scholarly journals A Preliminary Test of Measurement of Joint Angles and Stride Length with Wireless Inertial Sensors for Wearable Gait Evaluation System

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Takashi Watanabe ◽  
Hiroki Saito ◽  
Eri Koike ◽  
Kazuki Nitta
Keyword(s):  
Evaluation System ◽  
Stride Length ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Preliminary Test ◽  
Lower Limbs ◽  
Sensor System ◽  
Wearable Sensor ◽  
Joint Angles ◽  
Optical Motion

The purpose of this study is to develop wearable sensor system for gait evaluation using gyroscopes and accelerometers for application to rehabilitation, healthcare and so on. In this paper, simultaneous measurement of joint angles of lower limbs and stride length was tested with a prototype of wearable sensor system. The system measured the joint angles using the Kalman filter. Signals from the sensor attached on the foot were used in the stride length estimation detecting foot movement automatically. Joint angles of the lower limbs were measured with stable and reasonable accuracy compared to those values measured with optical motion measurement system with healthy subjects. It was expected that the stride length measurement with the wearable sensor system would be practical by realizing more stable measurement accuracy. Sensor attachment position was suggested not to affect significantly measurement of slow and normal speed movements in a test with the rigid body model. Joint angle patterns measured in 10 m walking with a healthy subject were similar to common patterns. High correlation between joint angles at some characteristic points and stride velocity were also found adequately. These results suggested that the wireless wearable inertial sensor system could detect characteristics of gait.

scholarly journals Comparative Analysis of Inertial Sensor to Optical Motion Capture System Performance in Push-Pull Exertion Postures

2016 ◽  
Vol 60 (1) ◽  
pp. 970-974 ◽  
Author(s):  
Sol Lim ◽  
Andrea Case ◽  
Clive D’Souza
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Interactive Effects ◽  
Flexion Angle ◽  
Task Demand ◽  
Optical Motion ◽  
Physical Task ◽  
Target Force ◽  
Optical Motion Capture

This study examined interactions between inertial sensor (IS) performance and physical task demand on posture kinematics in a two-handed force exertion task. Fifteen male individuals participated in a laboratory experiment that involved exerting a two-handed isometric horizontal force on an instrumented height-adjustable handle. Physical task demand was operationalized by manipulating vertical handle height, target force magnitude, and force direction. These factors were hypothesized to influence average estimates of torso flexion angle measured using inertial sensors and an optical motion capture (MC) system, as well as the root mean squared errors (RMSE) between instrumentation computed over a 3s interval of the force exertion task. Results indicate that lower handle heights and higher target force levels were associated with increased torso and pelvic flexion in both, push and pull exertions. Torso flexion angle estimates obtained from IS and MC did not differ significantly. However, RMSE increased with target force intensity suggesting potential interactive effects between measurement error and physical task demand.

scholarly journals Validation of Novel Relative Orientation and Inertial Sensor-to-Segment Alignment Algorithms for Estimating 3D Hip Joint Angles

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5143 ◽  
Author(s):  
Lukas Adamowicz ◽  
Reed Gurchiek ◽  
Jonathan Ferri ◽  
Anna Ursiny ◽  
Niccolo Fiorentino ◽  
...  
Keyword(s):  
Range Of Motion ◽  
Hip Joint ◽  
Inertial Sensor ◽  
Estimation Error ◽  
Relative Orientation ◽  
Joint Angles ◽  
Alignment Algorithms ◽  
Sensor Orientation ◽  
Flexion Extension ◽  
Optical Motion

Wearable sensor-based algorithms for estimating joint angles have seen great improvements in recent years. While the knee joint has garnered most of the attention in this area, algorithms for estimating hip joint angles are less available. Herein, we propose and validate a novel algorithm for this purpose with innovations in sensor-to-sensor orientation and sensor-to-segment alignment. The proposed approach is robust to sensor placement and does not require specific calibration motions. The accuracy of the proposed approach is established relative to optical motion capture and compared to existing methods for estimating relative orientation, hip joint angles, and range of motion (ROM) during a task designed to exercise the full hip range of motion (ROM) and fast walking using root mean square error (RMSE) and regression analysis. The RMSE of the proposed approach was less than that for existing methods when estimating sensor orientation ( 12 . 32 ∘ and 11 . 82 ∘ vs. 24 . 61 ∘ and 23 . 76 ∘ ) and flexion/extension joint angles ( 7 . 88 ∘ and 8 . 62 ∘ vs. 14 . 14 ∘ and 15 . 64 ∘ ). Also, ROM estimation error was less than 2 . 2 ∘ during the walking trial using the proposed method. These results suggest the proposed approach presents an improvement to existing methods and provides a promising technique for remote monitoring of hip joint angles.

scholarly journals Inertial Sensor-Based Motion Analysis of Lower Limbs for Rehabilitation Treatments

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Tongyang Sun ◽  
Hua Li ◽  
Quanquan Liu ◽  
Lihong Duan ◽  
Meng Li ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Quantitative Evaluation ◽  
Degrees Of Freedom ◽  
Subjective Experience ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Lower Limbs ◽  
Analysis Method ◽  
Practical Application ◽  
Input Signals

