It is generally accepted that augmented feedback, provided by a human expert or a technical display, effectively enhances motor learning. However, discussion of the way to most effectively provide augmented feedback has been controversial. Related studies have focused primarily on simple or artificial tasks enhanced by visual feedback. Recently, technical advances have made it possible also to investigate more complex, realistic motor tasks and to implement not only visual, but also auditory, haptic, or multimodal augmented feedback. The aim of this review is to address the potential of augmented unimodal and multimodal feedback in the framework of motor learning theories. The review addresses the reasons for the different impacts of feedback strategies within or between the visual, auditory, and haptic modalities and the challenges that need to be overcome to provide appropriate feedback in these modalities, either in isolation or in combination. Accordingly, the design criteria for successful visual, auditory, haptic, and multimodal feedback are elaborated.

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Augmented visual, auditory, haptic, and multimodal feedback in motor learning: A review

Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems 参加報告

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas.

/pdf/a-survey-on-mobile-crowdsensing-systems-challenges-solutions-224oot2vez.pdf

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

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).

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

As robotic devices are applied to problems beyond traditional manufacturing and industrial settings, we find that interaction between robots and humans, especially physical interaction, has become a fast developing field. Consider the application of robotics in healthcare, where we find telerobotic devices in the operating room facilitating dexterous surgical procedures, exoskeletons in the rehabilitation domain as walking aids and upper-limb movement assist devices, and even robotic limbs that are physically integrated with amputees who seek to restore their independence and mobility. In each of these scenarios, the physical coupling between human and robot, often termed physical human robot interaction (pHRI), facilitates new human performance capabilities and creates an opportunity to explore the sharing of task execution and control between humans and robots. In this review, we provide a unifying view of human and robot sharing task execution in scenarios where collaboration and cooperation between the two entities are necessary, and where the physical coupling of human and robot is a vital aspect. We define three key themes that emerge in these shared control scenarios, namely, intent detection, arbitration, and feedback. First, we explore methods for how the coupled pHRI system can detect what the human is trying to do, and how the physical coupling itself can be leveraged to detect intent. Second, once the human intent is known, we explore techniques for sharing and modulating control of the coupled system between robot and human operator. Finally, we survey methods for informing the human operator of the state of the coupled system, or the characteristics of the environment with which the pHRI system is interacting. At the conclusion of the survey, we present two case studies that exemplify shared control in pHRI systems, and specifically highlight the approaches used for the three key themes of intent detection, arbitration, and feedback for applications of upper limb robotic rehabilitation and haptic feedback from a robotic prosthesis for the upper limb. [DOI: 10.1115/1.4039145]

A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human-Robot Interaction

A novel wearable system for the online assessment of risk for biomechanical load in repetitive efforts

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.

http://percro.sssup.it/~antony/papers/conferences/WorldHaptics2009.pdf

Vibrotactile perception assessment for a rowing training system

Wearable motion tracking systems have gained large popularity in the last decades because of their effectiveness in many fields, from performance assessment to human-robot interaction. Among all the approaches, those based on inertial sensors have been widely explored. Since inertial sensors are affected by measurements drift, they need to be aided by other sensors, thus requiring sensor measurements to be fused. The most used sensor fusion techniques are based on Kalman filter. In particular, the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are used because of the non linearity characterizing most of the models. They often aim at reconstructing human motion by estimating limbs orientation, involving human's kinematics to constrain relative motion of the limbs. These models often neglect part of the degrees of freedom (DoFs) that characterize human upper limbs, especially when modeling humerus motion with respect to the chest. In this paper we present a novel 7 DoFs model which represents a trade-off between modeling accuracy and complexity for the human upper limb. In particular, we model the human shoulder girdle taking into account also the humerus head's elevation and the retraction due to the scapula's and the clavicle's motions. The model exploits inertial sensors measurements by means of an Unscented Kalman filter to reconstruct human movements. The system performance is validated firstly against a reconstruction based on an optical tracking system. Secondly, the 5 DoFs model extracted form the 7 DoFs one was checked to have state of the art performance and used to estimate the improvement of position estimation that are obtained by extending the model to 7 DoFs.

A novel 7 degrees of freedom model for upper limb kinematic reconstruction based on wearable sensors

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 (ie 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

Alessandro Filippeschi

Papers

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

A novel wearable system for the online assessment of risk for biomechanical load in repetitive efforts

Vibrotactile perception assessment for a rowing training system

A novel 7 degrees of freedom model for upper limb kinematic reconstruction based on wearable sensors

Energy management using virtual reality improves 2000-m rowing performance