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      Improving Movement Analysis in Physical Therapy Systems Based on Kinect Interaction

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      Proceedings of the 31st International BCS Human Computer Interaction Conference (HCI 2017) (HCI)

      digital make-believe, with delegates considering our expansive

      3 - 6 July 2017

      Movement Analysis, Kinect, Time Series Classification, Hidden Markov Models, Dynamic Time Warping

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          Abstract

          This study uses machine learning methods to analyse Kinect body gestures involved in the user interaction with exergaming systems designed for physical rehabilitation. We propose a method to improve gesture recognition accuracy and motion analysis, by extracting from the full body motion data recorded by the Kinect sensor three important features which are relevant to physical therapy exercises: body posture, movement trajectory and range of motion. By applying the Hidden Markov Model (HMM) and Dynamic Time Warping (DTW) algorithms, we obtained an improved accuracy by selecting specific features from the public UTD-MHAD full body gestures database (with up to 56% for HMM and 32% for DTW). Preliminary results show a positive correlation between the movement amplitude and the envelope feature (r = 0.92). Thus, this approach has the potential to improve gesture recognition accuracy and provide user feedback on how to improve the movement performed, in particular the movement amplitude. We propose further improvements and method validations to be the basis of creating an intelligent virtual rehabilitation assistant.

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          Most cited references 9

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          UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor

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            Compensation or Restoration: Closed-Loop Feedback of Movement Quality for Assisted Reach-to-Grasp Exercises with a Multi-Joint Arm Exoskeleton

            Assistive technology allows for intensive practice and kinematic measurements during rehabilitation exercises. More recent approaches attach a gravity-compensating multi-joint exoskeleton to the upper extremity to facilitate task-oriented training in three-dimensional space with virtual reality feedback. The movement quality, however, is mostly captured through end-point measures that lack information on proximal inter-joint coordination. This limits the differentiation between compensation strategies and genuine restoration both during the exercise and in the course of rehabilitation. We extended in this proof-of-concept study a commercially available seven degree-of-freedom arm exoskeleton by using the real-time sensor data to display a three-dimensional multi-joint visualization of the user's arm. Ten healthy subjects and three severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living assisted by the attached exoskeleton and received closed-loop online feedback of the three-dimensional movement in virtual reality. Patients in this pilot study differed significantly with regard to motor performance (accuracy, temporal efficiency, range of motion) and movement quality (proximal inter-joint coordination) from the healthy control group. In the course of 20 training and feedback sessions over 4 weeks, these pathological measures improved significantly toward the reference parameters of healthy participants. It was moreover feasible to capture the evolution of movement pattern kinematics of the shoulder and elbow and to quantify the individual degree of natural movement restoration for each patient. The virtual reality visualization and closed-loop feedback of joint-specific movement kinematics makes it possible to detect compensation strategies and may provide a tool to achieve the rehabilitation goals in accordance with the individual capacity for genuine functional restoration; a proposal that warrants further investigation in controlled studies with a larger cohort of stroke patients.
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              Segmenting Human Motion for Automated Rehabilitation Exercise Analysis

               J. Lin,  D. KULIC (2012)
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                Author and article information

                Contributors
                Conference
                July 2017
                July 2017
                : 1-5
                Affiliations
                Department of Computer Science

                Babeş-Bolyai University

                1 Mihail Kogalniceanu Street

                RO-400084 Cluj-Napoca, Romania
                Article
                10.14236/ewic/HCI2017.87
                © Călin et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2017 – Digital Make-Believe. Sunderland, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 31st International BCS Human Computer Interaction Conference (HCI 2017)
                HCI
                31
                Sunderland, UK
                3 - 6 July 2017
                Electronic Workshops in Computing (eWiC)
                digital make-believe, with delegates considering our expansive
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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