ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments

Guffanti, Diego; Brunete, Alberto; Hernando, Miguel; Rueda, Javier; Navarro, Enrique

doi:10.3390/s21206786

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…The angular position of the person in the camera frame (

{}^{{F}_{c}}\theta _{p}

) allows a continuous rotation of the camera to align it with the person. The process followed for tuning both controllers and analyzing their performance is detailed in Guffanti, Brunete, and Hernando (2021) and Guffanti, Brunete, Hernando, Rueda et al (2021).…”

Section: Methodsmentioning

confidence: 99%

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Robotics‐driven gait analysis: Assessing Azure Kinect's performance in in‐lab versus in‐corridor environments

Guffanti,

Brunete,

Hernando

et al. 2024

Journal of Field Robotics

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Gait analysis offers vital insights into human movement, aiding in the diagnosis, treatment, and rehabilitation of various conditions. Analyzing gait in corridors, rather than in lab, provides unique advantages for a more comprehensive understanding of human locomotion. However, limited dedicated technologies constrain gait data analysis in this context. In this study, a markerless gait analysis system using an Azure Kinect sensor mounted on a mobile robot is proposed and validated as a potential solution for gait analysis in corridors. Ten healthy participants (4 males and 6 females) underwent two tests. The first test (5 trials per participant) took place in the laboratory. Here, Azure Kinect performance was validated against a Vicon system, assessing eight gait signals and 22 gait parameters. The second test (2 trials per participant) was performed in the corridors over a 32‐m walking distance to compare this gait pattern with the one developed within the laboratory. The intrasession Intraclass Correlation Coefficient (ICC) reliability for in‐lab experiments was assessed by calculating the ICC between gait cycles captured in each session per participant. Notably, knee flexion/extension (ICC‐0.95), hip flexion/extension (ICC‐0.96), pelvis rotation (ICC‐0.88), and interankle distance (ICC‐0.98) demonstrated excellent reliability with high confidence. Similarly, hip adduction/abduction showed good reliability (ICC‐0.79), while trunk rotation exhibited moderate reliability (ICC‐0.72). In contrast, both trunk tilt (ICC‐0.24) and pelvis tilt (ICC‐0.41) consistently displayed lower reliability. This was observed for both the Vicon and the Azure systems, highlighting the intricate nature of capturing precise data for these specific signals in both systems. Validity outcomes indicated comparable error rates to literature standards ( knee flexion/extension, hip flexion/extension, and hip adduction/abduction), with 11 parameters having no significant differences from Vicon. Comparison of in‐lab and in‐corridor experiments show that individuals exhibit significantly longer stride time (1.10 s vs. 1.05 s), lower pelvis tilt ( vs. ), and lower minimum pelvis rotation ( vs. ) when walking in the laboratory. This study demonstrates promising outcomes in outdoor gait analysis with a robot‐mounted camera, revealing significant distinctions from controlled laboratory evaluations

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“…The angular position of the person in the camera frame (

{}^{{F}_{c}}\theta _{p}

Section: Methodsmentioning

confidence: 99%

“…The process followed for tuning both controllers and analyzing their performance is detailed in and Guffanti, Brunete, Hernando, Rueda et al (2021).…”

Section: Robot Localization and Controlmentioning

confidence: 99%

Robotics‐driven gait analysis: Assessing Azure Kinect's performance in in‐lab versus in‐corridor environments

Guffanti,

Brunete,

Hernando

et al. 2024

Journal of Field Robotics

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“…To generate the walking trajectory, a map (20 m wide × 30 m long) of the environment was constructed, and a trajectory of about 50 m in length was designed. The mapping functions and navigation strategies to implement the walking experiments with the ROBOGait system are described in [ 7 ]. Figure 1 b shows the planned trajectory and an example of the actual trajectories of the robot and a participant during one of the experiments.…”

Section: Methodsmentioning

confidence: 99%

“…However, such a stationary configuration still requires a dedicated space and the certainty of fixed camera positions. To resolve these shortcomings, our robotic system, ROBOGait [ 7 ], which features a 3D camera on a mobile base, is set to move along with an individual while capturing. With such a setup, walking can be analyzed in a non-lab environment, e.g., a hospital corridor, for a large number of steps.…”

Section: Introductionmentioning

confidence: 99%

Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios

Guffanti

Lemus

Brunete

et al. 2023

Sensors

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Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis.

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“…The feasibility of Red Green Blue Depth (RGB-D) cameras in contactless foot tracking and gait evaluation is commonly studied nowadays. While many were developed for general clinical applications [12][13][14][15][16], few literature targets the field of assistive technologies, which have limited view of the subjects.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Foot Tracking and Gait Evaluation with Geometric Modeling

Foo¹,

Chang²,

Ang

2022

Sensors

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Gait evaluation is important in gait rehabilitation and assistance to monitor patient’s balance status and assess recovery performance. Recent technologies leverage on vision-based systems with high portability and low operational complexity. In this paper, we propose a new vision-based foot tracking algorithm specially catering to overground gait assistive devices, which often have limited view of the users. The algorithm models the foot and the shank of the user using simple geometry. Through cost optimization, it then aligns the models to the point cloud, showing the back view of the user’s lower limbs. The system outputs the poses of the feet, which are used to compute the spatial-temporal gait parameters. Seven healthy young subjects are recruited to perform overground and treadmill walking trials. The results of the algorithm are compared with the motion capture system and a third-party gait analysis software. The algorithm has a fitting rotational and translational errors of less than 20 degrees and 33 mm, respectively, for 0.4 m/s walking speed. The gait detection F1 score achieves more than 96.8%. The step length and step width errors are around 35 mm, while the cycle time error is less than 38 ms. The proposed algorithm provides a fast, contactless, portable, and cost-effective gait evaluation method without requiring the user to wear any customized footwear.

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ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments

Cited by 11 publications

References 29 publications

Robotics‐driven gait analysis: Assessing Azure Kinect's performance in in‐lab versus in‐corridor environments

Robotics‐driven gait analysis: Assessing Azure Kinect's performance in in‐lab versus in‐corridor environments

Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios

Real-Time Foot Tracking and Gait Evaluation with Geometric Modeling

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