結合多關節慣性測量單元同步訊號比較運動疲勞前後 對下肢動作模式之影響

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趙珮筑(PEI-ZHU ZHAO) 查詢紙本館藏

畢業系所

生醫科學與工程學系

論文名稱

結合多關節慣性測量單元同步訊號比較運動疲勞前後對下肢動作模式之影響
(Using Multi-joint Synchronized Signals from Inertial Measurement Unit to Compare the Effects of Fatigue on Lower Limb Movement Patterns Before and After Exercise)

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至系統瀏覽論文 (2027-7-23以後開放)

摘要(中)

運動帶給身體好處的同時也可能帶來傷害，物理性疲勞是其中一個誘發傷害產生的原因，其受傷機制包括神經肌肉控制、本體感覺、姿勢控制和運動協調性的改變，這些改變會影響動作模式，因此，如果可以在運動過程中即時發現有害的異常表現將有助於傷害的預防。
本實驗利用慣性測量單元(Inertial measurement unit, IMU）設計一種穿戴式裝置，在下肢多個關節放上感測點，記錄受試者動作中同步訊號，受試者為19歲到44歲的健康成年人，利用強度不同的有氧運動誘發疲勞，最後，將運動前後收集的訊號進行分析，找出最佳測量點與異常動作模式。
根據實驗，當達到疲勞強度時，跳躍高度與頻譜在疲勞前後均有顯著差異。對於IMU感測點放置的選擇，對於跳躍高度變化的測量，腳踝(p = 0.001)和股骨(p = 0.001)是相對較佳的選擇；對於頻譜變化的測量，膝蓋 (p = 0.047)是相對較佳的選擇。
希望透過穿戴式裝置收集同步訊號，分析潛在疲勞特徵，對疲勞進行管理與偵測，讓訓練員和運動者對會導致受傷的異常動作模式有更高的感知能力，有助於未來運動計畫的調整並防止過度訓練造成的損傷。

摘要(英)

While exercise is beneficial to the body, it can also cause injuries. One reason for these injuries is physical fatigue, which affects mechanisms such as neuromuscular control, proprioception, posture control, and movement coordination. These changes can alter movement patterns. Therefore, detecting abnormal performance during exercise in real-time can help prevent injuries.
In this experiment, we designed a wearable device using inertial measurement units (IMUs). Sensors were placed on multiple joints of the lower limbs to record the synchronized signals. Participants were healthy adults aged 19 to 44. Fatigue was induced through aerobic exercises of varying intensity, and the signals were analyzed to identify the optimal measurement points and abnormal movement patterns.
According to the experiment, when participants reached fatigue intensity, there was a significant difference in jump height and frequency spectrum before and after fatigue. Regarding the choice of sensing point placement, for the measurement of jump height changes, ankle(p = 0.001) and femur(p = 0.001) are relatively better choices. For the measurement of spectrum changes, knee(p = 0.047) is relatively better choices.
We hope to use wearable devices to collect synchronized signals and analyze potential fatigue characteristics, so that we can manage and detect fatigue. Furthermore, Trainers and athletes will enhance the awareness of abnormal movement patterns that may lead to injuries. Finally, The ultimate goal is to prevent overtraining injuries by adjusting exercise program.

關鍵字(中)

★ 慣性測量單元
★ 疲勞
★ 動作模式
★ 同步訊號

關鍵字(英)

