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Human and Animal Motion Tracking Using Inertial Sensors II
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Human and Animal Motion Tracking Using Inertial Sensors II

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (22 October 2022) | Viewed by 13717

Special Issue Editor

Prof. Dr. Frederic Marin

E-Mail Website
Guest Editor
UMR CNRS 7338 BioMécanique et BioIngénierie, Université de Technologie de Compiègne (UTC), 60200 Compiègne, France
Interests: motion capture; motion analysis; inertial sensors; biomechanics; osteo-articular modeling; musculoskeletal modeling; physical activities monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Motion of humans or animals is considered as a biomarker of the performance of the neuro-musculoskeletal system. Consequently, it is a relevant method for clinical diagnosis and follow-up, and sports and ergonomics applications. Recent improvements of the technology of inertial sensors combining accelerometers and gyrometers completed by magnetometers, pressure sensors, etc., now allow for new perspectives as far as motion capture and analysis of humans and animals is concerned.

Due to the versatility of inertial sensors, measurement sessions can now easily be conducted outside the laboratory, for example, at the workplace or in field studies. They also allow for sessions of either a very short duration, such as shock and crash situations, but also for sessions lasting several days, as in the case of monitoring of physical activity. Inertial sensors can be used as single sensors or inertial sensors networks allowing to record kinematics or dynamics of either a single anatomical segment, the upper and lower limbs, or even the full body.

This Special Issue would like to display innovative work exploring new hardware and software solutions deriving from inertial sensors related to human or animal motion.

The particular topics of interest include but are not limited to:

  • Sensor calibrations and registrations on anatomical body;
  • Methods to determine anatomical orientations and translations;
  • Management of errors, bias, drift of the inertial sensors;
  • Clinical applications;
  • Ergonomics applications;
  • Sports application;
  • Quantification of physical activity.

Dr. Frederic Marin
Guest Editor

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Published Papers (4 papers)

