scholarly journals Continuous Fusion of Motion Data Using an Axis-Angle Rotation Representation with Uniform B-Spline

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5004
Author(s):  
Haohao Hu ◽  
Johannes Beck ◽  
Martin Lauer ◽  
Christoph Stiller
Keyword(s):  
Forward Direction ◽  
Measurement Unit ◽  
Automated Driving ◽  
Real World Data ◽  
Inertial Measurement ◽  
B Spline ◽  
Motion Data ◽  
Angle Rotation ◽  
Representation Method ◽  
Fusion Approach

The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept.

scholarly journals Development of an Algorithm for Automatically Assessing Lifting Risk Factors Using Inertial Measurement Units

2019 ◽  
Vol 63 (1) ◽  
pp. 1334-1338 ◽  
Author(s):  
Ming-Lun Lu ◽  
Shuo Feng ◽  
Grant Hughes ◽  
Menekse S. Barim ◽  
Marie Hayden ◽  
...  
Keyword(s):  
Risk Factors ◽  
The Body ◽  
Measurement Unit ◽  
Trunk Flexion ◽  
Inertial Measurement ◽  
Motion Data ◽  
Low Back Disorders ◽  
Machine Learning Approach ◽  
Measurement Units ◽  
Forearm Length

The objective of this study was to develop an algorithm for automatically processing data collected with inertial measurement unit (IMU) wearable devices to measure lifting risk factors for low back disorders. Five IMU sensors attached to five body segments were used for developing the algorithm. The algorithm consists of two modules running in parallel for detecting the beginning and ending of a lifting event as well as the vertical height (V) of the object lifted by two hands and the horizontal (H) distance between the object and the body during the lift. The motion synchronization feature of wrists’ motion data were used to train the lifting detection module using a machine learning approach. This module achieved a training accuracy of 85%. In the second module, the forearm length and gyroscope data of four sensors are proposed for calculating trunk flexion angle, V and H during a lift.

scholarly journals IMU sensor-based data glove for finger joint measurement

2020 ◽  
Vol 20 (1) ◽  
pp. 82
Author(s):  
Muhammad Ajwad Wa’ie Hazman ◽  
Ili Najaa Aimi Mohd Nordin ◽  
Faridah Hanim Mohd Noh ◽  
Nurulaqilla Khamis ◽  
M. R. M. Razif ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Inertial Measurement Unit ◽  
Finger Joint ◽  
Measurement Unit ◽  
Joint Measurement ◽  
Data Glove ◽  
Inertial Measurement ◽  
Motion Data ◽  
Distal Interphalangeal ◽  
Reliable Solution

<p>The methods used to quantify finger range of motion significantly influence how hand disability is reported. To date, the accuracy of sensors being utilized in data gloves from the literature has been ascertained yet need further analysis. This paper presents an inertial measurement unit sensor-based data glove for finger joint measurement developed for collecting a range of motion data of distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joints of an index finger. In this study, three inertial measurement sensors, MPU-6050 and two flexible bend sensors which are capable to detect angle displacement were attached to the distal interphalangeal, proximal interphalangeal and metacarpophalangeal finger joint points on the glove. The data taken from inertial measurement unit sensors and flexible bend sensors were acquired using Arduino and MATLAB software interface. The data obtained were compared with the reference data measured from goniometer to allow for accurate comparative measurement. The percentage of error resulted from MPU-6050 sensor unit were ranged from 0.81 % to 5.41 % were very low which indicates high accuracy when compared with the measurements obtained using goniometer. On the other hand, flexible bend sensor shows low accuracy (11.11 % to 19.35 % error). In conclusion, the inertial measurement unit sensor-based data glove using MPU-6050 sensors can be a reliable solution for tracking the progress of finger rehabilitation exercises. In order to motivate patients to adhere to the therapy exercises, interactive rehabilitation game will be developed in the future incorporating  MPU-6050 sensors on all five fingers.</p>

scholarly journals Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2988 ◽  
Author(s):  
Miguel D. Sánchez Sánchez Manchola ◽  
María J. Pinto Pinto Bernal ◽  
Marcela Munera ◽  
Carlos A. Cifuentes
Keyword(s):  
Lower Limb ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Linear Acceleration ◽  
Measurement Unit ◽  
Phase Detection ◽  
Inertial Measurement ◽  
Phase Partitioning ◽  
Motion Data ◽  
Gait Phase

Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0 . 75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

scholarly journals Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2246
Author(s):  
Scott Pardoel ◽  
Gaurav Shalin ◽  
Julie Nantel ◽  
Edward D. Lemaire ◽  
Jonathan Kofman
Keyword(s):  
Early Detection ◽  
Plantar Pressure ◽  
Inertial Measurement Unit ◽  
Binary Classification ◽  
Freezing Of Gait ◽  
Pressure Sensors ◽  
Measurement Unit ◽  
Distribution Data ◽  
Inertial Measurement ◽  
Plantar Pressure Distribution

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

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