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Construction method of video detection model for Parkinson's disease bradykinesia based on deep neural network

一种深度神经网络、运动迟缓的技术，应用在帕金森病运动迟缓视频检测模型的构建领域，能够解决限制受测试者活动范围等问题，达到实施简单方便的效果

Active Publication Date: 2020-07-17
ZHEJIANG UNIV
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Problems solved by technology

[0005] Due to issues of data transmission, battery power supply, and equipment cost, this type of contact sensor-based solution limits the range of motion of the subject and has certain limitations

Method used

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  • Construction method of video detection model for Parkinson's disease bradykinesia based on deep neural network
  • Construction method of video detection model for Parkinson's disease bradykinesia based on deep neural network
  • Construction method of video detection model for Parkinson's disease bradykinesia based on deep neural network

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Embodiment Construction

[0036] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0037] like figure 1 Shown, the construction method of the Parkinson's disease slow motion video detection model based on deep neural network of the present invention, comprises the steps:

[0038] Step 1: Data Acquisition.

[0039] The present invention only needs a smart phone and a pair of tripods as data collection equipment. The smartphone model used in this embodiment is iPhone 6s Plus, which supports a sampling frequency of 240 frames per second and video recording with a resolution of 1280×720 pixels. Use a tripod to fix the smartphone to avoid lens shake during the recording process, and place it about 50-70 cm in front of the patient; adjust the height of the tripod so that the palm is in the center of the viewfinder.

[0040] During the vide...

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Abstract

The invention discloses a construction method of a video detection model of Parkinson's disease motion retardation based on a deep neural network, which combines computer vision and deep learning technology, evaluates the motion pattern of a test subject through short videos, and judges that there is motion Possibility of delayed symptoms. The present invention considers both the movement behavior and the movement process, proposes a detailed data collection method and a movement trajectory definition method, and designs three novel metrics and a periodic movement network model PMNet to deal with the problem of bradykinesia symptom judgment. Different from the traditional method, the present invention has the characteristics of scalability and portability. The key point extraction method based on the convolutional neural network can be replaced by other more accurate models, and more features can be added to describe the motion behavior. In addition, the present invention is capable of performing other similar periodic motion assessments, such as finger tapping actions of item 3.4 of the MDS-UPDRS scale.

Description

technical field [0001] The invention belongs to the technical field of computer vision applications, and in particular relates to a method for constructing a Parkinson's disease slow motion video detection model based on a deep neural network. Background technique [0002] Parkinson's disease is a common degenerative disease of the central nervous system, which mainly affects the human motor system. Its clinical manifestations mainly include resting tremor, bradykinesia, muscle rigidity, and posture and gait disorders. At the same time, patients may be accompanied by depression and constipation. and non-motor symptoms such as sleep disturbance. Clinicians and related scholars have designed many standards for these clinical symptoms to help doctors make a comprehensive assessment, such as the Hoehn-Yahr scale, the British Queen Square brain bank standard, the Montreal Cognitive Assessment Scale, and the Unified Parkinson's Disease Rating Scale ( UPDRS) etc. Currently, the m...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/46G06K9/62G06N3/08G16H50/20
CPCG06N3/08G16H50/20G06V10/462G06F18/21
Inventor 尹建伟林博张金迪罗巍罗智凌邓水光李莹
Owner ZHEJIANG UNIV
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