The hemiplegic rehabilitation state diagnosing performed by therapists can be biased due to their subjective experience, which may deteriorate the rehabilitation effect. In order to improve this situation, a quantitative evaluation is proposed. Though many motion analysis systems are available, they are too complicated for practical application by therapists. In this paper, a method for detecting the motion of human lower limbs including all degrees of freedom (DOFs) via the inertial sensors is proposed, which permits analyzing the patient’s motion ability. This method is applicable to arbitrary walking directions and tracks of persons under study, and its results are unbiased, as compared to therapist qualitative estimations. Using the simplified mathematical model of a human body, the rotation angles for each lower limb joint are calculated from the input signals acquired by the inertial sensors. Finally, the rotation angle versus joint displacement curves are constructed, and the estimated values of joint motion angle and motion ability are obtained. The experimental verification of the proposed motion detection and analysis method was performed, which proved that it can efficiently detect the differences between motion behaviors of disabled and healthy persons and provide a reliable quantitative evaluation of the rehabilitation state.

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 A Space Inertial Sensor Ground Evaluation System for Non-Sensitive Axis Based on Torsion Pendulum

2020 ◽  
Vol 10 (9) ◽  
pp. 3090
Author(s):  
Shaoxin Wang ◽  
Liheng Chen ◽  
Yukun Wang ◽  
Zhenping Zhou ◽  
Keqi Qi ◽  
...  
Keyword(s):  
Seismic Noise ◽  
Evaluation System ◽  
Inertial Sensors ◽  
Comprehensive Evaluation ◽  
Inertial Sensor ◽  
Performance Testing ◽  
Torsion Pendulum ◽  
Measurement Resolution ◽  
Resolution Level ◽  
Sensitive Axis

The inertial sensor is the key measurement payload of the technology verification satellite of China’s space gravitational wave detection mission-Taiji Project, which uses capacitive sensors to sense the acceleration disturbance of the test mass under the influence of non-conservative forces in the frequency range of 10 mHz~1 Hz. It is necessary to perform a ground performance evaluation and estimate the working state of the payload in orbit. However, due to the influence of the earth’s gravity and seismic noise, it is impossible to directly evaluate the resolution level of the non-sensitive axis when testing with high-voltage levitation, which leads to incomplete evaluation of the performance of the inertial sensor. In order to implement this part of the test, the sensitive structure is designed and a torsion pendulum facility for performance testing is developed. The experimental results show that the measurement resolution of the non-sensitive axis of the inertial sensor can reach 9.5 × 10−7 m/s2/Hz1/2 under the existing ground environmental conditions and is mainly influenced by the seismic noise during the system measurement. If the inertial sensor enters orbit, the measurement resolution can achieve 3.96 × 10−9 m/s2/Hz1/2, which meets the requirements of the technology verification satellite for a non-sensitive axis. This proposed system also provides a reasonable method for the comprehensive evaluation of inertial sensors in the future.

scholarly journals Noninvasive Estimation of Joint Moments with Inertial Sensor System for Analysis of STS Rehabilitation Training

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Kun Liu ◽  
Jianchao Yan ◽  
Yong Liu ◽  
Ming Ye
Keyword(s):  
Lower Limb ◽  
Inertial Sensor ◽  
Angular Acceleration ◽  
Biomechanical Model ◽  
Sensor System ◽  
Wearable Sensor ◽  
Joint Moments ◽  
Camera System ◽  
Rehabilitation Training ◽  
Noninvasive Estimation

An original approach for noninvasive estimation of lower limb joint moments for analysis of STS rehabilitation training with only inertial measurement units was presented based on a piecewise three-segment STS biomechanical model and a double-sensor difference based algorithm. Joint kinematic and kinetic analysis using a customized wearable sensor system composed of accelerometers and gyroscopes were presented and evaluated compared with a referenced camera system by five healthy subjects and five patients in rehabilitation. Since there is no integration of angular acceleration or angular velocity, the result is not distorted without offset and drift. Besides, since there are no physical sensors implanted in the lower limb joints based on the algorithm, it is feasible to noninvasively analyze STS kinematics and kinetics with less numbers and types of inertial sensors than those mentioned in other methods. Compared with the results from the reference system, the developed wearable sensor system is available to do spatiotemporal analysis of STS task with fewer sensors and high degree of accuracy, to apply guidance and reference for rehabilitation training or desired feedback for the control of powered exoskeleton system.

scholarly journals Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3092 ◽  
Author(s):  
Ke Lu ◽  
Liyun Yang ◽  
Fernando Seoane ◽  
Farhad Abtahi ◽  
Mikael Forsman ◽  
...  
Keyword(s):  
Heart Rate ◽  
Energy Expenditure ◽  
Absolute Error ◽  
Lower Limbs ◽  
Sensor System ◽  
Motion Information ◽  
Wearable Sensor ◽  
New Model ◽  
Impedance Pneumography ◽  
Work Tasks

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21–65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R2 = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R2 = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R2 = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications.

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