Body movement awareness as a path to expression in violin playing

2017 ◽  
Author(s):  
Sara Balasch Lozano
Keyword(s):  
Body Movement ◽  
Movement Awareness ◽  
Violin Playing

Book project: Body, movement and mental health - a physiotherapy perspective

2012 ◽  
Author(s):  
Riitta Keskinen Rosenqvist ◽  
Gabriele Biguet ◽  
Adrienne Levy-Berg
Keyword(s):  
Mental Health ◽  
Body Movement

UPAYA PENINGKATAN KEMAMPUAN MOTORIK KASAR ANAK DOWN SYNDROME DENGAN OLAHRAGA BOLA KAKI DI GOLDEN KIDS

2019 ◽  
Vol 1 (2) ◽  
pp. 139-151
Author(s):  
Sinto Robindo ◽  
Melda Rumia Rosmeri Simorangkir
Keyword(s):  
Spinal Cord ◽  
Down Syndrome ◽  
Cognitive Abilities ◽  
Body Movement ◽  
Movement Control ◽  
Fine Motor ◽  
Educational Goals ◽  
Motor Movement ◽  
Brain And Spinal Cord ◽  
Syndrome Child

ABSTRACT All aspects of development are very important in a person's life where the development of cognition, affection and psychomotor is well developed in accordance with its development, these three aspects can be said to be good and successful if the three aspects develop well. Like wise with the psychomotor aspect where between gross motor and fine motor are also balanced. Motoric is the development of coordinated body movement control between nerves, brain, and spinal cord (spinal cord or spinal cord). Child's gross motorization can be optimized by improving his motor movement coordination skills through physical activity in the form of coordination of body movements. Like throwing, catching, kicking, running, melopat, and maintaining balance. The condition of a Down Syndrome child who experiences weakness in the ability to think will affect in all aspects of his life. Down syndrome children have problems in cognitive abilities, effective and self-care abilities. This results in them needing special education. Basically, the educational goals that children with Down Syndrome want to achieve are not different from those of education in general. Because Down Syndrome children themselves are born in the midst of society. Keywords: football sports, gross motoric, down syndrome

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

Depression Severity Is Related to Less Gross Body Movement: A Motion Energy Analysis

2021 ◽  
pp. 1-7
Author(s):  
Anna Sandmeir ◽  
Désirée Schoenherr ◽  
Uwe Altmann ◽  
Christoph Nikendei ◽  
Henning Schauenburg ◽  
...  
Keyword(s):  
Depressive Disorder ◽  
Energy Analysis ◽  
Rating Scale ◽  
Body Movement ◽  
Psychomotor Retardation ◽  
Diagnostic Interview ◽  
Depression Severity ◽  
Depressed Patients ◽  
Motion Energy ◽  
Objectively Measured

Psychomotor retardation is a well-known clinical phenomenon in depressed patients that can be measured in various ways. This study aimed to investigate objectively measured gross body movement (GBM) during a semi-structured clinical interview in patients with a depressive disorder and its relation with depression severity. A total of 41 patients with a diagnosis of depressive disorder were assessed both with a clinician-rated interview (Hamilton Depression Rating Scale) and a self-rating questionnaire (Beck Depression Inventory-II) for depression severity. Motion energy analysis (MEA) was applied on videos of additional semi-structured clinical interviews. We considered (partial) correlations between patients’ GBM and depression scales. There was a significant, moderate negative correlation between both measures for depression severity (total scores) and GBM during the diagnostic interview. However, there was no significant correlation between the respective items assessing motor symptoms in the clinician-rated and the patient-rated depression severity scale and GBM. Findings imply that neither clinician ratings nor self-ratings of psychomotor symptoms in depressed patients are correlated with objectively measured GBM. MEA thus offers a unique insight into the embodied symptoms of depression that are not available via patients’ self-ratings or clinician ratings.

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