scholarly journals Detection of Ground Contact Times with Inertial Sensors in Elite 100-m Sprints under Competitive Field Conditions

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7331
Author(s):  
Patrick Blauberger ◽  
Alexander Horsch ◽  
Martin Lames
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Ground Contact ◽  
Ground Truth Data ◽  
Electric Measurement ◽  
Elite Level ◽  
Sensor Signals ◽  
Measurement Units ◽  
Ground Contact Time

This study describes a method for extracting the stride parameter ground contact time (GCT) from inertial sensor signals in sprinting. Five elite athletes were equipped with inertial measurement units (IMU) on their ankles and performed 34 maximum 50 and 100-m sprints. The GCT of each step was estimated based on features of the recorded IMU signals. Additionally, a photo-electric measurement system covered a 50-m corridor of the track to generate ground truth data. This corridor was placed interchangeably at the first and the last 50-ms of the track. In total, 863 of 889 steps (97.08%) were detected correctly. On average, ground truth data were underestimated by 3.55 ms. The root mean square error of GCT was 7.97 ms. Error analyses showed that GCT at the beginning and the end of the sprint was classified with smaller errors. For single runs the visualization of step-by-step GCT was demonstrated as a new diagnostic instrument for sprint running. The results show the high potential of IMUs to provide the temporal parameter GCT for elite-level athletes.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

scholarly journals FES-Induced Cycling in Complete SCI: A Simpler Control Method Based on Inertial Sensors

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4268
Author(s):  
Benoît Sijobert ◽  
Ronan Le Guillou ◽  
Charles Fattal ◽  
Christine Azevedo Coste
Keyword(s):  
Electrical Stimulation ◽  
Control Method ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Setting Time ◽  
Reference Information ◽  
Novel Approach ◽  
Crank Angle ◽  
Measurement Units ◽  
User Friendly

This article introduces a novel approach for a functional electrical stimulation (FES) controller intended for FES-induced cycling based on inertial measurement units (IMUs). This study aims at simplifying the design of electrical stimulation timing patterns while providing a method that can be adapted to different users and devices. In most of studies and commercial devices, the crank angle is used as an input to trigger stimulation onset. We propose instead to use thigh inclination as the reference information to build stimulation timing patterns. The tilting angles of both thighs are estimated from one inertial sensor located above each knee. An IF–THEN rule algorithm detects, online and automatically, the thigh peak angles in order to start and stop the stimulation of quadriceps muscles, depending on these events. One participant with complete paraplegia was included and was able to propel a recumbent trike using the proposed approach after a very short setting time. This new modality opens the way for a simpler and user-friendly method to automatically design FES-induced cycling stimulation patterns, adapted to clinical use, for multiple bike geometries and user morphologies.

scholarly journals Locomotor Adaptations During RaceRunning in People With Neurological Motor Disorders

2019 ◽  
pp. 325-338
Author(s):  
Mohsen Shafizadeh ◽  
Nicola Theis ◽  
Keith Davids
Keyword(s):  
Stance Phase ◽  
Motor Disorders ◽  
Ground Contact ◽  
Inertial Measurement ◽  
Impact Peak ◽  
Measurement Units ◽  
Ground Contact Time ◽  
Active Peak ◽  
Locomotor Adaptations ◽  
Impact Shock

The aim of this study was to examine strategies to absorb impact shock during RaceRunning in participants with neurological motor disorders. For this purpose, 8 RaceRunning athletes (4 male and 4 female) voluntarily took part in the study. Each participant performed a series of 100-m sprints with a RaceRunning bike. Acceleration of the tibia and head was measured with 2 inertial measurement units and used to calculate foot-impact shock measures. Results showed that RaceRunning pattern was characterized by a lack of impact peak in foot–ground contact time and the existence of an active peak after foot contact. Due to the ergonomic properties of the RaceRunning bike, shock is attenuated throughout the stance phase. In conclusion, the results revealed that RaceRunning athletes with neurological motor disorders are capable of absorbing impact shock during assisted RaceRunning using a strategy that mimics runners without disabilities.

