| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 謝兆峰 | zh_TW |
| DC.creator | Chao-Feng Hsieh | en_US |
| dc.date.accessioned | 2023-7-26T07:39:07Z | |
| dc.date.available | 2023-7-26T07:39:07Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=110522153 | |
| dc.contributor.department | 資訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 在職業球賽中，碰撞和衝突是不可避免的，這很容易導致創傷性腦損
傷，甚至危及生命。這些腦震盪症狀可能會嚴重影響運動員的職業生涯
和發展，而後遺症可能導致永久性損傷。因此，一種即時且有效的測試
或檢測方法是必要的工具。過去大多數的測試方法相對主觀，通常需要
運動員自行報告，這可能易於操縱。過去可以使用視覺誘發電位（VEP）
測試人腦的視覺功能，但需要一個安靜和無干擾的環境來收集數據。腦
震盪患者通常在某些腦部區域有頭部受傷。隨著虛擬現實（VR）和機器
學習（ML）的快速發展，我們利用VR 頭戴式設備創造出一個沉浸式環
境，這個環境可以創造一個無干擾的封閉環境，以更可靠地從患者收集
數據。多模態融合方法可以檢測腦震盪症狀並對應不同的神經功能進行
全面評估，使用腦電圖（EEG）和眼動追蹤傳感器。最近，機器學習和
深度學習方法在發現腦震盪症狀方面應用較少，因此我們進一步研究這
些先進技術。我們提出了一種新穎的腦震盪檢測系統，結果在我們的模
擬數據集中實現了0.95 的準確率。 | zh_TW |
| dc.description.abstract | In professional ball games, collisions and conflicts are inevitable, which
can easily cause traumatic brain injuries and even life-threatening injuries.
These concussion symptoms can seriously impact a player’s career and
development, and the sequelae can cause permanent damage. Therefore,
an immediate and effective testing or detection method is a necessary tool.
Most of the testing methods in the past were relatively subjective, and they
usually need a self-report for players themselves, which may cause easily
be manipulated. In the past, a visual evoked potential (VEP) can be used
to test the visual function of the human brain but needed a silent and
interference-free environment to collect data. Concussion patients often
have head injuries in some brain sections. With the rapid development of
Virtual Reality (VR) and Machine Learning (ML), we utilize a VR headset
to create an immersive environment that creates an enclosed environment
that is interference-free in order to collect data more reliably from patients.
Multi-modal fusion methods can detect concussion symptoms and make a
comprehensive assessment for different brain sections corresponding with
different neurological functions with EEG and eye-tracking sensors. Recently,
ML and DL methods are less applied to find concussion symptoms,
then we further investigate these with advanced techniques. We propose a novel concussion detection system and the result can achieve an accuracy
of 0.95 in our simulation dataset. | en_US |
| DC.subject | 腦震盪 | zh_TW |
| DC.subject | 機器學習 | zh_TW |
| DC.subject | 融合方法 | zh_TW |
| DC.subject | 虛擬現實 | zh_TW |
| DC.subject | 輕度創傷性腦損傷 | zh_TW |
| DC.subject | 穩態視覺誘發電位 | zh_TW |
| DC.subject | 眼動追蹤 | zh_TW |
| DC.subject | Concussion | en_US |
| DC.subject | Machine Learning | en_US |
| DC.subject | Fusion strategy | en_US |
| DC.subject | Virtual Reality | en_US |
| DC.subject | Mild Traumatic Brain Injury(mTBI) | en_US |
| DC.subject | SSVEP | en_US |
| DC.subject | Eye Tracking | en_US |
| DC.title | 基於深度學習的虛擬現實腦震盪檢測與融合方法 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Deep-Learning-based Virtual Reality Concussion Detection with Fusion Method | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |