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.

人体运动跟踪是一项活跃的研究领域，在健康护理，日常活动识别，运动等领域得到了广泛的应用。在体育应用中，进行划船运动跟踪是为了满足多个目标，包括预防伤害，改善骑行能力。执行或提供虚拟教练。已建立的不同方法依赖于人体传感器来捕获划船运动。人体传感器虽然有效且易于实施，但它们可能会干扰运动员并给训练带来负面影响。在本文中，提出了一种无需佩戴穿戴式传感器或照相机即可跟踪划船运动的方法。取而代之的是，将传感器安装到室内划船机上，该划船机跟踪其滑动座椅，杠杆手柄以及在座椅上施加的力的运动。特别是，通过线性和角位移传感器以及位于座椅下方的力传感器来跟踪运动。将各自的变量输入到人工神经网络（ANN）中，以预测赛艇运动员肩膀的坐标，然后将其用于从几何角度推断肩，肘，髋和胸腰椎屈伸角度。通过使用Levenberg-Marquardt算法并在一个隐藏层中改变隐藏神经元的数量，来迭代设计成功的ANN架构。ANN预测的和从光学运动捕捉系统实验获得的肩部坐标之间的比较显示，平均误差小于4 cm，这导致角度平均误差值低至2.01°。操纵的化身用于视觉验证复制的动作。