★ Inertial measurement unit
★ Fatigue
★ Movement patterns
★ Synchronized Signals

論文目次

中文摘要 ………………………………………………………………………… i
英文摘要 ………………………………………………………………………… ii
誌謝 ………………………………………………………………………… iii
目錄 ………………………………………………………………………… iv
圖目錄 ………………………………………………………………………… viii
表目錄 ………………………………………………………………………… x
一、緒論…………………………………………………………………… 1
1-1 序言…………………………………………………………… 1
1-1-1 研究動機……………………………………………… 1
1-1-2 研究目的……………………………………………… 2
1-1-3 研究假設……………………………………………… 2
1-1-4 研究範圍……………………………………………… 2
1-1-5 名詞解釋……………………………………………… 3
1-2 本文架構……………………………………………………… 3
二、文獻探討……………………………………………………………… 4
2-1 疲勞對動作表現的影響……………………………………… 4
2-1-1 疲勞對姿勢控制的影響……………………………… 5
2-1-2 疲勞對跳躍的影響…………………………………… 5
2-1-3 疲勞可能造成的運動傷害…………………………… 6
2-2 疲勞與運動強度……………………………………………… 6
2-3 跳躍過程中常用的研究參數………………………………… 8
2-4 IMU與疲勞偵測…………………………………………… 10
2-5 文獻總結……………………………………………………… 12
三、研究內容與方法……………………………………………………… 13
3-1 研究對象……………………………………………………… 13
3-2 研究設備與工具……………………………………………… 14
3-2-1 慣性測量單元………………………………………… 14
3-2-2 心電圖測量儀………………………………………… 15
3-2-3 穿戴式裝置…………………………………………… 15
3-2-4 手機…………………………………………………… 17
3-2-5 階梯踏板……………………………………………… 17
3-2-6 基本資料量表………………………………………… 18
3-3 實驗設計……………………………………………………… 18
3-4 實驗日期與地點……………………………………………… 19
3-5 實驗流程……………………………………………………… 19
3-6 統計分析……………………………………………………… 25
四、研究結果……………………………………………………………… 25
4-1 受試者基本資料分析………………………………………… 25
4-2 IMU測量點分析…………………………………………… 27
4-2-1 以跳躍高度作為疲勞特徵…………………………… 27
4-2-2 以頻譜作為疲勞特徵………………………………… 31
五、結論…………………………………………………………………… 34
5-1 結論…………………………………………………………… 34
5-1-1 以跳躍高度作為疲勞特徵…………………………… 35
5-1-2 以頻譜作為疲勞特徵………………………………… 36
5-2 研究限制……………………………………………………… 38
六、未來展望……………………………………………………………… 38
6-1 人工智慧……………………………………………………… 38
6-2 精準醫療……………………………………………………… 39
參考文獻 ………………………………………………………………………… 41
附錄一受試者基本資料……………………………………………………… 46

