Using A Soft Conformable Foot Sensor to Measure Changes in Foot Strike Angle During Running

The potential association between running foot strike analysis and performance and injury metrics has created the need for reliable methods to quantify foot strike pattern outside the laboratory. Small, wireless inertial measurement units (IMUs) allow for unrestricted movement of the participants. Current IMU methods to measure foot strike pattern places small, rigid accelerometers and/or gyroscopes on the heel cap or on the instep of the shoe. The purpose of this study was to validate a thin, conformable IMU sensor placed directly on the dorsal foot surface to determine foot strike angles and pattern. Participants (n = 12) ran on a treadmill with different foot strike patterns while videography and sensor data were captured. Sensor measures were compared against traditional 2D video analysis techniques and the results showed that the sensor was able to accurately (92.2% success) distinguish between rearfoot and non-rearfoot foot strikes using an angular velocity cut-off value of 0°/s. There was also a strong and significant correlation between sensor determined foot strike angle and foot strike angle determined from videography analysis (r = 0.868, p < 0.001), although linear regression analysis showed that the sensor underestimated the foot strike angle. Conformable sensors with the ability to attach directly to the human skin could improve the tracking of human dynamics and should be further explored.

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Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units

Biosensors ◽

10.3390/bios10090109 ◽

2020 ◽

Vol 10 (9) ◽

pp. 109

Author(s):

Binbin Su ◽

Christian Smith ◽

Elena Gutierrez Farewik

Keyword(s):

Wearable Sensors ◽

Sensor Data ◽

Measurement Unit ◽

Deep Convolutional Neural Networks ◽

Inertial Measurement ◽

Measurement Units ◽

Gait Phase ◽

Robotic Devices ◽

Phase Recognition ◽

Five Phases

Gait phase recognition is of great importance in the development of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, the user’s current gait phase must first be identified accurately. Gait phase recognition can potentially be achieved through input from wearable sensors. Deep convolutional neural networks (DCNN) is a machine learning approach that is widely used in image recognition. User kinematics, measured from inertial measurement unit (IMU) output, can be considered as an ‘image’ since it exhibits some local ‘spatial’ pattern when the sensor data is arranged in sequence. We propose a specialized DCNN to distinguish five phases in a gait cycle, based on IMU data and classified with foot switch information. The DCNN showed approximately 97% accuracy during an offline evaluation of gait phase recognition. Accuracy was highest in the swing phase and lowest in terminal stance.

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Thermal Calibration Procedure and Thermal Characterisation of Low-cost Inertial Measurement Units

Journal of Navigation ◽

10.1017/s0373463315000600 ◽

2015 ◽

Vol 69 (2) ◽

pp. 373-390 ◽

Cited By ~ 8

Author(s):

Qingjiang Wang ◽

You Li ◽

Xiaoji Niu

Keyword(s):

Test Procedure ◽

Thermal Characteristics ◽

Sensor Data ◽

Navigation Systems ◽

Inertial Measurement Units ◽

Thermal Models ◽

Inertial Measurement ◽

Mems Sensor ◽

Measurement Units ◽

Sensor Errors

This paper investigates the thermal characteristics of typical Micro Electro-Mechanical System (MEMS) Inertial Measurement Units (IMUs) with a reliable thermal test procedure. Test results show that MEMS sensor errors, not only biases, but also scale factors and non-orthogonalities, may vary significantly with temperature. Also, MEMS sensor errors can have significant inconsistent curves under different temperature changing profiles. The existence of such inconsistencies posed a challenge to the following assumption of thermal calibration: the thermal drift of a sensor error is only related to the temperature of the sensor core. A robust way to mitigate this issue is given by using the sensor data during both heat-and-stay and cool-and-stay processes to establish the final thermal models. The performance of both IMUs and inertial navigation systems improved significantly after compensation with the established thermal models. Additionally, the variation of the IMU thermal parameters with time was observed, which suggests that periodical thermal calibration is necessary for MEMS IMUs.

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Continuous Tracking of Foot Strike Pattern during a Maximal 800-Meter Run

Sensors ◽

10.3390/s21175782 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5782

Author(s):

Kathryn A. Farina ◽

Alan R. Needle ◽

Herman van Werkhoven

Keyword(s):

Inertial Measurement Unit ◽

Measurement Unit ◽

Continuous Variables ◽

Potential Influence ◽

Inertial Measurement ◽

Injury Reduction ◽

Foot Strike ◽

Two Measures ◽

Level Of Significance ◽

Strike Angle

(1) Background: Research into foot strike patterns (FSP) has increased due to its potential influence on performance and injury reduction. The purpose of this study was to evaluate changes in FSP throughout a maximal 800-m run using a conformable inertial measurement unit attached to the foot; (2) Methods: Twenty-one subjects (14 female, 7 male; 23.86 ± 4.25 y) completed a maximal 800-m run while foot strike characteristics were continually assessed. Two measures were assessed across 100-m intervals: the percentage of rearfoot strikes (FSP%RF), and foot strike angle (FSA). The level of significance was set to p ≤ 0.05; (3) Results: There were no differences in FSP%RF throughout the run. Significant differences were seen between curve and straight intervals for FSAAVE (F [1, 20] = 18.663, p < 0.001, ηp2 = 0.483); (4) Conclusions: Participants displayed decreased FSA, likely indicating increased plantarflexion, on the curve compared to straight intervals. The analyses of continuous variables, such as FSA, allow for the detection of subtle changes in foot strike characteristics, which is not possible with discrete classifiers, such as FSP%RF.