scholarly journals Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach

2016 ◽  
Vol 2 (1) ◽  
pp. 715-718 ◽  
Author(s):  
David Graurock ◽  
Thomas Schauer ◽  
Thomas Seel
Keyword(s):  
Sensor Networks ◽  
Lower Limb ◽  
Healthy Subjects ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Lower Limbs ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Correct Pairing ◽  
Measurement Units

AbstractInertial sensor networks enable realtime gait analysis for a multitude of applications. The usability of inertial measurement units (IMUs), however, is limited by several restrictions, e.g. a fixed and known sensor placement. To enhance the usability of inertial sensor networks in every-day live, we propose a method that automatically determines which sensor is attached to which segment of the lower limbs. The presented method exhibits a low computational workload, and it uses only the raw IMU data of 3 s of walking. Analyzing data from over 500 trials with healthy subjects and Parkinson’s patients yields a correct-pairing success rate of 99.8% after 3 s and 100% after 5 s.

scholarly journals RIANN—A Robust Neural Network Outperforms Attitude Estimation Filters

AI ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 444-463
Author(s):  
Daniel Weber ◽  
Clemens Gühmann ◽  
Thomas Seel
Keyword(s):  
Neural Network ◽  
Motion Tracking ◽  
Method Development ◽  
State Of The Art ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Attitude Estimation ◽  
Human Motion ◽  
Ground Truth Data ◽  
The Individual

Inertial-sensor-based attitude estimation is a crucial technology in various applications, from human motion tracking to autonomous aerial and ground vehicles. Application scenarios differ in characteristics of the performed motion, presence of disturbances, and environmental conditions. Since state-of-the-art attitude estimators do not generalize well over these characteristics, their parameters must be tuned for the individual motion characteristics and circumstances. We propose RIANN, a ready-to-use, neural network-based, parameter-free, real-time-capable inertial attitude estimator, which generalizes well across different motion dynamics, environments, and sampling rates, without the need for application-specific adaptations. We gather six publicly available datasets of which we exploit two datasets for the method development and the training, and we use four datasets for evaluation of the trained estimator in three different test scenarios with varying practical relevance. Results show that RIANN outperforms state-of-the-art attitude estimation filters in the sense that it generalizes much better across a variety of motions and conditions in different applications, with different sensor hardware and different sampling frequencies. This is true even if the filters are tuned on each individual test dataset, whereas RIANN was trained on completely separate data and has never seen any of these test datasets. RIANN can be applied directly without adaptations or training and is therefore expected to enable plug-and-play solutions in numerous applications, especially when accuracy is crucial but no ground-truth data is available for tuning or when motion and disturbance characteristics are uncertain. We made RIANN publicly available.

scholarly journals Deep Sensing: Inertial and Ambient Sensing for Activity Context Recognition Using Deep Convolutional Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3803 ◽  
Author(s):  
Abayomi Otebolaku ◽  
Timibloudi Enamamu ◽  
Ali Alfoudi ◽  
Augustine Ikpehai ◽  
Jims Marchang ◽  
...  
Keyword(s):  
Time Series ◽  
Mobile Devices ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Deep Convolutional Neural Networks ◽  
Class Imbalance Problem ◽  
Context Recognition ◽  
Embedded Sensing ◽  
Sensor Signals ◽  
Ambient Sensing

