scholarly journals Continuous Authentication of Automotive Vehicles Using Inertial Measurement Units

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5283 ◽  
Author(s):  
Gianmarco Baldini ◽  
Filip Geib ◽  
Raimondo Giuliani
Keyword(s):  
Wearable Sensors ◽  
Measurement Unit ◽  
Daily Routine ◽  
Inertial Measurement Units ◽  
Digital Output ◽  
Inertial Measurement ◽  
Continuous Authentication ◽  
Measurement Units ◽  
Analytical Approaches ◽  
Road Segments

The concept of Continuous Authentication is to authenticate an entity on the basis of a digital output generated in a continuous way by the entity itself. This concept has recently been applied in the literature for the continuous authentication of persons on the basis of intrinsic features extracted from the analysis of the digital output generated by wearable sensors worn by the subjects during their daily routine. This paper investigates the application of this concept to the continuous authentication of automotive vehicles, which is a novel concept in the literature and which could be used where conventional solutions based on cryptographic means could not be used. In this case, the Continuous Authentication concept is implemented using the digital output from Inertial Measurement Units (IMUs) mounted on the vehicle, while it is driving on a specific road path. Different analytical approaches based on the extraction of statistical features from the time domain representation or the use of frequency domain coefficients are compared and the results are presented for various conditions and road segments. The results show that it is possible to authenticate vehicles from the Inertial Measurement Unit (IMU) recordings with great accuracy for different road segments.

scholarly journals Accuracy and Repeatability of Spatiotemporal Gait Parameters Measured with an Inertial Measurement Unit

2021 ◽  
Vol 10 (9) ◽  
pp. 1804
Author(s):  
Jorge Posada-Ordax ◽  
Julia Cosin-Matamoros ◽  
Marta Elena Losa-Iglesias ◽  
Ricardo Becerro-de-Bengoa-Vallejo ◽  
Laura Esteban-Gonzalo ◽  
...  
Keyword(s):  
Healthy Subjects ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
High Speeds ◽  
Controlled Conditions ◽  
Gait Parameters ◽  
Measurement Units ◽  
Accuracy And Repeatability

In recent years, interest in finding alternatives for the evaluation of mobility has increased. Inertial measurement units (IMUs) stand out for their portability, size, and low price. The objective of this study was to examine the accuracy and repeatability of a commercially available IMU under controlled conditions in healthy subjects. A total of 36 subjects, including 17 males and 19 females were analyzed with a Wiva Science IMU in a corridor test while walking for 10 m and in a threadmill at 1.6 km/h, 2.4 km/h, 3.2 km/h, 4 km/h, and 4.8 km/h for one minute. We found no difference when we compared the variables at 4 km/h and 4.8 km/h. However, we found greater differences and errors at 1.6 km/h, 2.4 km/h and 3.2 km/h, and the latter one (1.6 km/h) generated more error. The main conclusion is that the Wiva Science IMU is reliable at high speeds but loses reliability at low speeds.

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

Biosensors ◽  
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.

scholarly journals In Vivo Measurement of Wrist Movements during the Dart-Throwing Motion Using Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5623
Author(s):  
Gabriella Fischer ◽  
Michael Alexander Wirth ◽  
Simone Balocco ◽  
Maurizio Calcagni
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Absolute Difference ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Mean Absolute Difference ◽  
Inertial Measurement ◽  
Dart Throwing ◽  
Measurement Units ◽  
Throwing Motion

Background: This study investigates the dart-throwing motion (DTM) by comparing an inertial measurement unit-based system previously validated for basic motion tasks with an optoelectronic motion capture system. The DTM is interesting as wrist movement during many activities of daily living occur in this movement plane, but the complex movement is difficult to assess clinically. Methods: Ten healthy subjects were recorded while performing the DTM with their right wrist using inertial sensors and skin markers. Maximum range of motion obtained by the different systems and the mean absolute difference were calculated. Results: In the flexion–extension plane, both systems calculated a range of motion of 100° with mean absolute differences of 8°, while in the radial–ulnar deviation plane, a mean absolute difference of 17° and range of motion values of 48° for the optoelectronic system and 59° for the inertial measurement units were found. Conclusions: This study shows the challenge of comparing results of different kinematic motion capture systems for complex movements while also highlighting inertial measurement units as promising for future clinical application in dynamic and coupled wrist movements. Possible sources of error and solutions are discussed.