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Feasibility of a Sensor-Based Technological Platform in Assessing Gait and Sleep of In-Hospital Stroke and Incomplete Spinal Cord Injury (iSCI) Patients

Sensors ◽

10.3390/s20102748 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2748

Author(s):

Maartje M. S. Hendriks ◽

Marije Vos-van der Hulst ◽

Noel L. W. Keijsers

Keyword(s):

Spinal Cord ◽

Spinal Cord Injury ◽

Sensor Data ◽

Incomplete Spinal Cord Injury ◽

Inertial Measurement ◽

Cord Injury ◽

Neurological Patients ◽

Measurement Units ◽

Data Error ◽

Technological Platform

Recovery of the walking function is one of the most common rehabilitation goals of neurological patients. Sufficient and adequate sleep is a prerequisite for recovery or training. To objectively monitor patients’ progress, a combination of different sensors measuring continuously over time is needed. A sensor-based technological platform offers possibilities to monitor gait and sleep. Implementation in clinical practice is of utmost relevance and has scarcely been studied. Therefore, this study examined the feasibility of a sensor-based technological platform within the clinical setting. Participants (12 incomplete spinal cord injury (iSCI), 13 stroke) were asked to wear inertial measurement units (IMUs) around the ankles during daytime and the bed sensor was placed under their mattress for one week. Feasibility was established based on missing data, error cause, and user experience. Percentage of missing measurement days and nights was 14% and 4%, respectively. Main cause of lost measurement days was related to missing IMU sensor data. Participants were not impeded, did not experience any discomfort, and found the sensors easy to use. The sensor-based technological platform is feasible to use within the clinical rehabilitation setting for continuously monitoring gait and sleep of iSCI and stroke patients.

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Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology

Sustainability ◽

10.3390/su12239796 ◽

2020 ◽

Vol 12 (23) ◽

pp. 9796

Author(s):

J. M. Olivart i Llop ◽

D. Moreno-Salinas ◽

J. Sánchez

Keyword(s):

Real Time ◽

Software Design ◽

Reference System ◽

Inertial Measurement Units ◽

Inertial Measurement ◽

Research Areas ◽

Principal Axes ◽

Requirement Definition ◽

Measurement Units ◽

And Performance

An accurate positioning and attitude computation of vehicles, robots, or even persons is of the utmost importance and critical for the success of many operations in multiple commercial, industrial, and research areas. However, most of these positioning and attitude systems rely on inertial measurement units that must be periodically recalibrated and have a high cost. In the present work, the design of a real-time positioning and attitude system using three positioning sensors based on the GNSS-RTK technology is presented. This kind of system does not need recalibration, and it allows one to define the attitude of a solid by only computing the position of the system in the global reference system and the three angles that the relative positions of the GNSS antennas define with respect to the principal axes of the solid. The position and attitude can be computed in real time for both static and dynamic scenarios. The only limitation of the system is that the antennas need to be in open air to work at full performance and accuracy. All the design phases are covered in the prototype construction: requirement definition, hardware selection, software design, assembly, and validation. The feasibility and performance of the system were tested in both static and dynamic real scenarios.

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Mems Technology Quality Requirements as Applied to Multibeam Echosounder

Polish Maritime Research ◽

10.2478/pomr-2018-0132 ◽

2018 ◽

Vol 25 (4) ◽

pp. 59-64

Author(s):

Krzysztof Bikonis ◽

Jerzy Demkowicz

Keyword(s):

Inertial Sensors ◽

Low Cost ◽

Microelectromechanical System ◽

Power Efficient ◽

Inertial Measurement ◽

Multibeam Echosounder ◽

Mems Technology ◽

Measurement Units ◽

And Performance

Abstract Small, lightweight, power-efficient and low-cost microelectromechanical system (MEMS) inertial sensors and microcontrollers available in the market today help reduce the instability of Multibeam Sonars. Current MEMS inertial measurement units (IMUs) come in many shapes, sizes, and costs - depending on the application and performance required. Although MEMS inertial sensors offer affordable and appropriately scaled units, they are not currently capable of meeting all requirements for accurate and precise attitudes, due to their inherent measurement noise. The article presents the comparison of different MEMS technologies and their parameters regarding to the main application, namely Multibeam Echo Sounders (MBES). The quality of MEMS parameters is crucial for further MBES record-processing. The article presents the results of undertaken researches in that area, and these results are relatively positive for low-cost MEMS. The paper undertakes some vital aspect of using MEMS in the attitude and heading reference system (AHRS) context. The article presents a few aspects of MEMS gyro errors and their estimation process in the context of INS processing flow, as well as points out the main difficulties behind the INS when using a few top MEMS technologies.

