scholarly journals MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

Micromachines ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 442 ◽  
Author(s):  
Sen Qiu ◽  
Long Liu ◽  
Hongyu Zhao ◽  
Zhelong Wang ◽  
Yongmei Jiang
Keyword(s):  
Gait Analysis ◽  
Inertial Sensors ◽  
Low Cost ◽  
Calibration Method ◽  
Medical Rehabilitation ◽  
Sensor Data ◽  
Body Sensor Networks ◽  
Fusion Algorithm ◽  
Gait Impairments ◽  
Rehabilitation Assessment

Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment.

scholarly journals Development and Evaluation of a Low-Drift Inertial Sensor-Based System for Analysis of Alpine Skiing Performance

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2480
Author(s):  
Isidoro Ruiz-García ◽  
Ismael Navarro-Marchal ◽  
Javier Ocaña-Wilhelmi ◽  
Alberto J. Palma ◽  
Pablo J. Gómez-López ◽  
...  
Keyword(s):  
Data Fusion ◽  
Reference System ◽  
Inertial Sensor ◽  
Low Cost ◽  
Sampling Rate ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Improve Technique ◽  
Worst Case ◽  
Sensor Data Fusion

In skiing it is important to know how the skier accelerates and inclines the skis during the turn to avoid injuries and improve technique. The purpose of this pilot study with three participants was to develop and evaluate a compact, wireless, and low-cost system for detecting the inclination and acceleration of skis in the field based on inertial measurement units (IMU). To that end, a commercial IMU board was placed on each ski behind the skier boot. With the use of an attitude and heading reference system algorithm included in the sensor board, the orientation and attitude data of the skis were obtained (roll, pitch, and yaw) by IMU sensor data fusion. Results demonstrate that the proposed IMU-based system can provide reliable low-drifted data up to 11 min of continuous usage in the worst case. Inertial angle data from the IMU-based system were compared with the data collected by a video-based 3D-kinematic reference system to evaluate its operation in terms of data correlation and system performance. Correlation coefficients between 0.889 (roll) and 0.991 (yaw) were obtained. Mean biases from −1.13° (roll) to 0.44° (yaw) and 95% limits of agreements from 2.87° (yaw) to 6.27° (roll) were calculated for the 1-min trials. Although low mean biases were achieved, some limitations arose in the system precision for pitch and roll estimations that could be due to the low sampling rate allowed by the sensor data fusion algorithm and the initial zeroing of the gyroscope.

Sensor Data Fusion Algorithm for Indoor Environment Mapping Using Low-Cost Sensors

2013 ◽  
Vol 24 (3) ◽  
pp. 199-211 ◽  
Author(s):  
Luciano Buonocore ◽  
Cairo Lúcio Nascimento Júnior ◽  
Areolino de Almeida Neto
Keyword(s):  
Data Fusion ◽  
Indoor Environment ◽  
Low Cost ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Sensor Data Fusion

scholarly journals A Robust EMD-Based RVFL Network Fusion Algorithm for Low-Cost GPS/INS Integrated System

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Da Liu ◽  
Shufang Zhang ◽  
Jingbo Zhang
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Integrated System ◽  
Fusion Algorithm ◽  
Functional Link ◽  
Mode Decomposition ◽  
Error Accumulation ◽  
Lower Complexity ◽  
High Level

Global positioning system (GPS) and inertial navigation system (INS) are commonly combined to overcome disadvantages of each and constitute an integrated system that realizes long-term precision. However, the performance of the integrated system deteriorates on which GPS is unavailable. Especially when low-cost inertial sensors based on the microelectromechanical system (MEMS) are used, performance of the integrated system degrades severely over time. In this study, in order to minimize the adverse impact of high-level stochastic noise from low-cost MEMS sensors, denoising technology based on empirical mode decomposition (EMD) is employed to improve signal quality before navigation solution by which significant improvement of removing noise is achieved. Moreover, a random vector functional link (RVFL) network-based fusion algorithm is presented to estimate and compensate position error during GPS outage such that error accumulation is suppressed quickly when INS is working standalone. Performance of the proposed approach is evaluated by experimental results. It is indicated from comparison that the proposed algorithm takes advantages such as better accuracy and lower complexity and is more robust than the commonly reported methods and is more appropriate for real-time and low-cost application.

scholarly journals Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

2019 ◽  
Vol 8 (2S6) ◽  
pp. 357-361
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Initial Position ◽  
Mobile User ◽  
Sensor Data ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Positioning Accuracy

This paper proposed thehybridindoor positioning system in smartphone for positioning accuracy by fusion of wireless-fidelity (Wi-Fi) signals and inertial sensors from pedestrian dead reckoning (PDR) in smartphone. The proposed system uses Wi-Fi as the source of received signal strength indicator (RSSI) for fingerprint and smartphones sensor data from PDR. RSSI signals are used to determine the initial position and reduce error accumulation of PDR while smartphone sensor data are used to estimate user trajectory. Extended Kalman Filter (EKF) is the fusion algorithm used for its similarity with Kalman Filter (KF) but with advantages of processing non-linear progressions. An estimated 49 steps were detected which is identical to the 50 steps taken in the experiment while showing a trajectory similar to the actual route taken by the mobile user. A benefit of using built-in smartphone sensors is its cost-effectiveness and availability that does not require additional hardware. In addition, a nonlinear EKF is used to enhance the positioning accuracy in the proposed system. Further studies will be made in the potential of indoor positioning algorithm including the effect of noise interference on sensors and RSSI and the accumulated errors resulting from walking

scholarly journals Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3940
Author(s):  
Vânia Guimarães ◽  
Inês Sousa ◽  
Miguel Velhote Correia
Keyword(s):  
Gait Analysis ◽  
Relative Error ◽  
Clinical Decision Making ◽  
Inertial Sensors ◽  
Clinical Decision ◽  
The Body ◽  
Sensor Data ◽  
Sensor Orientation ◽  
Optical Motion ◽  
Spatio Temporal

