Using real-time acceleration data for exercise movement training with a decision tree approach

Chen, Yin-Jun; Hung, Yen-Chu

doi:10.1016/j.eswa.2010.04.089

Cited by 17 publications

(8 citation statements)

References 11 publications

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“…An example for terminal feedback can be found in (Chen & Hung, 2010) where the focus is put on the correct classification of motion errors by using a decision tree approach to determine an appropriate verbal feedback phrase. This phrase (e.g.…”

Section: Results Gained In Previous Motionmentioning

confidence: 99%

Towards an Automatic Motion Coaching System - Feedback Techniques for Different Types of Motion Errors

Ukita

Kaulen

Röcker

2014

Proceedings of the International Conference on Physiological Computing Systems

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Abstract:The development of a widely applicable automatic motion coaching system requires one to address a lot of issues including motion capturing, motion analysis and comparison, error detection as well as error feedback. In order to cope with this complexity, most existing approaches focus on a specific motion sequence or exercise. As a first step towards the development of a more generic system, this paper systematically analyzes different error and feedback types. A prototype of a feedback system that addresses multiple modalities is presented. The system allows to evaluate the applicability of the proposed feedback techniques for arbitrary types of motions in a next step.

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Section: Results Gained In Previous Motionmentioning

confidence: 99%

Towards an Automatic Motion Coaching System - Feedback Techniques for Different Types of Motion Errors

Ukita

Kaulen

Röcker

2014

Proceedings of the International Conference on Physiological Computing Systems

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“…The offline nature was pertinent in their case given the purpose of their application was to allow a patient to learn how to perform exercises correctly at the hospital, and later execute them at their home without any therapist by their side. Other works have investigated exercise coaching with vision through a socially assistive robot coach [6], pre-recorded videos with automated coaching advice [7], the Wiimotes attached to limbs for movement-training exercises [8], and wearable sensing with iPods for tracking home balance exercises [9].…”

Section: Related Workmentioning

confidence: 99%

Synchronous Big Data analytics for personalized and remote physical therapy

Calyam

Mishra

Antequera

et al. 2016

Pervasive and Mobile Computing

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With gigabit networking becoming economically feasible and widely installed at homes, there are new opportunities to revisit in-home, personalized telehealth services. In this paper, we describe a novel telehealth eldercare service that we developed viz., "PhysicalTherapy-as-a-Service" (PTaaS) that connects a remote physical therapist at a clinic to a senior at home. The service leverages a high-speed, low-latency network connection through an interactive interface built on top of Microsoft Kinect motion sensing capabilities. The interface that is built using user-centered design principles for wellness coaching exercises is essentially a 'Synchronous Big Data' application due to its: (i) high data-in-motion velocity (i.e., peak data rate is ≈400 Mbps), (ii) considerable variety (i.e., measurements include 3D sensing, network health, user opinion surveys and video clips of RGB, skeletal and depth data), and (iii) large volume (i.e., several GB of measurement data for a simple exercise activity). The successful PTaaS delivery through this interface is dependent on the veracity analytics needed for correlation of the real-time Big Data streams within a session, in order to assess exercise balance of the senior without any bias due to network quality effects. Our experiments with PTaaS in an actual testbed involving senior homes in Kansas City with Google Fiber connections and our university clinic demonstrate the network configuration and time synchronization related challenges in order to perform online analytics. Our findings provide insights on how to: (a) enable suitable resource calibration and perform network troubleshooting for high user experience for both the therapist and the senior, and (b) realize a Big Data architecture for PTaaS and other similar personalized healthcare services to be remotely delivered at a large-scale in a reliable, secure and cost-effective manner.

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“…There have been methods of learning skills using motion capture technology [1,2] and inertial sensors have been employed [3]. This researches has verified what kind of support is effective to enable the choreography to be practiced.…”

Section: Introductionmentioning

confidence: 95%

A system for practicing formations in dance performance supported by self-propelled screen

Tsuchida

Terada

Tsukamoto

2013

Proceedings of the 4th Augmented Human International Conference

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Collapsed formation in a group dance will greatly reduce the quality of the performance even if the dance in the group is synchronized with music. Therefore, learning the formation of a dance in a group is as important as learning its choreography. However, if someone cannot participate in practice, it is difficult for the rest of the members to gain a sense of the proper formation in practice. We propose a practicesupport system for performing the formation smoothly using a self-propelled screen even if there is no dance partner. We developed a prototype of the system and investigated whether a sense of presence provided by both methods of practicing formations was close to the sense we really obtain when we dance with humans. The result verified that the sense of dancing with a projected video was closest to the sense of dancing with a dancer, and the trajectory information from dancing with a self-propelled robot was close to the trajectory information from dancing with a dancer. Practicing in situations similar to real ones is able to be done by combining these two methods. Furthermore, we investigated whether the self-propelled screen obtained the advantages of dancing with both methods and found that it only obtained advantages of dancing with projected video.

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Using real-time acceleration data for exercise movement training with a decision tree approach

Cited by 17 publications

References 11 publications

Towards an Automatic Motion Coaching System - Feedback Techniques for Different Types of Motion Errors

Towards an Automatic Motion Coaching System - Feedback Techniques for Different Types of Motion Errors

Synchronous Big Data analytics for personalized and remote physical therapy

A system for practicing formations in dance performance supported by self-propelled screen

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