scholarly journals Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2983
Author(s):  
Marie Sapone ◽  
Pauline Martin ◽  
Khalil Ben Mansour ◽  
Henry Château ◽  
Frédéric Marin
Keyword(s):  
Motion Capture ◽  
Inertial Measurement Unit ◽  
Detection Methods ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
New Methods ◽  
Measurement Units ◽  
Ground Types ◽  
Stride Duration

The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.

scholarly journals Validity of Wearable Sensors at the Shoulder Joint: Combining Wireless Electromyography Sensors and Inertial Measurement Units to Perform Physical Workplace Assessments

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1885 ◽  
Author(s):  
Isabelle Poitras ◽  
Mathieu Bielmann ◽  
Alexandre Campeau-Lecours ◽  
Catherine Mercier ◽  
Laurent J. Bouyer ◽  
...  
Keyword(s):  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Emg Activity ◽  
Complex Task ◽  
Inertial Measurement Units ◽  
Simple Task ◽  
Inertial Measurement ◽  
Work Related ◽  
Measurement Units ◽  
Shoulder Kinematics

Background: Workplace adaptation is the preferred method of intervention to diminish risk factors associated with the development of work-related shoulder disorders. However, the majority of the workplace assessments performed are subjective (e.g., questionnaires). Quantitative assessments are required to support workplace adaptations. The aims of this study are to assess the concurrent validity of inertial measurement units (IMUs; MVN, Xsens) in comparison to a motion capture system (Vicon) during lifting tasks, and establish the discriminative validity of a wireless electromyography (EMG) system for the evaluation of muscle activity. Methods: Sixteen participants performed 12 simple tasks (shoulder flexion, abduction, scaption) and 16 complex lifting tasks (lifting crates of different weights at different heights). A Delsys Trigno EMG system was used to record anterior and middle deltoids’ EMG activity, while the Xsens and Vicon simultaneously recorded shoulder kinematics. Results: For IMUs, correlation coefficients were high (simple task: >0.968; complex task: >0.84) and RMSEs were low (simple task: <6.72°; complex task: <11.5°). For EMG, a significant effect of weight, height and a weight x height interaction (anterior: p < 0.001; middle: p < 0.03) were observed for RMS EMG activity. Conclusions: These results suggest that wireless EMG and IMUs are valid units that can be used to measure physical demand in workplace assessments.

scholarly journals Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2501 ◽  
Author(s):  
Mohammad Mokhlespour Esfahani ◽  
Maury Nussbaum
Keyword(s):  
Human Activity ◽  
Wearable Sensors ◽  
Activity Monitor ◽  
Activity Monitoring ◽  
Whole Body ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Wearable Technologies ◽  
Smart Textile ◽  
Measurement Units

Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation, especially to facilitate future use. In this study, 18 participants evaluate the preferred placement and usability of two STSs, along with a comparison to a commercial IMU system. These evaluations are completed after participants engaged in a range of activities (e.g., sitting, standing, walking, and running), during which they wear two representatives of smart textile systems: (1) a custom smart undershirt (SUS) and commercial smart socks; and (2) a commercial whole-body IMU system. We first analyze responses regarding the usability of the STS, and subsequently compared these results to those for the IMU system. Participants identify a short-sleeved shirt as their preferred activity monitor. In additional, the SUS in combination with the smart socks is rated superior to the IMU system in several aspects of usability. As reported herein, STSs show promise for future applications in human activity monitoring in terms of usability.

scholarly journals Validity of a Magnet-Based Timing System Using the Magnetometer Built into an IMU

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5773
Author(s):  
Carla Pérez-Chirinos Buxadé ◽  
Bruno Fernández-Valdés ◽  
Mónica Morral-Yepes ◽  
Sílvia Tuyà Viñas ◽  
Josep Maria Padullés Riu ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Short Duration ◽  
Wearable Sensors ◽  
The Other ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Future Studies ◽  
Sprint Running ◽  
Timing System ◽  
Measurement Units

Inertial measurement units (IMUs) represent a technology that is booming in sports right now. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically to evaluate a magnet-based timing system (M-BTS) for timing short-duration sports actions using the magnetometer built into an IMU in different sporting contexts. Forty-eight athletes (22.7 ± 3.3 years, 72.2 ± 10.3 kg, 176.9 ± 8.5 cm) and eight skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg) performed a 60-m linear sprint running test and a ski slalom, respectively. The M-BTS consisted of placing several magnets along the course in both contexts. The magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. The time between peaks was calculated. The system was validated with photocells. The 95% error intervals for the total times were less than 0.077 s for the running test and 0.050 s for the ski slalom. With the M-BTS, future studies could select and cut the signals belonging to the other sensors that are integrated in the IMU, such as the accelerometer and the gyroscope.