Inertial sensors can potentially assist clinical decision making in gait-related disorders. Methods for objective spatio-temporal gait analysis usually assume the careful alignment of the sensors on the body, so that sensor data can be evaluated using the body coordinate system. Some studies infer sensor orientation by exploring the cyclic characteristics of walking. In addition to being unrealistic to assume that the sensor can be aligned perfectly with the body, the robustness of gait analysis with respect to differences in sensor orientation has not yet been investigated—potentially hindering use in clinical settings. To address this gap in the literature, we introduce an orientation-invariant gait analysis approach and propose a method to quantitatively assess robustness to changes in sensor orientation. We validate our results in a group of young adults, using an optical motion capture system as reference. Overall, good agreement between systems is achieved considering an extensive set of gait metrics. Gait speed is evaluated with a relative error of −3.1±9.2 cm/s, but precision improves when turning strides are excluded from the analysis, resulting in a relative error of −3.4±6.9 cm/s. We demonstrate the invariance of our approach by simulating rotations of the sensor on the foot.

Navigation Sensor Data Fusion for the AUV Remus

2001 ◽  
Vol 38 (01) ◽  
pp. 65-69
Author(s):  
Thomas F. Fulton ◽  
Christopher J. Cassidy
Keyword(s):  
Data Fusion ◽  
Autonomous Underwater Vehicle ◽  
Low Cost ◽  
Field Trials ◽  
Sensor Data ◽  
Doppler Velocity ◽  
Fusion Algorithm ◽  
Sensor Data Fusion ◽  
Acoustic Navigation ◽  
Using Data

The development of a navigation sensor data fusion algorithm for the autonomous underwater vehicle (AUV) Remus is described. Remus is a small, low-cost AUV designed and built at the Ocean Systems Laboratory of the Woods Hole Oceanographic Institute. The navigation sensors for Remus include an acoustic navigation system, a Doppler velocity sonar, and a compass. The data from these sensors are integrated in an extended Kalman filter, with the objective of producing a more accurate vehicle track. Postprocessing results using data from two recent field trials are presented.

A SENSOR DATA FUSION ALGORITHM FOR INDOOR SLAM USING A LOW-COST MOBILE ROBOT

2012 ◽  
pp. 762-769
Author(s):  
LUCIANO BUONOCORE ◽  
AEROLINO DE ALMEIDA NETO ◽  
CAIRO LÚCIO NASCIMENTO JÚNIOR
Keyword(s):  
Data Fusion ◽  
Mobile Robot ◽  
Low Cost ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Sensor Data Fusion

A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

2008 ◽  
Vol 61 (2) ◽  
pp. 323-336 ◽  
Author(s):  
P. Aggarwal ◽  
Z. Syed ◽  
X. Niu ◽  
N. El-Sheimy
Keyword(s):  
Moving Objects ◽  
Inertial Sensors ◽  
Low Cost ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Mems Sensors ◽  
Calibration Methods

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

A Satellite and Inertial Navigation Solution in Crises Management Operation for First Responder Application

2016 ◽  
Vol 101 ◽  
pp. 158-167
Author(s):  
Antonio Ghetti ◽  
Luca Vittuari ◽  
Matteo Zanzi
Keyword(s):  
Inertial Sensors ◽  
Dead Reckoning ◽  
Human Motion ◽  
Sensor Data ◽  
Reference Trajectory ◽  
Fusion Algorithm ◽  
Definition Of ◽  
Field Test Result ◽  
Gnss Data ◽  
Crises Management

This work describes the activities and the results achieved within the SPARTACUS project with regards to the development of a pedestrian positioning system for first responders based on inertial sensors and GNSS data integration. In particular, three steps have been dealt with. At first the analysis of a typical human motion profile, highlighting the Zero Velocity Update constraints; secondly, the definition of a high accurate reference trajectory for test validation purposes, with both indoor and outdoor tracks. Finally, the tuning of Extended Kalman Filter parameters for the calibration of the best sensor data fusion algorithm mainly focused to dead reckoning positioning and field test result assessment.

scholarly journals A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7517
Author(s):  
Vânia Guimarães ◽  
Inês Sousa ◽  
Miguel Velhote Correia
Keyword(s):  
Gait Analysis ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Coordinate Frame ◽  
Complete Evaluation ◽  
Gait Parameters ◽  
Deep Recurrent Neural Network ◽  
Optical Motion ◽  
Frame Transformation

Gait performance is an important marker of motor and cognitive decline in older adults. An instrumented gait analysis resorting to inertial sensors allows the complete evaluation of spatiotemporal gait parameters, offering an alternative to laboratory-based assessments. To estimate gait parameters, foot trajectories are typically obtained by integrating acceleration two times. However, to deal with cumulative integration errors, additional error handling strategies are required. In this study, we propose an alternative approach based on a deep recurrent neural network to estimate heel and toe trajectories. We propose a coordinate frame transformation for stride trajectories that eliminates the dependency from previous strides and external inputs. Predicted trajectories are used to estimate an extensive set of spatiotemporal gait parameters. We evaluate the results in a dataset comprising foot-worn inertial sensor data acquired from a group of young adults, using an optical motion capture system as a reference. Heel and toe trajectories are predicted with low errors, in line with reference trajectories. A good agreement is also achieved between the reference and estimated gait parameters, in particular when turning strides are excluded from the analysis. The performance of the method is shown to be robust to imperfect sensor-foot alignment conditions.

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