Quantitative measurement of motor symptoms in Parkinson's disease: A study with full-body motion capture data

Das, Samarjit; Trutoiu, Laura; Murai, Akihiko; Alcindor, Dunbar; Oh, Michael; Torre, Fernando De la; Hodgins, Jessica K.

doi:10.1109/iembs.2011.6091674

Cited by 66 publications

(39 citation statements)

References 11 publications

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“…Clinically, systems for kinematic and kinetic analysis are used for the diagnosis of disease and illness, such as the severity of symptoms in Parkinson's disease [30]- [33], assessment of patient recovery from treatment, such as the outcomes of training schemes for patient rehabilitation [34], and control of prostheses through identification of movement intention [35]- [37].…”

Section: B Applicationsmentioning

confidence: 99%

Wearable Sensing for Solid Biomechanics

Wong

Zhang

et al. 2015

IEEE Sensors J.

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Abstract-Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in healthcare, sport, wellbeing, and workflow analysis. Conventional laboratorybased biomechanical analysis systems and observation-based tests are only designed to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing upon sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations that can affect the accuracy and robustness of existing methods, and different methods for reducing the impact of these sources of errors are described in this review. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, as well as the future direction of sensing for biomechanics are also discussed.

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Section: B Applicationsmentioning

confidence: 99%

Wearable Sensing for Solid Biomechanics

Wong

Zhang

et al. 2015

IEEE Sensors J.

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“…Sensor systems have typically concentrated on high frequency optical tracking for precision and have had achieved success in measuring important health conditions such as cerebral palsy and Parkinsons disease [7], [8]. Increasingly, there has been interest in the use of MEMS accelerometers, as opposed to optical, due to a greater potential for the portability and deployment of data capture.…”

Section: A Related Workmentioning

confidence: 99%

Inertial sensing for human motor control symmetry in injury rehabilitation

Field

Stirling

Ros

et al. 2013

2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics

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This paper proposes a series of methods for representing changes in human motion during injury rehabilitation using Micro-Electro-Mechanical Systems (MEMS) inertial sensors. Tracking the changes over a recovery period requires methods for evaluating the similarity of movement in an impaired state against a non-impaired state. We investigate the use of motion analyses such as the centre of mass (COM) tipping distance, the variance of joint velocity eigenvalues and the cumulative state changes of Gaussian mixture models (GMM) for monitoring the symmetry between the left and right sides of body during rehabilitation exercises. The methods are tested on an injured athlete over 4 months of recovery from an ankle operation and validated by comparing the observed improvement to the variation among a group of uninjured subjects. The results indicate that gradual changes are detected in the motion symmetry, thus providing quantitative measures to aid clinical decisions. Abstract-This paper proposes a series of methods for representing changes in human motion during injury rehabilitation using Micro-Electro-Mechanical Systems (MEMS) inertial sensors. Tracking the changes over a recovery period requires methods for evaluating the similarity of movement in an impaired state against a non-impaired state. We investigate the use of motion analyses such as the centre of mass (COM) tipping distance, the variance of joint velocity eigenvalues and the cumulative state changes of Gaussian mixture models (GMM) for monitoring the symmetry between the left and right sides of body during rehabilitation exercises. The methods are tested on an injured athlete over 4 months of recovery from an ankle operation and validated by comparing the observed improvement to the variation among a group of uninjured subjects. The results indicate that gradual changes are detected in the motion symmetry, thus providing quantitative measures to aid clinical decisions.

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“…Indeed, this progressively degenerative movement disorder [16] implies quantitative evaluations of hand movement as well as hand tremor for neurological examination [17]. These measurements could be based on the visual tracking of patient's hand motion [18] or on the paced finger tapping test [19] assessed with visual rating scales such as the Unified Parkinson's Disease Rating Scale (UPDRS), rather than on the traditional invasive methods such as electroencephalograms (EEGs) [20].…”

Section: Introductionmentioning

confidence: 99%

HD: Efficient Hand Detection and Tracking

Rouge

Shaikh

Olszewska

2016

Annals of Computer Science and Information Systems

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Abstract-Automated hand detection is useful for applications requiring reliable hand posture and hand gesture processing. Such applications include human-computer/human-robot interfaces for rehabilitation, serious games, or non-invasive medical diagnosis. Hence, in this paper, we focus on the design and development of a robust and fast hand detection and tracking (HD) system. The design of our HD system involved the study of the human skin colour and people's foreground properties, in order to merge these information for an efficient hand detection and tracking. Experiments have been carried out in real-world environment and have demonstrated the excellent performance of our HD system.

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Quantitative measurement of motor symptoms in Parkinson's disease: A study with full-body motion capture data

Cited by 66 publications

References 11 publications

Wearable Sensing for Solid Biomechanics

Wearable Sensing for Solid Biomechanics

Inertial sensing for human motor control symmetry in injury rehabilitation

HD: Efficient Hand Detection and Tracking

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