ArticleSensors (Basel, Switzerland)2021
Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- Human-machine Interface using functional electrostimulation and inertial sensors for lower limb rehabilitation in spinal cord injury individuals: a proof of concept.Medical & biological engineering & computing · 2026Article
- Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury.Scientific reports · 2025Article
- Real-Time Estimation of Lower Limb Posture and Joint Angles Using Wearable IMUs: Reproduced Hemiparetic and Normal Gait.Japanese journal of comprehensive rehabilitation science · 2025Article
- IoT-Based Wireless System for Gait Kinetics Monitoring in Multi-Device Therapeutic Interventions.Sensors (Basel, Switzerland) · 2024Article
- Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions.Sensors (Basel, Switzerland) · 2022Article
- Development of an IoT Electrostimulator with Closed-Loop Control.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
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Abstract
The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.
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