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Multidimensional Measures of Physical Activity and Their Association with Gross Motor Capacity in Children and Adolescents with Cerebral Palsy

Sensors ◽

10.3390/s20205861 ◽

2020 ◽

Vol 20 (20) ◽

pp. 5861

Author(s):

Corinna N. Gerber ◽

Lena Carcreff ◽

Anisoara Paraschiv-Ionescu ◽

Stéphane Armand ◽

Christopher J. Newman

Keyword(s):

Physical Activity ◽

Cerebral Palsy ◽

Performance Metrics ◽

Strong Association ◽

Ongoing Debate ◽

Gross Motor ◽

Inertial Measurement ◽

Pattern Complexity ◽

Measurement Units ◽

And Performance

The current lack of adapted performance metrics leads clinicians to focus on what children with cerebral palsy (CP) do in a clinical setting, despite the ongoing debate on whether capacity (what they do at best) adequately reflects performance (what they do in daily life). Our aim was to measure these children’s habitual physical activity (PA) and gross motor capacity and investigate their relationship. Using five synchronized inertial measurement units (IMU) and algorithms adapted to this population, we computed 22 PA states integrating the type (e.g., sitting, walking, etc.), duration, and intensity of PA. Their temporal sequence was visualized with a PA barcode from which information about pattern complexity and the time spent in each of the six simplified PA states (PAS; considering PA type and duration, but not intensity) was extracted and compared to capacity. Results of 25 children with CP showed no strong association between motor capacity and performance, but a certain level of motor capacity seems to be a prerequisite for the achievement of higher PAS. Our multidimensional performance measurement provides a new method of PA assessment in this population, with an easy-to-understand visual output (barcode) and objective data for clinical and scientific use.

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Reconstruction of operating loads in wind turbines with inertial measurement units

Forschung im Ingenieurwesen ◽

10.1007/s10010-021-00455-0 ◽

2021 ◽

Author(s):

Marcus Wiens ◽

Tim Martin ◽

Tobias Meyer ◽

Adam Zuga

Keyword(s):

Wind Turbines ◽

Tracking Error ◽

Test Site ◽

Sensor Data ◽

Inertial Measurement Units ◽

Additional Increase ◽

Inertial Measurement ◽

Load Monitoring ◽

Measurement Units ◽

Filter Approach

AbstractWind turbines are a major source of renewable energy. Load monitoring is considered to improve reliability of the systems and to reduce the cost of operation. We propose a load monitoring system which consists of inertial measurement units. These track the movement of rotor blade, hub and tower top. In addition, wind turbine states, e.g. yaw angle, pitch angle and rotation speed, are recorded. By solving a navigation algorithm with a Kalman Filter approach, the raw sensor data is combined with an error model to reduce the tracking error. In total, five inertial measurement units are installed on the research wind energy converter AD 8–180 on the test site in Bremerhaven. Results show that tracking the blade movement in full operation is possible and that loads can be estimated with a model-based approach. In comparison to simulations, the blade deflections can be approximated by an aeroelastic model. The presented approach can be used as basis for comprehensive load monitoring and observer system with additional increase of system robustness by measurement redundancy.

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Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Sensors ◽

10.3390/s21010106 ◽

2020 ◽

Vol 21 (1) ◽

pp. 106

Author(s):

Chih-Ya Chang ◽

Chia-Yeh Hsieh ◽

Hsiang-Yun Huang ◽

Yung-Tsan Wu ◽

Liang-Cheng Chen ◽

...

Keyword(s):

Assessment Tool ◽

Frozen Shoulder ◽

Quantitative Information ◽

Assessment Tools ◽

Inertial Measurement Units ◽

Inertial Measurement ◽

Measurement Units ◽

And Performance ◽

Task Segmentation ◽

Task Identification

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.

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PENGAWASAN DAN DISIPLIN TERHADAP KINERJA PEGAWAI PADA BADAN PERENCANAAN PEMBANGUNAN DAERAH

Jurnal Riset Bisnis dan Manajemen ◽

10.23969/jrbm.v13i2.3223 ◽

2020 ◽

Keyword(s):

Regression Analysis ◽

Research Method ◽

Employee Performance ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Consumer Responses ◽

Analysis Techniques ◽

Change Behavior ◽

And Performance ◽

Management Functions

Supervision as one of the management functions in achieving goals, plays a very important role by monitoring the possibility of preventable deviations, then discipline is a tool used to communicate with employees so that they are willing to change behavior and as an effort to increase awareness and willingness to obey all company regulations and prevail social norms. The purpose of this study is to determine the responses of respondents and the influence of supervision, work discipline on employee performance at BAPPEDA Kota Bandung, simultaneously or partially. This research method is quantitative with multiple linear regression analysis techniques. The results showed consumer responses to supervision with a score of 3.73, discipline with a score of 3.76, and performance with a score of 3.77. There is as well as a positive and significant effect partially and simultaneously from supervision, discipline of employee performance at BAPPEDA Kota Bandung.

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