Approach for Biped Robot Locomotion Interface Using a Single Inertial Sensor.Sensors (Basel, Switzerland) · 2023Article
- Minding your steps: a cross-sectional pilot study using foot-worn inertial sensors and dual-task gait analysis to assess the cognitive status of older adults with mobility limitations.BMC geriatrics · 2023Article
- A New Quantitative Gait Analysis Method Based on Oscillatory Mechanical Energies Measured near Body Center of Mass.Sensors (Basel, Switzerland) · 2022Article
- Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices.Computational intelligence and neuroscience · 2022Article
- The placement of foot-mounted IMU sensors does affect the accuracy of spatial parameters during regular walking.PloS one · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions.
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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.