Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).

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The paper presents a sensor fusion model for integrating wearable inertial measures with sensors in the environment. This approach is designed and tested to support body motion tracking of rowing in indoor and outdoor environment. The paper presents the approach based on a complex kinematic model and unscented Kalman filtering. The approach is validated in an in-door setup based on the SPRINT rowing system by comparison against results obtained from a commercial motion capture system, thus providing future directions for the assessment of rowers performance on an instrumented boat

Elite-standard rowers tend to use a fast-start strategy followed by an inverted parabolic-shaped speed profile in 2000-m races. This strategy is probably the best to manage energy resources during the race and maximise performance. This study investigated the use of virtual reality (VR) with novice rowers as a means to learn about energy management. Participants from an avatar group (n = 7) were instructed to track a virtual boat on a screen, whose speed was set individually to follow the appropriate to-be-learned speed profile. A control group (n = 8) followed an indoor training programme. In spite of similar physiological characteristics in the groups, the avatar group learned and maintained the required profile, resulting in an improved performance (i.e. a decrease in race duration), whereas the control group did not. These results suggest that VR is a means to learn an energy-related skill and improve performance.

The advancements in technology and the possibility of their integration in the domain of virtual environments allow access to new application domains previously limited to highly expensive setups. This is specifically the case of sport training that can take advantage of the improved quality of measurement systems and computing techniques. Given this the challenge that emerges is related to the way training is performed and how it is possible to evaluate the transfer from the virtual setup to the real case. In this work we discuss the aspect of system architecture for a VE in sport training, taking as a case study a rowing training system. The paper will address in particular the challenges of training technique in rowing

This paper presents a vibrotactile methodology for a rowing training system. Since hands' trajectories are fundamental in the rowing gesture, it is completely necessary to search and develop new technologies and techniques that can interact and help the user to perform a better movement. These methodologies must be as natural as possible in order to guarantee the transparency in the feedback of the system. Therefore this paper presents an analysis of visual, visual-tactile and tactile training strategies to understand the importance in the order and the period of time when each one is applied. Data analysis shows the importance of combining visual and tactile feedbacks to obtain the best results in the improvements of the user skills.

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.