Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

Ancillao, Andrea; Tedesco, Salvatore; Barton, John; O’Flynn, Brendan

doi:10.3390/s18082564

Cited by 179 publications

(177 citation statements)

References 90 publications

(169 reference statements)

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“…Mannini et al discussed the computational algorithms for classifying human physical activity using on-body accelerometers [23]. Ancillao et al identified, selected, and categorized the methodologies for estimating the ground reaction forces from kinematic data obtained by inertial measurement units worn by the subject, laying the foundation for kinetic analysis and motion performance testing outside the laboratory [24].…”

Section: Motion Capturementioning

confidence: 99%

Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences

et al. 2019

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Using video sequences to restore 3D human poses is of great significance in the field of motion capture. This paper proposes a novel approach to estimate 3D human action via end-to-end learning of deep convolutional neural network to calculate the parameters of the parameterized skinned multi-person linear model. The method is divided into two main stages: (1) 3D human pose estimation based on a single frame image. We use 2D/3D skeleton point constraints, human height constraints, and generative adversarial network constraints to obtain a more accurate human-body model. The model is pre-trained using open-source human pose datasets; (2) Human-body pose generation based on video streams. Combined with the correlation of video sequences, a 3D human pose recovery method based on video streams is proposed, which uses the correlation between videos to generate a smoother 3D pose. In addition, we compared the proposed 3D human pose recovery method with the commercial motion capture platform to prove the effectiveness of the proposed method. To make a contrast, we first built a motion capture platform through two Kinect (V2) devices and iPi Soft series software to obtain depth-camera video sequences and monocular-camera video sequences respectively. Then we defined several different tasks, including the speed of the movements, the position of the subject, the orientation of the subject, and the complexity of the movements. Experimental results show that our low-cost method based on RGB video data can achieve similar results to commercial motion capture platform with RGB-D video data.

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Section: Motion Capturementioning

confidence: 99%

Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences

et al. 2019

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“…Sabatini (2011) provides an overview of the use of inertial sensors for estimating segment and joint kinematics using physics-based techniques and sensor fusion algorithms [21]. Ancillao et al (2018) review physics-based techniques for estimating ground reaction forces and moments using wearable inertial sensors [40]. While these previous reviews capture the current state of physics-based techniques well, there has not been a comprehensive review of regression techniques for estimating joint, segment, and muscle kinetics and kinematics from wearable sensor data.…”

Section: Relevant Reviewsmentioning

confidence: 99%

“…Regression regress* OR "machine learning" OR "artificial intelligence" OR "statistical learning" OR "supervised learning" OR "unsupervised learning" OR "neural network" OR perceptron OR "support vector" OR "gaussian process" wearable OR accelerometer OR gyroscope OR electromyo* OR EMG OR sEMG OR "inertial sensor" OR "inertial measurement unit" OR IMU OR insole OR goniometer used to estimate joint angles; two of which are also included here. Finally, Ancillao et al (2018) also reviewed machine learning techniques for estimating ground reaction forces and moments [40]. Thus, studies estimating only ground reaction forces and moments were excluded in this review.…”

Section: Review Relevant Item Search Termsmentioning

confidence: 99%

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Gurchiek¹,

Cheney²,

McGinnis³

2019

Preprint

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Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

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“…Nowadays, there are a relatively great number of techniques and studies that include motion capture/analysis, vibration analysis, measurement of human performance, etc. [20][21][22][23], but there are few studies using infrared thermography (IRT). Nevertheless, the use of the IRT technique to evaluate skin temperature in health and sports is constantly growing, which shows the importance of this technique concerning performance in sports [24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Hand-Arm Vibration Assessment and Changes in the Thermal Map of the Skin in Tennis Athletes during the Service

Amaro

Paulino

Neto

et al. 2019

IJERPH

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During recent years the number of tennis athletes has increased significantly. When playing tennis, the human body is exposed to many situations which can lead to human injuries, such as the so-called tennis elbow (lateral epicondylitis). In this work a biomechanical analysis of tennis athletes, particularly during the service, was performed, considering three different types of over-grip and the presence of one anti-vibrator device. One part of the study evaluates the exposure to hand-arm vibration of the athlete, based on the European Directive 2002/44/EC concerning the minimum health and safety requirements, regarding the exposure of workers to risks from physical agents. The second part of the study considers an infrared thermography analysis in order to identify signs of risk of injury, particularly tennis elbow, one of the most common injuries in this sport. The results show that the presence of the anti-vibrator influences the vibration values greatly in the case of athletes with more experience and also for athletes with less performance. The presence of the Cork and/or Tourna on the racket grip does not have any significant effect on the hand-arm vibration (HAV), similarly in the case of athletes with the best performance and athletes with less technique. The results indicated that the infrared thermography technique may be used to identify the risk of injuries in tennis players.

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Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review

Cited by 179 publications

References 90 publications

Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences

Motion Capture Research: 3D Human Pose Recovery Based on RGB Video Sequences

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques

Hand-Arm Vibration Assessment and Changes in the Thermal Map of the Skin in Tennis Athletes during the Service

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