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Research

15 pages, 2638 KiB  
Article
Three-Dimensional Lower-Limb Kinematics from Accelerometers and Gyroscopes with Simple and Minimal Functional Calibration Tasks: Validation on Asymptomatic Participants
by Lena Carcreff, Gabriel Payen, Gautier Grouvel, Fabien Massé and Stéphane Armand
Sensors 2022, 22(15), 5657; https://doi.org/10.3390/s22155657 - 28 Jul 2022
Cited by 8 | Viewed by 2655
Abstract
The use of inertial measurement units (IMUs) to compute gait outputs, such as the 3D lower-limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the [...] Read more.
The use of inertial measurement units (IMUs) to compute gait outputs, such as the 3D lower-limb kinematics is of huge potential, but no consensus on the procedures and algorithms exists. This study aimed at evaluating the validity of a 7-IMUs system against the optoelectronic system. Ten asymptomatic subjects were included. They wore IMUs on their feet, shanks, thighs and pelvis. The IMUs were embedded in clusters with reflective markers. Reference kinematics was computed from anatomical markers. Gait kinematics was obtained from accelerometer and gyroscope data after sensor orientation estimation and sensor-to-segment (S2S) calibration steps. The S2S calibration steps were also applied to the cluster data. IMU-based and cluster-based kinematics were compared to the reference through root mean square errors (RMSEs), centered RMSEs (after mean removal), correlation coefficients (CCs) and differences in amplitude. The mean RMSE and centered RMSE were, respectively, 7.5° and 4.0° for IMU-kinematics, and 7.9° and 3.8° for cluster-kinematics. Very good CCs were found in the sagittal plane for both IMUs and cluster-based kinematics at the hip, knee and ankle levels (CCs > 0.85). The overall mean amplitude difference was about 7°. These results reflected good accordance in our system with the reference, especially in the sagittal plane, but the presence of offsets requires caution for clinical use. Full article
(This article belongs to the Special Issue Human and Animal Motion Tracking Using Inertial Sensors II)
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15 pages, 792 KiB  
Article
A Deep Learning Approach for Gait Event Detection from a Single Shank-Worn IMU: Validation in Healthy and Neurological Cohorts
by Robbin Romijnders, Elke Warmerdam, Clint Hansen, Gerhard Schmidt and Walter Maetzler
Sensors 2022, 22(10), 3859; https://doi.org/10.3390/s22103859 - 19 May 2022
Cited by 30 | Viewed by 5317
Abstract
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be [...] Read more.
Many algorithms use 3D accelerometer and/or gyroscope data from inertial measurement unit (IMU) sensors to detect gait events (i.e., initial and final foot contact). However, these algorithms often require knowledge about sensor orientation and use empirically derived thresholds. As alignment cannot always be controlled for in ambulatory assessments, methods are needed that require little knowledge on sensor location and orientation, e.g., a convolutional neural network-based deep learning model. Therefore, 157 participants from healthy and neurologically diseased cohorts walked 5 m distances at slow, preferred, and fast walking speed, while data were collected from IMUs on the left and right ankle and shank. Gait events were detected and stride parameters were extracted using a deep learning model and an optoelectronic motion capture (OMC) system for reference. The deep learning model consisted of convolutional layers using dilated convolutions, followed by two independent fully connected layers to predict whether a time step corresponded to the event of initial contact (IC) or final contact (FC), respectively. Results showed a high detection rate for both initial and final contacts across sensor locations (recall ≥92%, precision ≥97%). Time agreement was excellent as witnessed from the median time error (0.005 s) and corresponding inter-quartile range (0.020 s). The extracted stride-specific parameters were in good agreement with parameters derived from the OMC system (maximum mean difference 0.003 s and corresponding maximum limits of agreement (−0.049 s, 0.051 s) for a 95% confidence level). Thus, the deep learning approach was considered a valid approach for detecting gait events and extracting stride-specific parameters with little knowledge on exact IMU location and orientation in conditions with and without walking pathologies due to neurological diseases. Full article
(This article belongs to the Special Issue Human and Animal Motion Tracking Using Inertial Sensors II)
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15 pages, 4217 KiB  
Article
Wearable IMMU-Based Relative Position Estimation between Body Segments via Time-Varying Segment-to-Joint Vectors
by Chang June Lee and Jung Keun Lee
Sensors 2022, 22(6), 2149; https://doi.org/10.3390/s22062149 - 10 Mar 2022
Cited by 6 | Viewed by 2424
Abstract
In biomechanics, estimating the relative position between two body segments using inertial and magnetic measurement units (IMMUs) is important in that it enables the capture of human motion in unconstrained environments. The relative position can be estimated using the segment orientation and segment-to-joint [...] Read more.
In biomechanics, estimating the relative position between two body segments using inertial and magnetic measurement units (IMMUs) is important in that it enables the capture of human motion in unconstrained environments. The relative position can be estimated using the segment orientation and segment-to-joint center (S2J) vectors where the S2J vectors are predetermined as constants under the assumption of rigid body segments. However, human body segments are not rigid bodies because they are easily affected by soft tissue artifacts (STAs). Therefore, the use of the constant S2J vectors is one of the most critical factors for the inaccurate estimation of relative position. To deal with this issue, this paper proposes a method of determining time-varying S2J vectors to reflect the deformation of the S2J vectors and thus to increase the estimation accuracy, in IMMU-based relative position estimation. For the proposed method, first, reference S2J vectors for learning needed to be collected. A regression method derived a function outputting S2J vectors based on specific physical quantities that were highly correlated with the deformation of S2J vectors. Subsequently, time-varying S2J vectors were determined from the derived function. The validation results showed that, in terms of the averaged root mean squared errors of four tests performed by three subjects, the proposed method (15.08 mm) provided a higher estimation accuracy than the conventional method using constant vectors (31.32 mm). This indicates the proposed method may effectively compensate for the effects of STAs and ultimately estimate more accurate relative positions. By providing STA-compensated relative positions between segments, the proposed method applied in a wearable motion tracking system can be useful in rehabilitation or sports sciences. Full article
(This article belongs to the Special Issue Human and Animal Motion Tracking Using Inertial Sensors II)
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12 pages, 2492 KiB  
Article
Stance Phase Detection by Inertial Measurement Unit Placed on the Metacarpus of Horses Trotting on Hard and Soft Straight Lines and Circles
by Chloé Hatrisse, Claire Macaire, Marie Sapone, Camille Hebert, Sandrine Hanne-Poujade, Emeline De Azevedo, Frederic Marin, Pauline Martin and Henry Chateau
Sensors 2022, 22(3), 703; https://doi.org/10.3390/s22030703 - 18 Jan 2022
Cited by 10 | Viewed by 2735
Abstract
The development of on-board technologies has enabled the development of quantification systems to monitor equine locomotion parameters. Their relevance among others relies on their ability to determine specific locomotor events such as foot-on and heel-off events. The objective of this study was to [...] Read more.
The development of on-board technologies has enabled the development of quantification systems to monitor equine locomotion parameters. Their relevance among others relies on their ability to determine specific locomotor events such as foot-on and heel-off events. The objective of this study was to compare the accuracy of different methods for an automatic gait events detection from inertial measurement units (IMUs). IMUs were positioned on the cannon bone, hooves, and withers of seven horses trotting on hard and soft straight lines and circles. Longitudinal acceleration and angular velocity around the latero-medial axis of the cannon bone, and withers dorso-ventral displacement data were identified to tag the foot-on and a heel-off events. The results were compared with a reference method based on hoof-mounted-IMU data. The developed method showed bias less than 1.79%, 1.46%, 3.45% and −1.94% of stride duration, respectively, for forelimb foot-on and heel-off, and for hindlimb foot-on and heel-off detection, compared to our reference method. The results of this study showed that the developed gait-events detection method had a similar accuracy to other methods developed for straight line analysis and extended this validation to other types of exercise (circles) and ground surface (soft surface). Full article
(This article belongs to the Special Issue Human and Animal Motion Tracking Using Inertial Sensors II)
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