參考文獻

[1] Camilla De Bleecker et al., “Relationship Between Jump-Landing Kinematics and Lower Extremity Overuse Injuries in Physically Active Populations: A Systematic Review and Meta-Analysis.”, Sports Medicine, vol. 50, pp.1515-1532, Jun. 2020
[2] Jessica Heil et al., “The Influence of Exercise-Induced Fatigue on Inter-Limb Asymmetries: a Systematic Review.”, Sports Med-Open, vol.6, Aug. 2020
[3] Jo Verschueren et al., “Does Acute Fatigue Negatively Affect Intrinsic Risk Factors of the Lower Extremity Injury Risk Profile? A Systematic and Critical Review.”, Sports Medicine, vol. 50, pp 767-784, Apr. 2020
[4] Marc Dauty et al., “Anatomical and Neuromuscular Factors Associated to Non-Contact Anterior Cruciate Ligament.”, InjuryJournal of Clinical Medicine 11, no. 5, Mar. 2022
[5] Claudia Alba-Jiménez et al., “Trends Assessing Neuromuscular Fatigue in Team Sports: A Narrative Review.”, Sports, vol. 10, Feb. 2022
[6] Han Wei Lem et al., “Single leg drop jump performance identifies functional deficit in collegiate athletes who have returned to sports after ACL reconstruction: A case-control study.”, Medicine, vol. 101, pp.49, Dec. 2022
[7] A.S. Jackson et al., “Estimating Maximum Heart Rate From Age: Is It a Linear Relationship?”, Medicine & Science in Sports & Exercise, vol. 39, pp 822-829, May. 2007
[8] Gebel Arnd et al., “Effects of Physical and Mental Fatigue on Postural Sway and Cortical Activity in Healthy Young Adults.” , Frontiers in human neuroscience, vol. 16, Jun. 2022
[9] Güler, Özkan et al., “Effects of Aerobic and Anaerobic Fatigue Exercises on Postural Control and Recovery Time in Female Soccer Players.”, International journal of environmental research and public health, vol. 17, Aug. 2020
[10] Mohsin S. Fidai et al., “Fatigue Increases Dynamic Knee Valgus in Youth Athletes: Results From a Field-Based Drop-Jump Test.”, Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association, vol. 36, pp.214-222, Jan. 2020
[11] Erika Zemková, “Physiological Mechanisms of Exercise and Its Effects on Postural Sway: Does Sport Make a Difference?.”, Frontiers in physiology, vol. 13, Feb. 2022
[12] Charis Tsarbou et al., “The effect of fatigue on jump height and the risk of knee injury after a volleyball training game: A pilot study”, Biomedical Human Kinetics, vol.13, pp.197–204, 2021
[13] Armada-Cortés et al., “Can We Rely on Flight Time to Measure Jumping Performance or Neuromuscular Fatigue-Overload in Professional Female Soccer Players?”, Applied Sciences, vol. 10, Jun. 2020
[14] Peimin Yu et al., “The Acute Influence of Running-Induced Fatigue on the Performance and Biomechanics of a Countermovement Jump.”, Applied Sciences, vol. 10, Jun. 2020
[15] Kotsifaki Argyro et al., “Single leg vertical jump performance identifies knee function deficits at return to sport after ACL reconstruction in male athletes.”, Br J Sports Med, vol.56, pp. 490–498, 2022
[16] Christina N Cooper et al., “Effects of Lower-Body Muscular Fatigue on Vertical Jump and Balance Performance.”, Journal of strength and conditioning research, vol. 34, Oct. 2020
[17] Ioannis Konstantopoulos et al., “Fatigue-Induced Inter-Limb Asymmetries in Strength of the Hip Stabilizers, Postural Control and Gait Following a Unilateral Countermovement Vertical Jump Protocol.”, Sports, vol. 9, Feb. 2021
[18] Jo Verschueren et al., “Does Acute Fatigue Negatively Affect Intrinsic Risk Factors of the Lower Extremity Injury Risk Profile? A Systematic and Critical Review.”, Sports medicine, vol. 50, Apr. 2020
[19] Wilczyński et al., “Dynamic Knee Valgus in Single-Leg Movement Tasks. Potentially Modifiable Factors and Exercise Training Options. A Literature Review.”, International journal of environmental research and public health, vol. 17, Nov. 2020
[20] Smith, H. (n.d.).: Target Heart Rate Calculator. Inch Calculator. Retrieved., May 2024, from https://www.inchcalculator.com/target-heart-rate-calculator/
[21] Dumitru Constantin-Teodosiu et al., “Molecular Mechanisms of Muscle Fatigue.”, International journal of molecular sciences, vol. 22, Oct. 2021
[22] Darin A. Padua et al., “The Landing Error Scoring System as a Screening Tool for an Anterior Cruciate Ligament Injury-Prevention Program in Elite-Youth Soccer Athletes.”, Journal of athletic training, vo. 22, Jun. 2015
[23] Félix Croteau et al., “Case study of IMU loads and self-reported fatigue monitoring of water polo goalkeepers preparing for the Olympic games.”, Front. Sports Act. Living, vol. 5, May 2023
[24] Pengfei Chang et al., “Identification of runner fatigue stages based on inertial sensors and deep learning.”, Frontiers in Bioengineering and Biotechnology, vol. 17, Nov. 2023
[25] Hannah L. Dimmick et al., “Between-Day Reliability of Commonly Used IMU Features
during a Fatiguing Run and the Effect of Speed.”, Sensors, vol. 22, May 2022
[26] Yanran Jiang et al., “A Data-Driven Approach to Predict Fatigue in Exercise Based on
Motion Data from Wearable Sensors or Force Plate.”, Sensors, vol. 21, Feb. 2021
[27] Marotta L et al., “Towards Machine Learning-Based Detection of Running-Induced Fatigue in Real-World Scenarios: Evaluation of IMU Sensor Configurations to Reduce Intrusiveness.”, Sensors, vol. 21, May 2021
[28] Dimmick HL et al., “ Between-Day Reliability of Commonly Used IMU Features during a Fatiguing Run and the Effect of Speed.”, Sensors, vol.22, May 2022
[29] Olivia L. Dyer et al., “Effects of static exercises on hip muscle fatigue and knee wobble assessed by surface electromyography and inertial measurement unit data.”, Scientific Reports, vol. 14, May 2024
[30] Salil Apte et al., “Biomechanical Response of the Lower Extremity to Running-Induced Acute Fatigue: A Systematic Review.”, Frontiers in physiology, vol. 12, Aug. 2021
[31] Jacob S. Arlotti et al., “Benefits of IMU-based Wearables in Sports Medicine: Narrative Review.”, International Journal of Kinesiology and Sports Science, vol. 10, Jan. 30, 2022
[32] Makoto Asaeda et al., “Factors associated with dynamic knee valgus angle during single-leg forward landing in patients after anterior cruciate ligament reconstruction. ”, Asia-Pacific Journal of Sports Medicine, Arthroscopy,Rehabilitation and Technology, vol. 22, pp. 56-61, Oct. 2020
[33] Sakorn Mekruksavanich et al., “Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network”, 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), pp. 130-134, Oct. 2022
[34] Sakorn Mekruksavanich et al., “A Residual Deep Learning Method for Accurate and Efficient Recognition of Gym Exercise Activities Using Electromyography and IMU Sensors.”, Applied System Innovation, Jul. 2024
[35] Sujee Lee et al., “Multimodal sensor fusion models for real-time exercise repetition counting with IMU sensors and respiration data.”, Information Fusion, vol.104, Apr. 2024
[36] Zhaolong Zheng et al., “Training Data Selection and Optimal Sensor Placement for
Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction.”, Entropy, May 2021
[37] Alessandra Angelucci et al., “An IMU-Based Wearable System for Respiratory Rate Estimation in Static and Dynamic Conditions.”, Cardiovascular Engineering and Technology, vol. 14, Jun. 2023
[38] Hélène De Cannière et al., “Wearable Monitoring and Interpretable Machine Learning Can Objectively Track Progression in Patients during Cardiac Rehabilitation.”, Sensors, Jun. 2020

指導教授

林澂(Chen Lin)

審核日期

2024-7-26

推文