Instrumented Rowing Machine for Optimized Training

Bassetti, M.; Braghin, Francesco; Cheli, Federico; Maldifassi, Stefano

doi:10.1007/978-1-4614-2419-2_51

Cited by 4 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wearable sensors are extensively used in sports research, especially for analyzing the performance of elite athletes [2,11,16]. For rowing, most research focuses either on the athlete's force or power output [2][3][4][5]8] and the optimal movement of the oars [1,5,12,18,19] and the boat [18,19]. The posture of the athlete received less attention so far, while it does provide deeper insight in flaws in the rower's technique that directly influence performance.…”

Section: Related Workmentioning

confidence: 99%

“…For rowing exercise on the water, the movement of the boat and the oars can be monitored using sensors [5]. For indoor rowing, the rowing machine can be instrumented to measure various parameters of the rower's activity, such as power output, exerted force and the position of the slide and handle bar [3]. Alternatively, the posture of the rower can be measured using cameras or on-body sensors [17] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

Bosch¹,

Shoaib²,

Geerlings³

et al. 2015

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

In this exploratory work the motion of rowers is analyzed while rowing on a rowing machine. This is performed using inertial sensors that measure the orientation at several positions on the body. Using these measurements, this work provides a preliminary analysis of the differences between experienced and novice rowers, or between a good and a bad technique. The analysis shows that the measured postural angles show no clear trend that would set apart experienced and novice rowers or a bad and a good technique. However, there are clear differences in absolute postural angle's consistency and timing consistency of strokes between novice and experienced rowers. We also applied a machine learning technique to the data to find the similarities between different rowers and an experienced reference rower. The results can be used to compare the quality of the rowing technique with respect to a reference. In this paper, we present our initial results as well as the challenges that need to be further explored.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

Bosch¹,

Shoaib²,

Geerlings³

et al. 2015

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

show abstract

“…These inputs included compression force F exerted by the rower upon the seat and the z-axis coordinate z P of the CoP. F significantly increases with increasing rowing frequency, 40 whereas z P varies with both body limb positions and trunk inclination with respect to the seat plane. Due to the symmetry of the movement during the rowing motion, the x-axis coordinate of the CoP was not used as ANN input.…”

Section: Shoulder Position Prediction By Means Of An Annmentioning

confidence: 99%

Tracking rower motion without on-body sensors using an instrumented machine and an artificial neural network

BenSiSaid

Ababou

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part P:

View full text Add to dashboard Cite

Human motion tracking is an active field of research driven by its diverse applications in areas such as health care, daily activity recognition, sports, etc. In sports applications, tracking rowing motion is performed to meet several goals, including prevention of injuries, improvement of performance or provision of virtual coaching. Different established approaches rely on on-body sensors to capture rowing motion. While on-body sensors are effective and straight forward to implement, they can disturb the athlete and negatively impact the training. In this paper, an approach is presented to track rowing motion without body-worn sensors or cameras. Instead, sensors were attached to an indoor rowing machine that tracked the motion of its sliding seat, lever handle and the force exercised on the seat. In particular, the motion was tracked by means of linear and angular displacement sensors, as well as force sensors placed underneath the seat. The respective variables were fed into an Artificial Neural Network (ANN) to predict the coordinates of the rower’s shoulder, which in turn, were used to geometrically infer the angles of the shoulder, elbow, hip and thoracolumbar flexion-extension. A successful ANN architecture was iteratively designed by using the Levenberg-Marquardt algorithm and varying the number of hidden neurons in one hidden layer. A comparison between ANN-predicted and experimentally obtained shoulder coordinates from an optical motion capture system showed a mean error of less than 4 cm, which led to an angle mean error value as low as 2.01°. A rigged avatar was used to visually verify the reproduced motion. The avatar animation was well-received by experts, especially considering the shoulder adduction-abduction in the frontal plane.

show abstract

“…Wearable sensors are extensively used in sports research, especially for analyzing the performance of elite athletes [77,78,79]. For rowing, most research focuses either on the athlete's force or power output [80,78,81,70,71] and the optimal movement of the oars [82,73,70,83,84] and the boat [82,73]. The posture of the athlete received less attention so far, while it does provide deeper insight in flaws in the rower's technique that directly influence performance.…”

Section: Related Workmentioning

confidence: 99%

“…For rowing exercise on the water, the movement of the boat and the oars can be monitored using sensors [70]. For indoor rowing, the rowing machine can be instrumented to measure various parameters of the rower's activity, such as power output, exerted force and the position of the slide and handle bar [71]. Alternatively, the posture of the rower can be measured using cameras or on-body sensors [72] .…”

Section: Introductionmentioning

confidence: 99%

Pushing the limits of intertial motion sensing

Bosch¹

View full text Add to dashboard Cite

This thesis explores how to capture and digitize human and animal motion with miniature IMU motion sensing devices and fulfill a wide range of application requirements despite resource constraints related to sensor limitations, wireless communication, processing, and energy consumption. The hypothesis is that the limitations of inertial sensing technology can be circumvented by devising intelligent algorithms that exploit application specifics. In the first part of this thesis, these limitations are circumvented by only measuring the acceleration and angular velocity magnitude, which are directionless values. This is used to track the intensity of motion over time, allowing for objective measurement of for example how active a person is, which has applications in healthcare. In the second part of this thesis, the capabilities of the sensor devices include measuring orientation and displacement with acceptable quality, yet only over short intervals of up to a minute, depending on the intensity of the motion. This is used to measure body angles and gait symmetry for human and equine applications. The time limitation for the displacement result is circumvented by exploiting the periodicity of locomotion, so that it is not needed to calculate the absolute displacement. Body angles are only measured relative to the vertical axis, so that an accurate attitude measurement is sufficient. In the third part of this thesis, an indoor positioning system is designed and analyzed that aims to meet the requirements for use by firefighters using foot-mounted motion sensors and pedestrian dead-reckoning (PDR). This is achieved by tracking the full path that a motion sensor takes trough a building while it is attached to the firefighter's boot. Performance is impeded by drift errors on the orientation yaw direction and in the displacement result and the irregular and sometimes intense movements performed by firefighters are challenging. These challenges are addressed by using innovative algorithms that involve additional sensors in the tracking process.Experiments show that it is possible to estimate simple motion features like motion intensity and whether sensors are moving simultaneously, even with very limited IMU sensors. With more recent and capable inertial sensors, it possible to measure periodic angular and lateral kinematic motion features with sufficient accuracy for sports and medical applications targeted at humans and animals alike. With state-of-the-art IMU v devices augmented with barometric and magnetic sensors, it is possible to estimate the path of a firefighter in a building, despite the fact that firefighters tend to move in unconventional ways. The collective experiments show that limitations of inertial sensing technology can be circumvented by devising intelligent algorithms that exploit application specifics. During the course of these experiments, sensor devices were developed that are now being used world-wide in academia and medical applications. vii zoals bewegingsintensiteit en of sensoren gelijktijdig...

show abstract

Instrumented Rowing Machine for Optimized Training

Cited by 4 publications

References 8 publications

Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

Analysis of Indoor Rowing Motion using Wearable Inertial Sensors

Tracking rower motion without on-body sensors using an instrumented machine and an artificial neural network

Pushing the limits of intertial motion sensing

Contact Info

Product

Resources

About