A Narrative Review on Wearable Inertial Sensors for Human Motion Tracking in Industrial Scenarios

Digo, Elisa; Pastorelli, Stefano Paolo; Gastaldi, Laura

doi:10.3390/robotics11060138

Cited by 15 publications

(7 citation statements)

References 62 publications

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“…To address this problem, this study estimates the lower-limb sagittal joint angles during gait based on acceleration and angular velocity, which can be measured using an inertial measurement unit (IMU). Although various sensors such as millimeter-wave radar ( Alanazi et al, 2022 ), fiber optics ( Mohamed et al, 2012 ; Kim et al, 2014 ), electro-goniometer ( Rowe et al, 2000 ; da Silva Camassuti et al, 2015 ), and e-textiles ( Tognetti et al, 2015 ) can estimate the kinematics according to previous studies, IMUs are portable (light and small), incur low cost, exhibit minimal invasiveness, and are not obtrusive ( e.g ., full-body suits) ( Digo, Pastorelli & Gastaldi, 2022 ). Owing to these advantages, portable measurement tools can help recruit larger, more diverse populations in clinical studies.…”

Section: Introductionmentioning

confidence: 99%

Lower-limb sagittal joint angles during gait can be predicted based on foot acceleration and angular velocity

Inai,

Takabayashi

2023

PeerJ

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Background and purpose Continuous monitoring of lower-limb movement may help in the early detection and control/reduction of diseases (such as the progression of orthopedic diseases) by applying suitable interventions. Therefore, it is invaluable to calculate the lower-limb movement (sagittal joint angles) while walking daily for continuous evaluation of such risks. Although cameras in a motion capture system are necessary for calculating lower-limb sagittal joint angles during gait, the method is unrealistic considering the setting is difficult to achieve in daily life. Therefore, the estimation of lower-limb sagittal joint angles during walking based on variables, which can be measured using wearable sensors (e.g., foot acceleration and angular velocity), is important. This study estimates the lower-limb sagittal joint angles during gait from the norms of foot acceleration and angular velocity using machine learning and validates the accuracy of the estimated joint angles with those obtained using a motion capture system. Methods Healthy adults (n = 200) were asked to walk at a comfortable speed (10 trials), and their lower-limb sagittal joint angles, foot accelerations, and angular velocities were obtained. Using these variables, we established a feedforward neural network and estimated the lower-limb sagittal joint angles. Results The average root mean squared errors of the lower-limb sagittal joint angles during gait ranged between 2.5°–7.0° (hip: 7.0°; knee: 4.0°; and ankle: 2.5°). Conclusion These results show that we can estimate the lower-limb sagittal joint angles during gait using only the norms of foot acceleration and angular velocity, which can help calculate the lower-limb sagittal joint angles during daily walking.

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Section: Introductionmentioning

confidence: 99%

Lower-limb sagittal joint angles during gait can be predicted based on foot acceleration and angular velocity

Inai,

Takabayashi

2023

PeerJ

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“…Optical systems are not adequate for this purpose because they work at too low frequencies and they are not able to detect abrupt gestures as early as possible. The problem of frequency can be solved by adopting wearable magneto-inertial measurement units (MIMUs), which collect data from the triaxial accelerometer, gyroscope and magnetometer embedded in each sensor [12]. In addition, MIMUs also overcome other limits typical of optical systems such as occlusion, encumbrance, a limited range of acquisition volume, heavy weight and high cost.…”

Section: Introductionmentioning

confidence: 99%

Test–Retest Repeatability of Human Gestures in Manipulation Tasks

et al. 2023

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The importance of performance excellence and operator’s safety is fundamental not only when operators perform repetitive and controlled industrial tasks, but also in case of abrupt gestures due to inattention and unexpected circumstances. Since optical systems work at frequencies that are too low and they are not able to detect gestures as early as possible, combining the use of wearable magneto-inertial measurement units (MIMUs) with the adoption of deep learning techniques can be useful to instruct the machine about human motion. To improve the initial training phase of neural networks for high classification performance, gesture repeatability over time has to be verified. Since the test–retest approach has been poorly applied based on MIMUs signals in a context of human–machine interaction, the aim of this work was to evaluate the repeatability of pick-and-place gestures composed of both normal and abrupt movements. Overall, results demonstrated an excellent test–retest repeatability for normal movements and a fair-to-good test–retest repeatability for abrupt movements. In addition, results suggested important information about the application of deep learning to identify the types of movements: the test showed how to improve reinforcement learning for the identification of onset gestures, whereas the retest allowed for defining the time necessary to retrain the network.

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“…Optical systems are not adequate for this purpose, because they work at too low frequencies and they are not 2 able to detect abrupt gestures as early as possible. The problem of frequency can be solved by adopting wearable magneto-inertial measurement units (MIMUs), which collect data from the triaxial accelerometer, gyroscope, and magnetometer embedded in each sensor [12]. Instead, the problem of early detection of abrupt movements can be approached through deep learning techniques, which are useful to extract and learn features directly from raw data and hence to instruct the machine about human motion [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Test-Retest Repeatability of Human Gestures in Manipulation Tasks

Digo¹,

Caselli²,

Polito³

et al. 2023

Preprint

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The importance of performance excellence and operator’s safety is fundamental not only when operators perform repetitive and controlled industrial tasks, but also in case of abrupt gestures due to inattention and unexpected circumstances. Since optical systems work at too low frequencies and they are not able to detect gestures as early as possible, combining the use of wearable magneto-inertial measurement units (MIMUs) with the adoption of deep learning techniques can be useful to instruct the machine about human motion. To improve the initial training phase of neural networks for high classification performance, gestures repeatability over time has to be verified. Since test-retest approach has been poorly applied based on MIMUs signals in a context of human-machine interaction, the aim of this work was to evaluate the repeatability of pick-and-place gestures composed of both normal and abrupt movements. Overall, results demonstrated an excellent test-retest repeatability for normal movements and a fair-to-good test-retest repeatability for abrupt movements. Moreover whereas the reduction of time between test and retest sessions increased repeatability indices.. In detail, results of the test suggested how to improve the reinforcement learning for the identification of gestures onset, whereas results of the retest gave important information about the time necessary to retrain the network.

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A Narrative Review on Wearable Inertial Sensors for Human Motion Tracking in Industrial Scenarios

Cited by 15 publications

References 62 publications

Lower-limb sagittal joint angles during gait can be predicted based on foot acceleration and angular velocity

Lower-limb sagittal joint angles during gait can be predicted based on foot acceleration and angular velocity

Test–Retest Repeatability of Human Gestures in Manipulation Tasks

Test-Retest Repeatability of Human Gestures in Manipulation Tasks

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