Power Spectrum Entropy of Acceleration Time-series during Movement as an Indicator of Smoothness of Movement

Kojima, Motonaga; Obuchi, Shuichi; Mizuno, Kousuke; Henmi, Osamu; Ikeda, Noriaki

doi:10.2114/jpa2.27.193

Cited by 19 publications

(20 citation statements)

References 12 publications

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“…Smoothness of Movement (SM) Common measures of smoothness of movement are jerk measures [40] or spectrum-based measures [41]. It was found that it is important to use a smoothness measure that is dimensionless [40].…”

Section: Methodsmentioning

confidence: 99%

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

Strohrmann

Labruyère

Gerber

et al. 2013

Journal of NeuroEngineering and Rehabilitation

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BackgroundRehabilitation services use outcome measures to track motor performance of their patients over time. State-of-the-art approaches use mainly patients’ feedback and experts’ observations for this purpose. We aim at continuously monitoring children in daily life and assessing normal activities to close the gap between movements done as instructed by caregivers and natural movements during daily life. To investigate the applicability of body-worn sensors for motor assessment in children, we investigated changes in movement capacity during defined motor tasks longitudinally.MethodsWe performed a longitudinal study over four weeks with 4 children (2 girls; 2 diagnosed with Cerebral Palsy and 2 with stroke, on average 10.5 years old) undergoing rehabilitation. Every week, the children performed 10 predefined motor tasks. Capacity in terms of quality and quantity was assessed by experts and movement was monitored using 10 ETH Orientation Sensors (ETHOS), a small and unobtrusive inertial measurement unit. Features such as smoothness of movement were calculated from the sensor data and a regression was used to estimate the capacity from the features and their relation to clinical data. Therefore, the target and features were normalized to range from 0 to 1.ResultsWe achieved a mean RMS-error of 0.15 and a mean correlation value of 0.86 (p<0.05 for all tasks) between our regression estimate of motor task capacity and experts’ ratings across all tasks. We identified the most important features and were able to reduce the sensor setup from 10 to 3 sensors. We investigated features that provided a good estimate of the motor capacity independently of the task performed, e.g. smoothness of the movement.ConclusionsWe found that children’s task capacity can be assessed from wearable sensors and that some of the calculated features provide a good estimate of movement capacity over different tasks. This indicates the potential of using the sensors in daily life, when little or no information on the task performed is available. For the assessment, the use of three sensors on both wrists and the hip suffices. With the developed algorithms, we plan to assess children’s motor performance in daily life with a follow-up study.

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Section: Methodsmentioning

confidence: 99%

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

Strohrmann

Labruyère

Gerber

et al. 2013

Journal of NeuroEngineering and Rehabilitation

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“…We assume

as the discrete Fourier transform of the vibration signal

( N is the number of samples). Then the power spectrum of

[ 33 ]. According to the energy conservation law, we set:

…”

Section: Descriptions Of Information Entropy Featuresmentioning

confidence: 99%

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network

Jiang

Shen

et al. 2018

Sensors

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Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

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“…Triaxial accelerometers are small and inexpensive and allow easy measurement without any limitation of the measurement environment. Kojima et al proposed a method for evaluating the smoothness of movement by assessing entropy, based on the spectrum changes of the acceleration time-series during movements 18 ) . Entropy is a measure that estimates the amount of information and is a fundamental concept in the theory of information and communication proposed by Shannon 19 , 20 ) .…”

Section: Introductionmentioning

confidence: 99%

Relationship between correcting error and smoothness of movement in the process of motor learning

et al. 2020

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[Purpose] Improvement in the smoothness of movement is a motor learning outcome. This study sought to clarify the relationship between motor skills and smoothness of movement in motor learning. [Participants and Methods] We subjected 12 healthy adults to a task in which they had to learn the sensation of a load while standing up and sitting down. We attached triaxial accelerometers to the seventh cervical spine and the third lumbar spinous process of the participants prior to measurement. We took the measurements over two successive days and used absolute error and variable error as indicators of motor learning outcomes. In addition, we used entropy, calculated from the results of the power spectrum analysis of acceleration changes, as an indicator of the smoothness of the movement. [Results] In the test sessions, absolute and variable errors showed a significant reduction. Entropy also showed a similarly significant decrease, although the change in errors and entropy showed different transitions. [Conclusion] Qualitative indicators of motor learning captured an aspect of motor learning that one cannot capture by quantitative indicators. In the future, qualitative indicators will be necessary to judge the outcomes of motor learning.

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Power Spectrum Entropy of Acceleration Time-series during Movement as an Indicator of Smoothness of Movement

Cited by 19 publications

References 12 publications

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network

Relationship between correcting error and smoothness of movement in the process of motor learning

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