Prediction of human gait activities using wearable sensors

2021 ◽  
pp. 095441192110012
Author(s):  
Ahmed Halim ◽  
A. Abdellatif ◽  
Mohammed I Awad ◽  
Mostafa RA Atia
Keyword(s):  
Prediction Accuracy ◽  
Gait Cycle ◽  
Wearable Sensors ◽  
Machine Learning Algorithms ◽  
Human Gait ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Acceptable Accuracy ◽  
Prediction Time ◽  
Measurement Units

This paper aims to enhance the accuracy of human gait prediction using machine learning algorithms. Three classifiers are used in this paper: XGBoost, Random Forest, and SVM. A predefined dataset is used for feature extraction and classification. Gait prediction is determined during several locomotion activities: sitting (S), level walking (LW), ramp ascend (RA), ramp descend (RD), stair ascend (SA), stair descend (SD), and standing (ST). The results are gained for steady-state (SS) and overall (full) gait cycle. Two sets of sensors are used. The first set uses inertial measurement units only. The second set uses inertial measurement units, electromyography, and electro-goniometers. The comparison is based on prediction accuracy and prediction time. In addition, a comparison between the prediction times of XGBoost with CPU and GPU is introduced due to the easiness of using XGBoost with GPU. The results of this paper can help to choose a classifier for gait prediction that can obtain acceptable accuracy with fewer types of sensors.

scholarly journals A Framework for Sensor-based Assessment of Upper-limb Functioning

2021 ◽  
Author(s):  
Ann David ◽  
Tanya Subash ◽  
SKM Varadhan ◽  
Alejandro Melendez-Calderon ◽  
Sivakumar Balasubramanian
Keyword(s):  
Upper Limb ◽  
Daily Life ◽  
Wearable Sensors ◽  
Clinical Work ◽  
Behavioral Pattern ◽  
Upper Limbs ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Using Data

AbstractThe ultimate goal of any upper-limb neurorehabilitation procedure is to improve upper-limb functioning in daily life. While clinic-based assessments provide an assessment of what a patient can do, they do not completely reflect what a patient does in his/her daily life. The compensatory use of the less affected upper-limb (e.g. “learned non-use”) in daily life is a common behavioral pattern seen in patients with hemiparesis. To this end, there has been an increasing interest in the use of wearable sensors to objectively assess upper-limb functioning. This paper presents a framework for assessing upper-limb functioning using sensors by providing: (a) a set of definitions of important construct associated with upper-limb functioning; (b) presenting different visualization methods for evaluating upper-limb functioning, along ways to qualitatively analyze different visualization methods; and (c) two new measures for quantifying how much an upper-limb is used and the relative bias in the use of the two upper-limbs. The demonstration of some of these components is presented using data collected from inertial measurement units from a previous study. The proposed framework can help guide the future technical and clinical work in this area to realize a valid, objective, and robust tool for assessing upper-limb functioning. This will in turn drive the refinement and standardization of the assessment of upper-limb functioning.

scholarly journals Kinematic Analysis of Figure Skating Jump by Using Wearable Inertial Measurement Units

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 124
Author(s):  
Yuchen Shi ◽  
Atsushi Ozaki ◽  
Masaaki Honda
Keyword(s):  
Kinematic Analysis ◽  
Wearable Sensors ◽  
Figure Skating ◽  
Inertial Measurement Units ◽  
Key Factors ◽  
Inertial Measurement ◽  
Flight Phase ◽  
Measurement Units ◽  
Figure Skater

The purpose of this study was to demonstrate the feasibility of measuring and analyzing characteristics of figure skating jumps using wearable sensors. One elite figure skater, outfitted with five inertial measurement units (IMUs), performed flip jumps with single, double, and triple revolutions. Take-off event and flight phase of each trial were under analysis. Kinematic differences among jumps with variant revolutions as well as key factors for performing successfully landed triple jumps were determined by IMU signals. Compared with a video-based method, this study revealed the following characteristics that coincide with previous studies: at take-off event, the skater performed pre-rotation and took off with preferred postural positions as revolutions increased (p < 0.01); during flight, the skater struggled more to maintain the smallest inertial of moment as revolutions increased (p < 0.01); in order to perform successfully landed jumps, it was crucial that the skater improved the control of preparation for flight at take-off (p < 0.05).

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