Introduction: IMU sensors (three-dimensional accelerometer, gyroscope and magnetometer) enable assessment of walking in older adults outside the laboratory. We studied whether IMUs are valid for detecting walking parameters (step events, time, length, and cadence) in a laboratory and outdoors on a level surface in older adults.
Methods: This validation study is part of a larger cross-sectional study. Twenty-six participants (mean age 76 years, 65 % female) walked on a treadmill indoors and on a sport track outdoors at self-selected speed. IMUs were attached laterally on the shanks and on the lower back at the level of L3-L4. Initial contact (IC) and step lengths were also estimated using acceleration signals (vertical, antero-posterior) from the pelvic IMU. Terminal contact (TC) was determined from the shank IMU sagittal angular velocity. For step length, inverted pendulum model and participant's leg length (0.53 x height) was used. Step duration was calculated from IC to the opposite leg IC and stride duration from IC to next ipsilateral IC. Cadence was calculated as steps/min. As reference data, 3D motion capture was used in the laboratory and a high-speed video camera outdoors. Intraclass correlation coefficients (ICC), root mean squared errors (RMSE), typical errors and Bland-Altman plots were calculated and drawn.
Results: When comparing IC timing between IMU and reference data, mean bias was 0.031 s in the laboratory and -0.004 s outdoors, and for TC -0.057 s and -0.070 s respectively. Step and stride duration and cadence showed ICC values >0.80 and mean bias was <0.005 s for step and stride durations and <0.05 steps/min for cadence in both environments. Step length ICC values were <0.40 in the laboratory and outdoors.
Significance: IMUs can be used to monitor temporal walking variables in older adults and may be useful for rehabilitation interventions and functional capacity assessment.
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http://dx.doi.org/10.1016/j.gaitpost.2024.10.013 | DOI Listing |
Sensors (Basel)
January 2025
Development Adaptation Handicap (DevAH) Research Unit, Université de Lorraine, 54000 Nancy, France.
Analyzing performance in rowing, e.g., analyzing force and power output profiles produced either on ergometer or on boat, is a priority for trainers and athletes.
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December 2024
Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, 14770 Brandenburg an der Havel, Germany.
Determining whether preoperative performance-based knee function predicts postoperative performance-based knee function and whether patient-reported outcome measures (PROMs) completed by participants can detect these changes could significantly enhance the planning of postoperative rehabilitation for patients following total knee arthroplasty (TKA). This study aims to collect data on performance-based knee function using inertial measurement units (IMUs) worn by participants both preoperatively and postoperatively. PROMs will be completed by the patients before and after surgery to assess their ability to detect the same changes in performance-based knee function measured by the sensors.
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December 2024
National Institute for Occupational Safety and Health, Cincinnati, OH 45226, USA.
The American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Values (TLVs) for lifting provides risk zones for assessing two-handed lifting tasks. This paper describes two computational models for identifying the lifting risk zones using gyroscope information from five inertial measurement units (IMUs) attached to the lifter. Two models were developed: (1) the ratio model using body segment length ratios of the forearm, upper arm, trunk, thigh, and calf segments, and (2) the ratio + length model using actual measurements of the body segments in the ratio model.
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December 2024
División de Sistemas e Ingeniería Electrónica (DSIE), Campus Muralla del Mar, s/n, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain.
This paper presents a novel end-to-end architecture based on edge detection for autonomous driving. The architecture has been designed to bridge the domain gap between synthetic and real-world images for end-to-end autonomous driving applications and includes custom edge detection layers before the Efficient Net convolutional module. To train the architecture, RGB and depth images were used together with inertial data as inputs to predict the driving speed and steering wheel angle.
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December 2024
SOTI Aerospace, SOTI Inc., Mississauga, ON L5N 8L9, Canada.
Indoor navigation is becoming increasingly essential for multiple applications. It is complex and challenging due to dynamic scenes, limited space, and, more importantly, the unavailability of global navigation satellite system (GNSS) signals. Recently, new sensors have emerged, namely event cameras, which show great potential for indoor navigation due to their high dynamic range and low latency.
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