With the widespread use of embedded sensing capabilities of mobile devices, there has been unprecedented development of context-aware solutions. This allows the proliferation of various intelligent applications, such as those for remote health and lifestyle monitoring, intelligent personalized services, etc. However, activity context recognition based on multivariate time series signals obtained from mobile devices in unconstrained conditions is naturally prone to imbalance class problems. This means that recognition models tend to predict classes with the majority number of samples whilst ignoring classes with the least number of samples, resulting in poor generalization. To address this problem, we propose augmentation of the time series signals from inertial sensors with signals from ambient sensing to train Deep Convolutional Neural Network (DCNNs) models. DCNNs provide the characteristics that capture local dependency and scale invariance of these combined sensor signals. Consequently, we developed a DCNN model using only inertial sensor signals and then developed another model that combined signals from both inertial and ambient sensors aiming to investigate the class imbalance problem by improving the performance of the recognition model. Evaluation and analysis of the proposed system using data with imbalanced classes show that the system achieved better recognition accuracy when data from inertial sensors are combined with those from ambient sensors, such as environmental noise level and illumination, with an overall improvement of 5.3% accuracy.

scholarly journals Centre of Pressure Estimation during Walking Using Only Inertial-Measurement Units and End-To-End Statistical Modelling

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6136
Author(s):  
Janez Podobnik ◽  
David Kraljić ◽  
Matjaž Zadravec ◽  
Marko Munih
Keyword(s):  
Model Performance ◽  
Ground Truth ◽  
Statistical Modelling ◽  
Inertial Measurement Units ◽  
Centre Of Pressure ◽  
Ground Truth Data ◽  
Inertial Measurement ◽  
Gait Characteristic ◽  
Measurement Units ◽  
Rms Error

Estimation of the centre of pressure (COP) is an important part of the gait analysis, for example, when evaluating the functional capacity of individuals affected by motor impairment. Inertial measurement units (IMUs) and force sensors are commonly used to measure gait characteristic of healthy and impaired subjects. We present a methodology for estimating the COP solely from raw gyroscope, accelerometer, and magnetometer data from IMUs using statistical modelling. We demonstrate the viability of the method using an example of two models: a linear model and a non-linear Long-Short-Term Memory (LSTM) neural network model. Models were trained on the COP ground truth data measured using an instrumented treadmill and achieved the average intra-subject root mean square (RMS) error between estimated and ground truth COP of 12.3 mm and the average inter-subject RMS error of 23.7 mm which is comparable or better than similar studies so far. We show that the calibration procedure in the instrumented treadmill can be as short as a couple of minutes without the decrease in our model performance. We also show that the magnetic component of the recorded IMU signal, which is most sensitive to environmental changes, can be safely dropped without a significant decrease in model performance. Finally, we show that the number of IMUs can be reduced to five without deterioration in the model performance.

scholarly journals Automatic Orientation Estimation of Inertial Sensors in C-Arm CT Projections

2019 ◽  
Vol 5 (1) ◽  
pp. 195-198 ◽  
Author(s):  
Mareike Thies ◽  
Jennifer Maier ◽  
Björn Eskofier ◽  
Andreas Maier ◽  
Marc Levenston ◽  
...  
Keyword(s):  
Coordinate System ◽  
Inertial Sensors ◽  
Local Coordinate System ◽  
Three Dimensional ◽  
Ground Truth ◽  
Radial Symmetry ◽  
Sift Algorithm ◽  
Position Data ◽  
Measurement Units ◽  
Three Dimensional Coordinate

AbstractTo obtain CT images of the knee joint in a more lifelike position, data acquisition can be performed with patients in standing rather than in lying position. However, in that situation, people tend to show involuntary motion. One possibility to compensate for that motion is the use of Inertial Measurement Units, that capture the accelerations during the scan. For this purpose, their local coordinate system needs to be known. An estimation based on the SIFT algorithm was implemented and compared to an existing approach that uses the Fast Radial Symmetry transform and to expert labels for evaluation. The SIFT method showed to be superior to the existing approach as it could extract stable feature points from the projections that were used to estimate the three-dimensional coordinate system in a reliable manner. The final algorithm achieved a mean euclidean distance of 2.61 mm between the calculated position of the origin and the assumed ground truth by the expert labels.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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