Sensor fusion for complex articulated body tracking applied in rowing

Ruffaldi, Emanuele; Peppoloni, Lorenzo; Filippeschi, Alessandro

doi:10.1177/1754337115583199

Cited by 17 publications

(19 citation statements)

References 26 publications

(34 reference statements)

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“…Finally, the custom device, described in References [ 32 , 33 ], is composed of 11 IMUs, attached to the upper arms, lower arms, upper legs, lower legs, pelvis, head and T1 vertebra, that allows reconstructing the movement of the whole body except the wrist joint. In Figure 3 c a setup with four units worn by a volunteer is presented.…”

Section: Materials and Methodsmentioning

confidence: 99%

Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling

Giannini

Bassani

Avizzano

et al. 2020

Sensors

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The assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture enables to advance the risk assessment by providing reliable, repeatable, and objective measures. However, existing solutions do not address either a full body assessment or the inclusion of measures for the evaluation of the effort. This article proposes a novel system for the assessment of biomechanical overload, capable of covering all areas of ISO 11228, that uses a sensor network composed of inertial measurement units (IMU) and electromyography (EMG) sensors. The proposed method is capable of gathering and processing data from three IMU-based motion capture systems and two EMG capture devices. Data are processed to provide both segmentation of the activity and ergonomic risk score according to the methods reported in the ISO 11228 and the TR 12295. The system has been tested on a challenging outdoor scenario such as lift-on/lift-off of containers on a cargo ship. A comparison of the traditional evaluation method and the proposed one shows the consistency of the proposed system, its time effectiveness, and its potential for deeper analyses that include intra-subject and inter-subjects variability as well as a quantitative biomechanical analysis.

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Section: Materials and Methodsmentioning

confidence: 99%

Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling

Giannini

Bassani

Avizzano

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…Kayaking/Canoeing Citation Sport Validation Technology [12] Rowing Optical motion capture [14] Rowing Coach qualitative assessment [17] Rowing Manual feature labelling [18] Rowing Controlled laboratory validation test [20] Rowing Force plates [25] Rowing GPS [27] Rowing GPS [30] Rowing GPS [32] Rowing Navilock-550 ERS [33] Rowing Optical motion capture [38] Rowing GPS [39] Rowing GPS [40] Rowing Optical motion capture [41] Rowing Optical motion capture [42] Canoeing Video Camera [43] Rowing GPS and stroke coach monitor [44] Rowing GPS [46] Rowing Reference measures from rowing simulator [47] Rowing Peach innovations measurement oarlock…”

Section: Citation Sport Filtering/windowing Data Fourier Transform (Fmentioning

confidence: 99%

“…The ability to measure the quality of the stroke cycle with an abundance of metrics is thus of high interest to coaches and athletes alike. Stroke rate (cadence) was the most frequently extracted metric surrounding stroke quality (12/24-50.0%) [14,16,21,25,26,[33][34][35][36][37]. Athletes can have an optimal stroke rate based on their physicality and thus it is an important metric to monitor.…”

Section: Performance Featuresmentioning

confidence: 99%

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A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors

et al. 2019

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Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into many coaches’ training regimes. There is an emerging trend to use inertial sensors for performance monitoring in rowing; however, the use and selection of the sensor used has not been appropriately reviewed. Previous literature assessed the sampling frequency, position, and fixing of the sensor; however, properties such as the sensor operating ranges, data processing algorithms, and validation technology are left unevaluated. To address this gap, a systematic literature review on rowing performance monitoring using inertial-magnetic sensors was conducted. A total of 36 records were included for review, demonstrating that inertial measurements were predominantly used for measuring stroke quality and the sensors were used to instrument equipment rather than the athlete. The methodology for both selecting and implementing technology appeared ad hoc, with no guidelines for appropriate analysis of the results. This review summarizes a framework of best practice for selecting and implementing inertial sensor technology for monitoring rowing performance. It is envisaged that this review will act as a guide for future research into applying technology to rowing.

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“…A network of IMUs on the rower can capture rowing with results similar to motion capture cameras [17] and real-time accelerometer-based feedback has been found to improve rowing consistency when used on indoor ergometers [18]. Tessendorf et.…”

Section: Related Workmentioning

confidence: 99%

Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics

et al. 2019

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Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics.

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Sensor fusion for complex articulated body tracking applied in rowing

Cited by 17 publications

References 26 publications

Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling

Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling

A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors

Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics

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